Knowledge Dependence and the Formation of Director...

r Academy of Management Journal2017, Vol. 60, No. 5, 1986–2013.https://doi.org/10.5465/amj.2015.0499

KNOWLEDGE DEPENDENCE AND THE FORMATION OFDIRECTOR INTERLOCKS

MICHAEL D. HOWARDMICHAEL C. WITHERS

LASZLO TIHANYITexas A&M University

In this study, we examine knowledge dependence, a unique form of external dependencefirms face when they pursue new technologies. Our focus is on the formation of in-terorganizational ties as a means to manage the firm’s knowledge dependence. Studyingthe board interlock ties of 717 technology-based firms in 2002–2006, we find that tieformation is more likely when an external counterpart is more closely aligned with theglobal trajectory of the focal firm’s core technology and when the counterpart is moreactive in defending its intellectual property in this area. As a result of the interlock, thefirm is more likely to gain access to the counterpart firm’s knowledge resources throughresearch and development alliances and forestall litigation barriers in the use of coretechnologies. Our findings provide important theoretical implications for the unique roleof knowledge resources in interorganizational dependence and tie formation.

INTRODUCTION

Technological knowledge has been increasinglyrecognized as a critical resource for developing andsustaining competitive advantage (Cefis & Marsili,2005; DeCarolis & Deeds, 1999). In many cases, firmsbuild on knowledge from outside sources in orderto complement internal research and development(R&D) efforts (Cassiman & Veugelers, 2006), maintaincapabilities to integrate with suppliers (Brusoni,Prencipe, & Pavitt, 2001), or increase their learningand flexibility with respect to new technological de-velopments (Cohen & Levinthal, 1990; Liebeskind,Oliver, Zucker, & Brewer, 1996). Perhaps more thanany other form of resource, technological knowledgeis influenced by broader developments in the envi-ronment (Kotha, George, & Srikanth, 2013; Powell,Koput, &Smith-Doerr, 1996). Firmsmaybemotivatedto establish and actively manage key relationshipswith other firms in order to maintain access toknowledge resources andbenefit from trends relevantto their own technologies.

The early decisions of Freescale Semiconductormay provide an illustration of the nature of theserelationships. Freescale was established in 2004 as

a spin-off from Motorola, carrying forward estab-lished product lines in microprocessors, sensors,and other semiconductor products. Among its R&Defforts at the time of the spin-off, Freescale continuedto develop several significant technologies. Theirmicroelectro-mechanical sensors (MEMS) played animportant role in automotive airbag systems andhave led to new applications in motion-based homevideo games. Their magnetoresistive random accessmemory (MRAM) chips required no power to storedata and represented a breakthrough for use inaerospace and automotive applications. The MRAMproduct won the 2006 Electronic Products’ “Productof the Year Award.”

Freescale Semiconductor faced significant chal-lenges in operating as a newly independent entity.1

Among the early actions taken by the company,Freescale appointed severalnewmembers to its boardof directors. Kevin J. Kennedy, chief executive officer(CEO) of JDS Uniphase, and Stephen P. Kaufman, anestablished director of telecommunications equip-ment firm Harris Corporation, joined Freescale’sboard in 2004. Krish A. Prabhu, CEO of Tellabs, wasadded as a director in August of the following year.

The new board members brought significantexperience in managing and directing technology

We are grateful for the insightful guidance and con-structive input of our editor, Gerard George, and the threeanonymous reviewers. We also wish to thank MurrayBarrick and Warren Boeker for their assistance and feed-back in the development of this paper.

1 These challenges would ultimately lead to a failed lev-eraged buyout, dramatic losses in revenue andmarket share,andChairman andCEOMichelMayer’s resignation less thanfour years later (Thornton, Burrows, & Crockett, 2008).

1986

Copyright of the Academy of Management, all rights reserved. Contents may not be copied, emailed, posted to a listserv, or otherwise transmitted without the copyright holder’s expresswritten permission. Users may print, download, or email articles for individual use only.

https://doi.org/10.5465/amj.2015.0499

firms. For example, following the appointment ofMr. Prabhu, Michel Mayer, Freescale chairman andCEO, stated, “Krish is a widely recognized technol-ogist and a highly respected international businessexecutive.” However, while the firm no doubtbenefited from these directors’ expertise, the ap-pointment of these directors may have reflectedFreescale’s attempts tomanage its relationshipswiththe external environment as well. For example, eachdirector’s affiliated firm held claims to intellectualproperty that served as key building blocks forFreescale. The new MEMS technologies drew onfabrication methods developed and patented by JDSUniphase. The MRAM design leveraged Harris Cor-poration’s silicon wafer bonding techniques to ad-here the new magnetically permeable layer to thesurface of the semiconductor chip. Freescale’s de-velopments in digital signal processing built uponTellabs’ patented technologies in the control andsynchronization of telecommunications networksignals. Challenged to extend its line of technologiesas a newly independent firm, could Freescale havesought ties with these companies in order to manageits dependence on the external environment for ac-cess to key knowledge resources?

To answer this question, we examine the roleknowledge dependence plays in the formation ofinterorganizational ties. Specifically, we investigatewhether or not firms are able to eliminate barriersand ensure access to knowledge resources by usingtheir director interlocks. Prior researchdemonstratesthat forming ties with counterpart firms, often viadirector interlocks, provides an important mecha-nism for managing dependence on those firms(Beckman, Schoonhoven, Rottner, & Kim, 2014;Mizruchi, 1996; Pfeffer & Salancik, 1978). Could theties in the above example also be used to managea distinct form of external dependence increasinglysalient in the technology-driven economy, a de-pendence based on knowledge?

Given the importance of knowledge as an organi-zational resource and the necessity to build onknowledge components from external sources, wetheorize that knowledge dependencemay lead to theformation of ties betweenorganizations as ameans tomanage this dependence. In proposing a knowledgedependence perspective of interorganizational tieformation, we focus on two essential factors that arein play when firms attempt to manage their de-pendence on external sources of knowledge. First,we posit that a firm is more likely to form a directorinterlock tie with a counterpart whose technologydevelopment is more closely aligned with the global

trajectory of the focal firm’s core technologies. Sec-ond, we argue that the likelihood of interlock tieformation is higher when a counterpart firm is visi-bly active in pursuing patent litigation in thesetechnology areas. We not only investigate the ante-cedents of interlock tie formation but explore theoutcomes of tie formation, a neglected aspect ofboard interlocks in previous research. Specifically,we consider two outcomes pertinent for the man-agement of knowledge dependence. First, we seek tofind out whether or not the presence of interlock tiesbetween partners reduces the likelihood of patentlitigation against each other. Second, we inquire ifpartner firms are more or less likely to pursue R&Dalliances with each other when they are linked to-gether with board interlock ties. Using a sample oftechnology-based firms, we find evidence that firmsdo in fact manage their knowledge dependence byforming director interlocks with their counterparts.

We seek to make three theoretical contributionswith this study. We first intend to advance theliterature on external dependence by examiningthe critical resource that knowledge may represent(Casciaro & Piskorski, 2005; Pfeffer & Salancik, 1978;Wry, Cobb, & Aldrich, 2013). Our study offers newinsights into dependence-managing strategies byexamining how the firm’s dependence on the exter-nal technological developments in its field maylead to interlock formation. Following research onknowledge resources, we suggest that the theoreticalmechanismunderlying knowledge dependencemaybe qualitatively different from the dependence thatemerges from more traditional resources, includingfinancial and physical resources. Specifically, weposit that the path-dependent nature of knowledgeand the technological trajectories in which it residescreate a unique formof dependence for firms seekingto build on external knowledge. We extend thisperspective by elucidating the specific dependencemanagingmechanisms that board interlocks provideand the outcomes that derive from such ties. Inparticular, we find that forming board interlocksreduces the likelihood of litigation threats and in-creases the likelihood of forming an R&D alliancewith the coopted firm. In this regard, our study an-swers the call to provide evidence as to whetherdependence managing strategies have the desiredeffects (Pfeffer, 2003).

Second, accentuating the distinct nature of knowl-edge dependence enables us to consider a number ofpreviously unexplored factors that underlie resourcedependence relationships. For example, Pfeffer (2003:xxiv) suggests: “it would be useful for studies of

2017 1987Howard, Withers, and Tihanyi

strategy in a resourcedependence tradition to considerthe externally-oriented, sometimes non-market basedactions companies undertake to provide competitiveleverage.” He then offers litigation as just such a non-market based action. Our study lends support to thenotion that firms’ litigious actions, in the formofpatentassertions, may influence other firms’ external de-pendencies associated with knowledge. Furthermore,our focus on external knowledge provides us with anopportunity to examine whether the formation of in-terlock ties leads to specific advantages in gaining andmaintaining access to critical knowledge resources,evidence that efforts to address knowledge depen-dence may provide significant value.

Third, we contribute to the literature on board in-terlock formation. We propose that the underlyingknowledge connections between firms may repre-sent an important consideration in the decision toselect new directors. Specifically, we posit thattechnological knowledge dependence, outside ofthe motivations for and mechanisms of knowledgetransfer, may lead firms to attempt to form interlockties and thus coopt and gain the support of otherfirms (Diestre, Rajagopalan, & Dutta, 2015). Ourstudy design helps us to isolate this alternative effectby observing the role external organizations play inthe global trajectory of the focal firm’s technology,while accounting for the tendency to draw directlyfrom their knowledge.

MANAGING KNOWLEDGE DEPENDENCE BYDIRECTOR INTERLOCKS

As a firm interacts with its external environment,certain resources may be more critical to its strategyand ability to reduce uncertainty (Pfeffer & Salancik,1978). The resource dependence perspective recog-nizes a number of resources, including “monetary orphysical resources, information, and social legiti-macy” that are critical to a firm’s ability to reduce itsenvironmental uncertainty (Wry et al., 2013: 447). Ofthese, financial resources have been viewed as animportant set of factors that drives organizationaldependence. For example, Mizruchi and Stearns(1988) find that firms are more likely to form in-terlocks with financial institutions when faced withfinancial dependence and these interlocks lead to thefirms’ increased ability to obtain financial resources.However, the influence of financial institutions andthe need to interlock with them precipitously de-clines when firms rely less on traditional sources offinancial funding and increase the numbers of theirown specialized financial experts (e.g., chief financial

officers [CFOs]) (Mizruchi, Stearns, &Marquis, 2006).Despite this general trend, research recognizesthat interlocks can be a mechanism to manage de-pendency on a particular resource.

While previous studies on resource dependencehave considered a variety of external resources,limited attention has been devoted to the depen-dence that may emerge from external knowledge.This void is surprising given the pace at whichknowledge resources have gained importance in re-cent years (Zhou & Li, 2012). Knowledge is recog-nized as critical to a firm’s ability to carry out itschosen strategy and to obtain superior performance(Grant, 1996). From this perspective, knowledge,and, in particular, tacit knowledge, serves as a keysource of competitive advantage.

To better understand the potential role of knowl-edge in creating a unique form of resource de-pendence, we conducted a series of interviews andonline surveys with technology firm executivesacross a variety of industries. Their responses suggestthat knowledge dependence is a recognized issue,often discussed and addressed at the board level. Forexample, a senior executive from a large U.S. defensecontractor noted, “I think you see that dependency isoccurring across a lot of companies just because theinvestor risks are just too great, too high.” Furthercomments on knowledge dependence by the execu-tives are summarized in Table 1.

Beyond their growing importance, knowledge re-sources are conceptually different from other organi-zational resources thathavebeen the focusof resourcedependence theory. The differences between knowl-edge and other resources, such as financial resources,maysuggest theneed fornewtheoretical explanationsfor the dependence relationships and firms’ relatedactions. In particular, new technological knowledgetends to be attained following a path dependency orwithin a technological trajectory (Jung & Lee, in press).The path-dependent nature of knowledge resourcessuggests that self-reinforcing mechanisms may driveknowledge accumulation (Sydow, Schreyogg, &Koch,2009). In this regard, “prior knowledge permits theassimilation and exploitation of new knowledge”(Cohen & Levinthal, 1990: 135–136). Similarly,knowledge resources follow particular technologicaltrajectories (Dosi, 1982, 1988; Nelson &Winter, 1977,1982). These technological trajectories reflect the factthat technological knowledge and innovation “seemto follow advances in a way that appears somewhat‘inevitable’ and certainly not fine tuned to thechanging demand and cost conditions” (Nelson &Winter, 1977: 56–57).

1988 OctoberAcademy of Management Journal

As Eisenhardt and Santos (2002: 145) suggest,“given the dispersion of knowledge (both withinand outside the firm) and the uncertainty in theenvironment, knowledge sourcing is an importantknowledge process by which managers identify andgain access to relevant knowledge that is being cre-ated in the environment.” This need to obtain accessto external knowledge resources may create knowl-edge dependencies between firms. For example,Cohen and Levinthal (1990: 136) suggest that by de-veloping related prior knowledgewithin a particularknowledge area, “a firm may more readily accumu-late what additional knowledge it needs in the sub-sequent periods in order to exploit any criticalexternal knowledge that may become available.”However, along with this heightened ability to ac-cumulate knowledge, the path-dependent nature ofknowledge and its technological trajectory may alsoincrease the focal firm’s dependence on knowledge

providers and the general knowledge domain. Forexample, firms operating in markets characterizedby network externalities are highly dependent onbroader technology trends. As noted by Schilling(2002: 388), “firms sponsoring technologies that areincompatible with the dominant design may findtheir technologies locked out of the market.”

Director Interlocks as a Dependence ManagingMechanism

Previous research recognizes that director in-terlocks serve as information conduits between orga-nizations (Haunschild & Beckman, 1998; Shropshire,2010). Although directors are not expected to havea detailed understanding of their firms’ technologies,many of them likely recognize the implications oftechnological knowledge on the strategies and per-formance of their firms. As an interviewed executive

TABLE 1Interview Comments Regarding Knowledge Dependence, Director Selection, and Technology Trajectory

Firm Description External Knowledge Dependence Director Selection Technology Trajectory

Large defense contractor “I think a lot of companies arebecoming highly dependent onpartners. . .”

“. . .with the significant risk ofinvestment in a technology thatdoesn’t prove out or doesn’tbecome marketable you havecorporations looking to drawthat kind of expertise on theirboard.”

“. . .an increased interest inbuilding new nuclear energyreactors—inmany cases a lot ofcontrol rooms were based onanalog technology. This[external] business haddesigned digital updates.”

“I think you see that dependencyoccurring across a lot ofcompanies just because theinvestor risks are just too great,too high.”

Large telecommunications firm “. . .there’s interdependenciesthere when it comes totechnology and whether that bein general running the businessor on the learning side of thebusiness.”

“. . .in fact our last couple of boardmembers have been from thetechnologies business; havebeen. . . involved in start-ups, orthey come from another highlytechnical company.”

“When we restructured and putour mobility side of the houseandmerged it with our businesssolution side of the house andwewent to the board. . . thatwasimportant because allcompanies are becoming muchmore highly mobile much lesswired. . .”

Medium-sized software firm “Yes [dependencies occur]. . .customer concentration risk ishow this would bemost directlyexperienced.”

“Should the use of a patentedtechnology in the future gobeyond a tactical use and bemore a component ofdetermining strategic directionfor our company [then thatrelationship] could yielddiscussion about a Boardposition.”

“Data security andprivacy is akeyconcern for us, so we haveextensive sessions on risk there.The business is a softwarebusiness, so we also discuss theoverall technology in everymeeting.”

Small VC-backedtechnology firm

“Yes, that potential [knowledgedependence] does exist.”

“Typically board members wouldprovide 1–3 board adviserswhoseprimary rolewas to be anexpert in some aspect oftechnological deployment.”

“The VC (Venture Capital)community was interested intechnology that providesa competitive advantage. . . onhow to cost-effectively deploytechnology insaleableproducts.”

“We manage the risk of over-dependence on that third partythrough regular projectmanagement and technicalmeetings. . .”


noted in describing board meeting technology dis-cussions, “It’s more at the strategic level. They’ll talkto them about the technology, but they’ve got to put itat a level that people fromallwalks of life. . ., that theycan understand.” Another executive observed, “Iwould say themajority of discussions are. . . logical orintuitive enough that the board understands what theproduct potential is without having to get into a lot ofdetail.”

Outside directors can be particularly valuableparticipants in such board discussions, owing totheir access to information on strategic opportuni-ties, new organizational practices, resources, andpotential strategic pitfalls at other firms (Mizruchi,1996). From this perspective, director interlocksprovide a means for knowledge transfer betweenfirms (Shropshire, 2010). For example, Westphal,Seidel, and Stewart (2001: 717) find that directorinterlocks facilitate “the imitation of an underlyingdecision process or script that can be adapted tomultiple policy domains (e.g., business strategy,compensation policy, acquisition activity, etc.).”Information transmission via interlocking director-ates has been shown to influence the establishmentof investor relations departments (Rao & Sivakumar,1999), engagement in acquisitionactivities (Haunschild,1993; Haunschild & Beckman, 1998), implementationof poison pills (Davis, 1991), and international ex-pansion (Connelly, Johnson, Tihanyi, & Ellstrand,2011). Although research offers key insights into therole interlocks may play in acquiring general in-formation and knowledge, as Pfeffer (2003) pointsout, studies considering the role of information andinterlocks have yet to consider the role that de-pendence on external providers of knowledge mayplay in interlock formation.

In addition to its role to provide conduits of in-formation, the board of directors is proposed toserve as a dependence-managing mechanism eitherthrough cooptation or by providing a number ofcritical resources to the firm (Hillman, Withers, &Collins, 2009). Kotter (1979: 89) posits that one wayfor organizations to manage external dependence is“by establishing favorable relationships with thosethey are dependent upon and with alternative sour-ces of support in their domain.” Inviting membersfrom more powerful entities to sit on the dependentfirm’s board can be an attempt to coopt the in-dividuals (and the firms they represent) to gain theirsupport for the dependent organization (Mizruchi,1996).

An interlock is formed when an individual ini-tially affiliatedwith one firm (in the role of executive

or director) is also added to another firm’s board(Mizruchi, 1996). In reflecting the cooptation un-derlying an interlock, Pfeffer and Salancik (1978:163) explain that “when an organization appoints anindividual to a board, it expects the individual willcome to support the organization, will concernhimself with its problems,will favorably present it toothers, and will try to aid it.” This support from thecoopted director will help tomanage the uncertaintyaround the resource exchanges that occur within theexternal environment. In this regard, the firm man-ages its dependence “by trading sovereignty forsupport” to ensure the focal firm has access to thecritical resources, information flows, and interfirmcommitments (Davis & Cobb, 2010: 25). Forminga board interlock and acquiescing to the focal firm’scooptation efforts also offer somebenefits to themorepowerful firm as it gains influence over the focal firmand maintains control over the critical resourcecreating thedependence (Casciaro&Piskorski, 2005;Pfeffer & Salancik, 1978).

Empirical research in this area supports the di-rector’s role in linking the focal firm to its environ-ment (Beckman et al., 2014) and providing a numberof important resources to the firm (Drees & Heugens,2013). Research on boards of directors also supportsthe notion that board composition is modified overtime to adapt to environmental changes a focal firmmay face (Beckman, Haunschild, & Phillips, 2004;Hillman, Cannella, & Paetzold, 2000). Reflecting therole boards may perform in managing dependence,Pfeffer (1987: 42) contends that “the structure of theboard, and in particular, the use of interlocks tomanage resource dependence has probably been themost empirically examined form of intercorporaterelation.” Given the dependence-managing role thatinterlocks can play, we develop hypotheses on therelationship between knowledge dependence andboard interlocks.

HYPOTHESES

Dependence Through Technological Trajectories

Resource dependence theorists consider the ac-tions and behavior of organizations as responses tocomplex environmental demands that involve thediverse motivations and concerns of other organi-zations (Wry et al., 2013). From this perspective,thus, “organizational activities and outcomes areaccounted for by the context in which the organiza-tion is embedded” (Pfeffer & Salancik, 1978: 39). Weextend this logic to proffer that external knowledge


places constraints on firms and, as such, firms at-tempt to directly manage dependencies that emergefrom the reliance on external knowledge.

The development of knowledge tends to be pathdependent (Helfat, 1994; Nelson & Winter, 1982)owing to the efforts of organizations to create in-novations from prior internal and external knowl-edge (Galunic &Rodan, 1998;Kogut &Zander, 1992).The choice of recombination of knowledge elementscan determine whether a technology spawns futuresuccessful innovations or leads to a “dead end”(Fleming, 2001; Podolny&Stuart, 1995). Continuouschange and efforts to seek new paths for innova-tion lead to the emergence of technological trajecto-ries (Dosi, 1982). Technological trajectories providea reference frame for defining a technology, its ca-pabilities and limitations, and how it should beevaluated (Alexy, George, & Salter, 2013). The ac-tions of industry participants shape this trajectory,with technological outcomes determined throughthe knowledge development activities and strategiesof many organizations (Chatterji & Fabrizio, 2014).

Given the complexities of knowledge develop-ment by multiple industry participants (Pfeffer &Salancik, 1978; Rosenberg, 1979), firms cannot fullycontrol the direction and ultimate value of their in-novation efforts; rather, they depend greatly on thebroader trajectories and dominant designs in thetechnologies they choose to pursue (Anderson &Tushman, 1990; Gatignon, Tushman, Smith, &Anderson, 2002; Roy & Sarkar, 2016). Though tech-nologies often coalesce around a single trajectory,development occurs through a recombinant process(Fleming & Sorenson, 2001), and trajectories shiftand advance as new combinations of knowledge el-ements are identified and pursued (Carnabuci &Bruggeman, 2009). Organizations seek to developthe value of the core knowledge elements in whichthey possess expertise (Fleming & Sorenson, 2004;Henderson & Clark, 1990). At the same time, theglobal trajectory reflects how their core knowledge isbeing used at a field level, representing the state ofthe art or the collective wisdom across the industrywith regard to the best approach for combining otherknowledge elements with this core technology.

External firms will vary in how closely they alignwith or match this global trajectory (i.e., formingcombinations of knowledge elements with the focalfirm’s technology in a fashion that closely trackswith the field-level direction of development). Firmsclosely aligned with the global trajectory mayachieve this status by having greater influence inpushing the direction of technology development

(Christensen & Rosenbloom, 1995), they may befortunate to have internal resources and capabilitiesthat are well suited to the emergent direction(Anderson & Tushman, 1990; Eisenhardt & Santos,2002), or the nature of changes in the trajectory maybe well suited to their structure and strategy (Wolter& Veloso, 2008). Whether through their influence orfortune in possessing favorable resources and capa-bilities, firms alignedwith the global trajectory are ina position to impact the future direction of the focaltechnology, while enhancing their own chances forsurvival and success (Suarez & Utterback, 1995).

Firms closely aligned with the global trajectoryalso may significantly impact a focal firm’s in-novation efforts by creating or removing barriers toaccess in key knowledge areas. Firms may pursuestrategies to shape the external direction of techno-logical development such as selectively revealingtheir own knowledge (Alexy et al., 2013), aggres-sively defending their claims to intellectual property(Ziedonis, 2004), or rallying other innovators andfirms to align with their preferred technological tra-jectory (Spencer, 2003). Collective action amonggroups of firms can enhance their competitive suc-cess in defining the trajectory and resulting domi-nant design in an area of technology, and firmscentrally located in the development of a technologyarea are shown to have a greater influence on thetrajectory (Soh, 2010).

A firm may have opportunities for reducing un-certainty and constraints stemming from externalknowledge development by coopting organizationsclosely aligned with the global trajectory of its coretechnology. The use of board interlocks is a particu-larly useful mechanism for coopting external firmsin order to manage outside dependencies (Pfeffer,1972). When an external firm is closely aligned withthe dominant trajectory of the focal firm’s coretechnology, the focal firmmay seek out executives ofthe other firm to serve as directors in the hopes ofmanaging its overall dependence on that technolog-ical trajectory (Burt, Christman, & Kilburn, 1980;Drees & Heugens, 2013). An executive from a largedefense contractor, for example, described to us howthey had established “. . .an increased interest inbuilding new nuclear energy reactors – in manycases a lot of control rooms were based on analogtechnology. This [external] business had designeddigital updates.” Forming interlock ties with suchexternal partners may help ensure the firm’s accessto information and ability to influence the directionof technology trajectories in a way that may allowthem to achieve commercial success. The defense


firm executive described this process as follows:“. . .with the significant risk of investment in a tech-nology that doesn’t prove out or doesn’t becomemarketable, you have corporations looking to drawthat kind of expertise on their board.”

Interlock ties to firms closely aligned with theglobal trajectory of a technologymay reduce barriersand increase access to knowledge resources associ-ated with that technology. Forming a director in-terlock between two firmsmay reduce the likelihoodof competitive action or manipulation of access totechnological knowledge from a partner firm, whichin turn reduces the uncertainty around obtainingknowledge from the external environment. Thus, therole of the counterpartmay shift fromexternal sourceto ally through the involvement of its directors inboard discussions about technology strategy. At thesame time, insights shared by representatives fromthe counterpart firm may clarify the commercialopportunities provided through the dominant tra-jectory of its core technologies. Along this line,Diestre and colleagues (2015) find that when phar-maceutical firms consider entering a new market,they more likely appoint an interlocking directorwith experience in the new market. In general,though, we suggest that the external constraints de-rived from using external knowledge resources fromaglobal trajectorywillmotivate firms to establish tieswith outside firms more closely aligned with the fo-cal firms’ core technology in order to manage theirdependence on outside knowledge resources.

Hypothesis 1. A focal firm is more likely to forma director interlock with an outside firm whoseinnovations in the focal firm’s core technologyarea are more closely aligned with the globaltrajectory of that technology.

Threat of Litigation over Intellectual Property

Beyond the characteristics of technology sharedbetween firms, the strategies and actions of firmscontributing to core technological trajectories maydirectly influence knowledge dependence. Knowl-edge development in a field relies on somewhat con-tradictory forces of coordination and appropriation.Coordination (explicit or implicit) allows the broaderresources of a field to advance the state of the art ina given areaof technology (Liebeskindet al., 1996).Asa result, the pace of innovation can exceed whatwould be possible through the efforts of a single or-ganization (Powell et al., 1996). At the same time,firms participating in the advancement of technology

must have a reasonable expectation that they will beable to gain from their investment.

Firms that aggressively assert claims related totheir knowledge portfolio may increase the knowl-edge dependence of other organizations (Dunford,1987). These firms possess the technological exper-tise and market presence to undermine the efforts ofthe focal firm seeking new external knowledge. Inturn, the focal firm’s knowledge dependence is ex-acerbatedby thedegree towhich external knowledgeelements remain under the control of the resourceprovider.

Patent assertion is particularly pertinent in thistechnological knowledge context because of the gen-eral legal function of patent citations (Collins &Wyatt,1988; Lerner, 1995). The threat of patent assertion isrecognized as an important driver of firm behavior(Lerner, 1995). Interestingly, litigious actions are alsorecognized as potential mechanisms for firms tomaintain dependence (Pfeffer, 2003). In this regard,litigation threat may be reflective of both the impor-tance of a given technology, in general, and a firm’sability tocontrol that resourceand itsuse, inparticular.

Firms that aggressively assert their claims on thebenefits of knowledge advancement in their areas oftechnology may signal potential challenges to otherfirms in the field. In turn, other firms face greaterrisks in obtaining the benefits of their own technol-ogy development efforts. They are subject to hold-up risks or the threat that another firm with legalclaims to a given technology will interfere with itsdevelopment (Arora, Fosfuri, & Gambardella, 2004;Scotchmer, 1991). Furthermore, firms holding ad-vantageous positions with respect to legal claims onknowledgemay leverage their positions, preemptingthe efforts of the focal firm to develop and commer-cialize related technologies (Dunford, 1987).

Firms are likely to adopt strategies to address theirknowledge dependence when other organizationsaggressively engage in assertions of intellectual prop-erty in the focal firm’s technological domain. Theymay exercise caution in pursuing technologies infieldswhere firmshave beenmore active in defendinglegal claims associated with their technologies, espe-cially when they face higher litigation costs (Lerner,1995).Other researchhas shownthat firmswill engagein strategic behavior to establish protected pockets oftechnology through patents and related legal claims(Ziedonis, 2004). In the faceof aggressive protection ofintellectual property, firms may undertake efforts tocoopt external knowledge providers by forming directtieswith firmspossessing andenforcinggreater claimsto underlying technologies. Board interlock ties may


provide a conduit for information exchange betweenfirms operating in similar technological domains. Thelitigiousness of a counterpart firm may seem to pre-clude the focal firm from forming such ties; however,our fieldwork indicates this is not necessarily the case.As one executive observed, “I don’t think from a riskperspective we would have necessarily backed awayfrom a company that sued to protect their intellectualproperty.” Interlocks may reduce uncertainty re-garding the claims of the counterpart firmwith respectto proprietary technologies, a factor shown to influ-ence the likelihood of patent litigation (Lanjouw &Schankerman, 2001). It may also allow firms to shareinformation on strategies and intended uses of relatedtechnological knowledge, preempting conflicts ormisunderstandings between the organizations.

Hypothesis 2. A focal firm is more likely to forma director interlock with another firm that moreaggressively defends legal claims in the focalfirm’s core technology.

The Implications of Interlocks

We suggest that the formation of a director in-terlock can be an important mechanism for firms tomanage their dependence on external knowledgesources. The research on resource dependence anddirector interlocks generally finds that these in-terorganizational relationships are used to manageexternal dependence (e.g., Boyd, 1990; Hillmanet al., 2000; Pfeffer, 1987). Interestingly, we havevery little evidence on the specific mechanisms thatinterlocks offer to manage the dependence. Reflect-ing this point, Pfeffer (2003: xix) states, “Virtually allof the research treating organizational responses tointerdependence has a strange omission—any con-sideration of whether these various cooptive strate-gies are successful, or at an even more refined level,the conditions under which the various strategieswork and when they don’t.”

Our context, however, allows us to examinewhether interlock ties lead to specific outcomes thatmay reflect the management of knowledge de-pendence. In particular, we consider two importantoutcomes: the likelihood of patent litigation and theformation of R&D alliances between the two firms.The first outcome is directly tied to the managementof dependence through cooptation (Dunford, 1987);whereas, the second outcome reflects the potentialfor, at least partially, absorbing the dependence fol-lowing the formation of a board interlock betweenthe two firms (Casciaro & Piskorski, 2005).

As we have suggested, when a focal firm operatesin a particular knowledge domain, it may seek in-terlock ties with firms that aggressively defend theirclaims within the domain. We extend this perspec-tive to suggest that interlock formation between thetwo firms, in turn, reduces the likelihood of litigiousbehavior between them. Prior research recognizesthat board composition can influence a firm’s liti-gation risk, in general (Kassinis & Vafeas, 2002). Inthe context of knowledge dependence, the formationof board interlocks between two firms may directlyimpact the likelihood that they will pursue patentlitigation against one other. Similar to other in-terorganizational relationships, such as R&D con-sortia (e.g., Joshi & Nerkar, 2011), a board interlockbetween two firms may lead the partnering firms topursue other means to manage any disagreementsover patented technology.

For the more powerful firm, the litigious behaviorcan be an important mechanism to maintain otherfirms’ dependence on the knowledge area (Dunford,1987). However, for the focal firm, forming boardinterlocks can be an important mechanism to ensurethat the firm has support from firmswith power overit (Pfeffer, 1972). Through these cooptation efforts,“the aimof bringing inpotentiallyhostile outsiders isto socialize them and to commit them to provideassistance to the focal organization” (Pfeffer &Salancik, 1978: 110). Such relationships might en-sure that an external provider does not restrict accessto the knowledge resource or hold up a focal firm’stechnological development by engaging in patentlitigation. In this regard, the formation of a boardinterlock can reduce the likelihood of litigation.

Hypothesis 3.Thepresenceofadirector interlockbetween firms reduces the likelihood that theywill pursue patent litigation against each other.

While the formation of board interlocks may serveas a coopting mechanism to manage external knowl-edge dependence, other mechanisms can absorb thedependence more directly (Casciaro & Piskorski,2005; Drees & Heugens, 2013; Hillman et al., 2009).In contrast to the indirect approach of cooptation,constraint absorption represents the management ofdependence by obtaining more direct control overexternal resources (Casciaro&Piskorski, 2005; Pfeffer& Nowak, 1976). In the context of knowledge de-pendence, R&D alliances provide an important mech-anism for managing uncertainty and acquiring controlover external technological resources (Steensma,Marino, Weaver, & Dickson, 2000). Firms dependentonexternal knowledgemayseekout suchpartnerships


with firms closely tied to the technological trajectory(Lavie & Rosenkopf, 2006).

While cooptation and constraint absorption repre-sent different mechanisms to managing dependence,they are not mutually exclusive (Beckman et al., 2004;Gulati & Westphal, 1999). Rather, organizations mayemploy multiple strategies in an attempt to managetheir dependence on the external environment (Kotter,1979). In particular, current board interlock partnersmay be more likely to form R&D alliances withone another to reduce uncertainty surrounding theknowledge domain. As Beckman and colleagues(2004) theorize, thesemultiplex relationships inwhicha firm forms an alliance with an existing interlockingpartnermay emerge from theneed to address industry-level uncertainty. In a similar fashion, uncertaintysurrounding the global or industry-level trajectory offocal firm technologiesmay lead to suchmultiplex ties.

The uncertainty derived from technological tra-jectories and broader knowledge dependence alsomay lead firms to seek out greater stability in theirinterorganizational relationships. Existing inter-locking partners may represent potential alliancepartners that bring less of a threat for adverse selec-tion andmoral hazard (Beckmanet al., 2004;Gulati &Westphal, 1999). AsGulati andWestphal (1999: 475)generally posit, “board interlocks may also channelinformation between firms and thus serve as a cata-lyst for the creation of new alliances between firms.”However, Gulati and Westphal also find that “thatthe mere presence of a board interlock tie betweentwo firms does not appear to increase (or decrease)the likelihood that they will enter into a strategicalliance with one another” (1999: 497). Rather, thelikelihood of alliance formation depends on thecontent of the board interlock.

Within the context of knowledge dependence, in-terlock partners sought for their value in managingthe uncertainty around the technological trajectoriesmay be particularly salient partners for R&D allianceformation. Given the interlock is formed to manageexternal dependence and share information re-garding the knowledge domain (Pfeffer & Salancik,1978), the content of the interlock ismore focused oninterfirm coordination and cooperation, which mayincrease the trust between the potential alliancepartners (Gulati, 1995a). In turn, interlocks formed tomanage knowledge dependence may lead to theformation of R&D alliances.

Hypothesis4.Thepresenceofadirector interlockbetween firms increases the likelihood that theywill pursue R&D alliances with each other.

METHODS

Sample and Data Sources

We examine interlock tie formation as a socialnetwork-based phenomenon, comprising a frame-work of interorganizational relationships amongfirms in fields associated with technology develop-ment. Sample selection, along with the definition ofnetwork boundaries, is critical in social networkanalysis. Network nodes must be included or ex-cluded based on a careful consideration of the un-derlying social processes and proposed theoreticalrelationships that influence network tie formation(Wasserman&Faust, 1994). In our study,we focus onfirms engaged in technology development that arewell established and sufficiently large to pursueboard interlocks as a method of addressing knowl-edge dependence. We selected a series of boundaryconditions consistent with these characteristics.First, we include only publicly-traded U.S. firmswith two or more directors. Publicly-traded firmsare subjected to greater scrutiny and regulatoryrequirements in terms of the selection and activitiesof directors serving on their boards (Holder-Webb,Cohen, Nath, & Wood, 2008; Johannisson & Huse,2000). Information on board characteristics and in-terlock ties was obtained from MSCI GMI Ratings(Governance Metrics International, now owned byMorgan Stanley Capital International, formerly theCorporate Library). Next, we included firms com-peting in industries that tend to generate a visiblerecord of innovation through patents filed with theU.S. Patent and Trademark Office (USPTO). Theseindustries include medical devices, biotechnology,semiconductors, and networks and communica-tions (Cohen, Nelson, & Walsh, 2000). Our sampleincludes this broader set of industries in order tocapture dynamics of knowledge dependence thatmay emerge across industry boundaries. For exam-ple, medical devices may draw from innovations indrug technologies for the internal application ofmedicines. Similarly, there is a growing use ofsemiconductor technology for internal diagnosticdevices and the use of smart implants in medicaltreatment. We do, however, address the greater ten-dency for tie formation between firms of commonindustries through the use of industry match controlvariables, as described in more detail below.

To capture the full dynamics of interlock tie for-mation, we included firms regardless of whethertheywere observed to engage in patenting during thestudy period. For those firms with a record of pat-enting, data were collected through the publicly


available files of the Harvard Patent NetworkDataverse (Lai, D’Amour, & Fleming, 2009). Finally,we selected the specific timeframe for our observa-tion of interlock network tie formation, establishingthe five-year period of 2002–2006 as the window forour study. This time period was chosen in order toensure that patent applications of sample firms werelikely to have completed the review process; priorresearch shows that over 99% of patent applicationsfiledwith theUSPTO are resolvedwithin seven years(Trajtenberg, 1990). Furthermore, the Sarbanes-Oxleylegislation passed in 2002 should have a relativelyuniform effect during our study period (Green, 2005;Linck, Netter, & Yang, 2009). Applying all of thesecriteria, our network consists of 717 nodes, corre-sponding to the firms in our sample.

In order to test the effects of interlock tie formationon the number of alliances formed between partnerfirms predicted in Hypothesis 4, we built an addi-tional data structure of dyad-year observations foreach pair combination of the companies in our sam-ple, observed over the period 2002–2006. The panelstructure allowedus to track the effects of interlock tieformation in enhancing the likelihood that firms willpursue more R&D alliances with interlock partners.With missing data, the total sample size for this datastructure is 1.2 million dyad-year observations.

Variables. We operationalize our first dependentvariable, Interlock Tie Formation, as the selection ofa new director to the board of the focal firm whoholds a previously established position as a directoron the board of the alter firm. Specifically, we ob-serve new ties that are formed during the study pe-riod of 2002–2006, controlling for interlock ties thatexisted in the prior year. Furthermore, we considerthat dependence-coopting ties should be concurrent,with the new director added to the focal firm’s boardmaintaining his or her membership on the board ofthe alter firm. In testing the subsequent effects ofinterlock ties, we examine the dependent variables,Patent Litigation Adversarial Tie Formation andR&D Alliances. The litigation outcome measure isa network-based variable constructed in a fashionsimilar to the interlock tie formation variable. Foreach observation year, we construct a sociomatrix inwhich the firm listed in the jth column is the potentialplaintiff and the firm listed in the ith row is the po-tential defendant. The xij location of the matrix iscoded as 1 for years in which a U.S. Federal Courtpatent infringement case was filed corresponding tothis plaintiff-defendant pair, and it is coded as0 otherwise. Data regarding patent infringementcases are drawn from Lex Machina, a private

organization tracking intellectual property litigation intheUnitedStates. Patent litigation is not uncommon inour sample—the average number of sample firm pairsengaged in patent litigation is 48 per year, with a peakof 72 adversarial litigation network ties in 2006. ThemeasureofR&DalliancesassociatedwithHypothesis5isconstructedas thecountofR&Dalliancesannouncedbetween sample firms in the year of observation. Wedraw from SDC Platinum, a third party databasetracking alliance activity between publicly-tradedfirms, in order to collect these data.

To test Hypothesis 1, we calculate the Alter FirmAlignment to Focal Firm Core Technology Trajec-tory, reflecting the extent to which the external firmmatches the global trajectory in its use of the focalfirm’s core technology. For each focal firm in eachyear of observation, we determine its core technol-ogy area. This is captured as the primary U.S. patentclassification (USPC) that appears most frequentlyon patent applications submitted by the firm in theobservation year.We then calculate a vectormeasureof the global technology trajectory of this patent clas-sification by observing how it has been recombinedwith other patent classifications across the entireU.S.patent record.Recognizing therecombinantnatureof development in a given technology, this measuretracks all patents listing the USPC of the focal firm’score technology and identifies all of the other USPCcodes that have been co-listed with this technology.The directional components of the vector measure arecomprised of the co-listed patent classification codes,and the magnitudes are provided through the count ofpatents combining each of them with the focal tech-nology. This vector measure is lagged by one year(e.g., we observe the global trajectory measure in 2003for firmA’s core technology in 2004). We note that theglobal trajectory of a firm’s core technology is an in-herently dynamic measure in two respects: first, thedominant technology categoryof the firmmay changeover time (thoughnot likelyquicklyoroften, given theinvestment in technology expertise); and second, thecategories and frequency of knowledge elementscombined with the technology at the field-level willvary from one period to the next as the industry pur-sues new opportunities for recombinant innovation.

Next, we construct the alter firm-specific trajec-tory of the focal firm’s core technology for everyotherfirm in the sample. With the goal of evaluating howeach alter firm is aligned with the dominant globaltrajectory of the focal firm’s core technology, weobserve how closely each outside firm’s use ofknowledge element combinations matches with thebroader field-level trend. To do so, we observe how


each alter firm combines other knowledge elementswith the focal firm’s core technology. In the extremecases, alter firms may have no observed use of thefocal technology (i.e., vector of no direction, withmagnitude zero), or exactly match the direction ofthe global trajectory (identical vector to the globaltrajectory in terms of direction or proportions of co-ordinate components, though smaller magnitude).We then calculate the Euclidean distance betweeneach alter firm’s trajectory in that technology to theglobal (U.S. patent record) trajectory. In our frame-work, focal firmswill seek to resolve their knowledgedependence by forming ties to alter firms that aremore closely aligned to the global trajectory (lowerEuclidean distance) of their core technology.2 Weperform a linear transformation of the Euclideandistancemeasure, which has a range from zero to thesquare root of two into a relatedness measure, whichhas a range from 1 to zero. This allows for a moreintuitive interpretation of the results.

Our test of Hypothesis 2 once again draws on thepatent and legal record of the alter firm, measuringthe Alter Firm Patent Assertions in Focal Firm CoreTechnology Area. This reflects the number of courtcases filed by the alter firm that reference a patentwith its primary USPC code matching the coretechnology area of the focal firm.3 We employ a one-year lag, with litigation filed in year t-1 observed toimpact interlock formation in year t. Finally, Hy-potheses 3 and 4 examine downstream effects of in-terlock tie formation, with the outcome variablesdescribed previously.

We control for a number of factors likely to impacttechnology strategy, firm innovation and perfor-mance, and director interlock tie formation. Weconstruct a measure of Technological Uncertainty.For each patent in the focal firm’s knowledge port-folio, we capture its primary technology category,designated by theUSPC code.Wedetermine the dateof the first patent in the U.S. patent record to list thisUSPC code, calculating the number of years betweenthis date and the application date of the focal patent.We then aggregate this at the firm level by taking themean value across all patents with applicationssubmitted by the focal firm in the observation year.

To facilitate the interpretation of this variable, wetransform this measure from an average of elapsedtime of USPC code use (which could range from 0 tosome positive number of years) to a within-sampleuncertainty measure, which ranges from 1 (most re-cent, hencemore uncertain technologies) to 0 (older,more understood technologies). Consistent with re-cent research (Oriani & Sobrero, 2008), we suggestthat patents drawing on older, more establishedtechnologies will have lower uncertainty, whilepatents based on relatively new technologies will beless predictable in their impact and benefit to thefirm. We observe Citation of Alter Firm Technology,andwhether the focal firm draws directly on an alterfirm’s inventions by citing a previously establishedpatent of the alter firm in its own patented technol-ogies (Jaffe & Trajtenberg, 2002). This variable islagged, reflecting the cross-firm patent citations oneyear prior to the observation year.We also control forthe Number of Patents for both the focal and alterfirm, as well as the Alter Firm Patent Impact. This ismeasured as the count of external forward citations(i.e., omitting self-cites) of alter firm patents overa prior five-year period. It is plausible that counter-part firms possessing knowledge of greater impactwill attract more interorganizational ties.

We draw from Compustat data to construct con-trols associated with firm size, financial perfor-mance, and industry segment. We control for therelative values of these factors between the focal andalter firm, as well as the absolute value for the focalfirm.4 Difference in Employees measures the size ofthe alter firm relative to the focal firmwith respect tototal number of personnel employed. Difference inR&D Expenditures reflects the relative R&D in-vestment between the firms. Difference in Tobin’s Qcaptures the relative financial performance betweenthe firms based on both book value and market cap-italization. We examine the four-digit SIC (StandardIndustrial Classification) code of the focal and alterfirm in the dyad; Industry Match Controls test homo-phily between sample firms, capturing whether theyoperate in the same primary SIC code.We also controlfor management and board characteristics that have

2 Specifically, we observe the alignment between thealter firm’s innovations in this technology area and thebroader trajectory of such innovations across the field.Thus, thismeasure is independent of the specific trajectorypursued by the focal firm.

3 Note that the litigationmay focus onanydefendant, notnecessarily targeting the focal firm.

4 The relative size and performance levels between po-tential partner firms as well as the absolute levels experi-enced by the focal firm may have unique effects on thetendency to form interlock ties. For example, a firm withbetter financial performance (higher Tobin’s Q) may gen-erally seekoutmore external ties, thoughat the same time itmay seek partners with a similar level of performance(lower difference in Tobin’s Q).


been shown in prior work to contribute to board in-terlock tie formation (Withers, Hillman, & Cannella,2012). Focal Firm CEO Duality is a binary variable,taking a value of 1 for firms in which the CEO alsochairs the board of directors. Focal Firm Board Size isa count of the total number of directors on the board.Focal Firm Board Independence is measured as theratio of outsiders on the board. We also control forFocal Firm Patent Assertions in Federal Court, mea-sured as the number of U.S. Federal Court patentlawsuits filed by the focal firm in the prior year.

We control for other network and dyadic charac-teristics between firms.GeographicDistance betweenFirms captures the physical proximity of the head-quarters of the firms in our sample. Greater proximitymay enhance the likelihood that firms will form in-terlock ties. Following prior research (Zucker, Darby,& Brewer, 1999), we geocoded the addresses of eachfirm’s headquarters. We then calculated the Haver-sine distance, or great-circle arc length over the sur-face of the earth (Sorenson & Stuart, 2001). We alsocontrolled for Prior Alliances between Firms in ourtests of interlock formation. Existing collaborativerelationships may influence the formation of in-terlock ties (Beckman et al., 2004). Drawing fromSDCPlatinum, we included a binary variable reflectingwhether alliances were formed between all possibledyad pairs in the sample in the year prior to obser-vation. Finally, we control for Prior Patent Litigationbetween Firms, reflecting previous litigation ties be-tween dyad firms. The geographic distance, alliance,and litigation controls are loaded as sociomatrix net-work objects for our analysis.

In the longitudinal sample used to test R&D alli-ances as an outcome, we retain focal firm-yearobservations for key control variables that mightimpact the formation of litigation adversarial ties andR&D alliances. These consist of focal firm number ofpatents, number of employees, R&D expenditures,and Tobin’s Q. We also retain the prior alliance andlitigationmeasures, andwe include the independentvariables of Hypotheses 1 and 2, Alter Firm Align-ment to Focal Firm Core Technology Trajectory andAlter Firm Patent Assertions in Focal Firm CoreTechnology Area, as controls.

Network Level Analysis

In order to test our hypotheses,we employStochasticActor-Oriented Models (SAOMs) (Burk, Steglich, &Snijders, 2007; Snijders, van deBunt, & Steglich, 2010),a technique that uses Markov Chain Monte Carlo Max-imum Likelihood Estimation (MCMC-MLE) to model

network evolution (Snijders, 2005). This allows us toproperly model tie interdependence across the boardinterlocknetwork,appropriatelycapture the time-basednature of network tie formation through longitudinalanalysis, and test whether the hypothesized effects ofknowledge dependence persist when analyzed in thebroadernetwork context.We implemented thenetworkanalysis using Siena version 3.2 (Snijders, Steglich,Schweinberger, & Huisman, 2008). We provide a moredetailed discussion of the mathematical assumptionsunderlying SAOM analysis in the Appendix.

We treat the board interlock network as a directednetwork, meaning that we distinguish between sentand received ties. In our analysis, the directionalityof the tie travels from the focal firm (i.e., hiring) boardto the alter firm. From a theoretical perspective, thetwo possible directions of interlock tie formationcorrespond to the distinct mechanisms of cooptationand infiltration in the efforts of firms to address theirdependence (Palmer, 1983). In the phenomenon atthe center of our study, dependent firms invite di-rectors of alter firms to join their boards. From theresourcedependenceperspective, thebenefits of thisform of interlock accumulate from the reduction independence that derives from coopting a morepowerful actor (Pfeffer & Salancik, 1978). In contrast,the infiltrationmodel suggests that a dependent firmthat sends an executive or director to a more pow-erful firm can gain benefits from such exchange. Thislatter approach has been applied in political sociol-ogy, but in general it is seen as an alternative mech-anism to the cooptationmodel suggested by resourcedependence theory (Mizruchi, 1996) and theorizedto be at work in our study.

In addition to control variables for the firm-specific and dyadic factors, we include a number ofnetwork structural terms that have been shown toimpact network tie formation (Kim, Howard, CoxPahnke, & Boeker, 2016). The Rate Parameters rep-resent the average number of changes in the networkbetween the discrete panels in our network longitu-dinal structure (Burk et al., 2007). For example, the2003 rate parameter captures the average number offormed and dissolved interlock ties for a sample firmbetween the observed networks in 2002 and 2003.The Outdegree (Density) term serves as an interceptin SAOM analysis, reflecting the baseline tendencytoward outgoing tie formation in the observed net-work (Wasserman & Pattison, 1996). Reciprocitycaptures the social tendency of firms to reciprocateinterlock ties, i.e., inviting a director from anotherfirm to serve on its board in response to a simi-lar invitation from the other firm. We note that


reciprocal ties involve concurrent links betweenfirms based on board membership of two separatedirectors—one originating as a board member fromeach of the two firms. The Indegree Popularity termmodels the general attractiveness of a given firm inreceiving a greater number of invitations for boardinterlocks. Finally, the Transitive Closure parametercontrols for transitivity in tie formation.When firmAhas simultaneous interlock ties to firms B and C, thisterm captures the increased tendency for B and C tosubsequently form an interlock.

Table 2 presents the parameters included in ourSAOM analysis, along with a description of the cor-responding social processes of tie formation.

RESULTS

We present the descriptive statistics and bivariatecorrelations of the study variables in Table 3,5 andthe results of our SAOManalysis in Table 4. Variablecoefficients and standard errors are reported, alongwith significance levels corresponding to two-tailedtests of the hypotheses.

To satisfy the assumption that the observed panelsrepresent time slices of a gradually evolving networkand are thus suitable for stochastic modeling tech-niques, it is necessary to calculate the extent of thechanges in network ties between consecutive wavesof observation. A quantitative measure of change be-tween time periods can be captured through theJaccard index (Jaccard, 1900). Thismeasures the ratioof ties common between time waves to the sum ofcommon, newly formed, and terminated ties. Valuescloser to 1 correspond to relatively low levels of net-work tie change, while values approaching zero re-flect substantial levels of change. Jaccard indexvaluesgreater than 0.3 are shown to satisfy the assumptionsof stochastic network evolution (Snijders et al., 2010).We calculated the Jaccard index for each panel tran-sition and our data satisfied this requirement.6

The Siena models shown in Table 4 meet the re-quirements for convergence, with t-ratios less than0.25 for the overall model and less than 0.1 formodel predictors (Snijders et al., 2008). The net-work structural terms in Model 2 are significant in

predicting changes in interlock tie formation. Reci-procity, indegree popularity, and transitive closureall have significant effects, demonstrating that someof the observed variation in network tie evolutionoccurs due to the underlying social processes asso-ciated with these parameters. This confirms that theassumption of independence between interlock tiesis not valid in our sample and that conventional lo-gistic regression techniqueswould fail to account forthese structural factors.

Model 3 incorporates the variables associated withHypotheses 1–3. The coefficient for Alter Firm Align-ment to Focal Firm Core Technology Trajectory ispositive and significant in predicting interlock for-mation (b51.754,p, .001).Thisprovides support forHypothesis 1, that alter firms more closely aligned tothe global trajectory of the focal firm’s core technologyaremore likely toelicit interlocks.Themeasureof alterfirm patent assertions in the focal firm’s core tech-nology area also shows a positive, significant co-efficient (b5 0.123p, .05). This supportsHypothesis2, that a greater tendency to pursue lawsuits in thefocal firm’s technology area increases the likelihoodofboard interlock tie formation with a given firm.

We next turn to the empirical test of Hypothesis 3,whichpredicts that theexistenceofboard interlock tieswill reduce the likelihood of adversarial tie formationin the form of patent litigation. We follow a similarapproach in modeling the evolution of the patent liti-gation network using SAOMs, implemented throughSiena. The results of our Sienamodels for the litigationnetwork among sample firms are shown in Table 5.

We once again check the suitability of stochasticevolution models in the patent litigation by calcu-lating the Jaccard indexof changes betweenpanels inour network data. Our calculations show that ourdata meet the requirements for the stochastic mod-eling approach.7 The models shown in Table 5 meetthe convergence threshold with t-statistic valuesbelow 0.1 for all covariates and below 0.25 formodelconvergence. The inclusion of network structuraleffects in Model 5 shows that indegree popularity issignificant, suggesting that some sample firms havea greater general tendency to benamed as defendantsin patent suits. Interestingly, transitive closure is notsignificant in Model 5, providing evidence that thedynamics of adversarial tie formation work differ-ently from the type of friendship ties represented byinterlock relationships. Overall, the significance ofnetwork structural effects in the patent litigation

5 Descriptive statistics and correlations in Table 3 arebased on our dyad-year data structure. SAOM analysismodels the evolution of tie formation at the network leveland thusdoesnot allowus to calculate bivariatecorrelationsthatmaybegenerated from firm-ordyad-levelobservations.

6 Jaccard index valueswere 0.388 for 2002–2003, 0.449 for2003–2004, 0.484 for 2004–2005, and 0.316 for 2005–2006.

7 Jaccard index resultswere 0.641 for 2002–2003, 0.776 for2003–2004, 0.688 for 2004–2005, and 0.730 for 2005–2006.


models again supports the use of network-levelanalysis in analyzing the evolution of ties.

Model 6 inTable 5 incorporates the board interlocktie variable. In the Siena framework, board interlocksthat are present in time t-1 predict the formation oflitigation ties in time t for each panel observation. Asshown in Model 6, the coefficient associated withboard interlocks is negative and highly significant(b5 –14.564,p, .001). This supports the assertion ofHypothesis 3, that firms sharing an interlock will notpursue patent litigation against each other.

Finally, we test the role of board interlock ties ininfluencing R&D alliance formation. For this analysis,we employ a more conventional panel regressionframework, rather than SAOM models of network

evolution. While data on interlock tie formation andpatent litigation offer specific dates for tie formationand dissolution (e.g., directors resign or lawsuits areresolved or dismissed), dates of R&D alliance dissolu-tion are seldom reported in the press or through otherpublic filings.Asa result,wecannotproperlyassess theevolution of change in the alliance network, calculateaccurate Jaccard ratios, ordeterminewhether the rateofchange is suitable for the application of stochasticmodeling techniques. We constructed a dyad-yearpanel structure with one observation for each poten-tial firm pair, in each year of the 2002–2006 study pe-riod, resulting in more than 1.2 million observations.

We treat the alliance outcome variable as a binarymeasure, equaling 1 if the dyad firms form an

TABLE 2Social Network Analysis Terms

Parameter Diagram Social Process

Network structural effectsOutdegree (density) Baseline tendency for alliance tie formation

Reciprocity Tendency toward reciprocity in alliance tie formation

Indegree popularity Tendency toward variation in the degree to which an actor receivesmultiple ties

Transitive closure Tendency for the closure of transitive triads

Covariate network effectsPatent citation tie (multiplexity) Tendency for firm dyads that have patent citation or prior alliance ties

to form a board interlock tieAlliance tie (multiplexity)

Actor relation effectsAlter firm alignment to focal firm core technology

trajectoryTendency of firms to form interlock tieswith counterpartsmore closelyaligned to the global trajectory of their core technology orcounterparts that pursue patent litigation in their core technologyAlter firm patent assertions in focal firm core

technology areaPrior alliances between firmsPrior patent litigation between firmsAlter firm: Focal firm difference in employees Tendency of firms with greater difference in # of employees, R&D

spending, financial performance, and number of patents, geographicdistance, or in the same industry to form ties

Alter firm: Focal firm difference in R&D expendituresAlter firm: Focal firm difference in Tobin’s QAlter firm: Focal firm difference in patentsGeographic distance between firmsIndustry match controls (six-digit NAICS, North

American Industry Classification System)

Actor characteristic effectsFocal firm technological uncertainty Tendency of firms with more technological uncertainty, a given

number of patents, number of employees, rate of R&D spending,financial performance, CEO duality, board size, and degree of boardindependence to form ties with any other firm

Focal firm number of patentsFocal firm average age of technologyFocal firm number of employeesFocal firm R&D expendituresFocal firm Tobin’s QFocal firm CEO dualityFocal firm board sizeFocal firm board independence


alliance in the observation year and 0 otherwise. Inthe dyad-year structure, alliance formation is an ex-ceptionally rare outcome event, with less than 0.1%of observations yielding a new alliance tie. Maxi-mum likelihood estimation in logistic regression forsuch rare outcomes may lead to a small sample bias.We thus employ rare events logistic regression (King& Zeng, 2001), which uses a bias correction methodto address this problem. The models reflecting theresults of this analysis are shown in Table 6.

Model 8 in Table 6 provides the results for the testof Hypothesis 4. The coefficient associated withboard interlock network ties is positive and signifi-cant in predicting the formation of R&D alliances(b 5 1.291, p , .001). This provides support forHypothesis 4, that having formed aprior interlock tieincreases the likelihood that a focal firm will obtainaccess to the knowledge of the alter firm throughcollaborative R&D alliances.

Robustness Tests

While the SAOM analysis provides substantialbenefits in terms of the ability to model in-terdependent network structures in a longitudinalfashion, we also conducted a conventional re-gression analysis to help corroborate our findings.We used the dyad-year longitudinal data structurefrom our tests of Hypothesis 4. Once again, we userare events logistic regression in order to model theexceedingly rare outcome of interlock ties formedbetween sample firms across the many dyad combi-nations in our observationperiod. The results for thisregression analysis are presented in Table 7.

As shown in Table 7, the alter firm alignment withthe global trajectory of the focal firm’s technology(b5 1.890,p, .001) and thepursuit ofpatent litigationby the alter firm in this technology area (b 5 0.137,p, .05) are both significant in predicting interlock tieformation. We also note that in contrast to the base-line findings of Table 4, the variable, Citation of AlterFirm Technology, is positive and significant in pre-dicting interlock formation in the Table 7 analysis.This may provide some evidence of a knowledgetransfer motivation for board interlocks, while at thesame time retaining support for our hypothesizedeffects of knowledge dependence.8

We also tested an alternative operationalization ofthe effects of alter firm litigation on interlock tieformation. We constructed an interaction variablebetween prior patent litigation (the number of suitsfiled by the alter firm in the previous year) and thecore trajectory alignment with the focal firm. Withinour theoretical framework, alter firm litigious-ness and greater focal firm knowledge dependenceshouldplausiblyhave a compounding effect, leadingto a greater likelihood of interlock tie formation. Thisrelationship is tested in Model 11 of Table 7. Asshown, the interaction variable has a positive, sig-nificant effect on interlock tie formation (b 5 0.116,p, .05), offering further evidence of the relationshipbetween alter firm litigation behavior and interlockties.9

DISCUSSION

Our study provides evidence that firms managetheir knowledge dependence by engaging in in-terorganizational tie formation and uncovers condi-tions that may increase the likelihood of such tieformation. Knowledge dependence in our studyemerges from relational factors describing knowl-edge characteristics between firms and actions ofcounterparts in defending their proprietary knowl-edge. The formation of director interlocks is recog-nized as an important strategy for providing accessto critical resources (Boyd, 1990; Mizruchi, 1996;Pfeffer, 1972). In other words, a firm may coopt an-other firm in the environment by appointing an in-dividual representing the firm or who has a tie to theexternal dependence (Pfeffer, 1972).

Director interlocks play a special role in the con-text of this study. Firms in our sample are motivatedto form director interlock ties owing to their positionrelative to the global technology trajectory and forreducing the threats of litigation over intellectualproperty. Our findings reveal that the position ofspecific external firms in driving the global trajec-tory in key areas of knowledge impacts a firm’sknowledge dependence-reducing strategies. Whileknowledge dependence is globally derived throughthe field-level trajectory of core knowledge, firmscan address this dependence through dyadic re-lationships with key players in the field.

The dependence-enhancing strategy of externalfirms in protecting their technologies also influencesfirm decisions to seek out new ties. Our results

8 Additionally, the fact that dyadic patent citation is notsignificant in the baseline Siena models may demonstratethat network structural effects or interdependencies be-tween network ties absorb this variance in tie formationassociated with knowledge transfer.

9 We are grateful for the input of an anonymous reviewerin suggesting this approach.


suggest that firmswill build relationships with otherfirms thatmore aggressively defend their intellectualproperty through litigation in key technology areas.Such aggressive strategies likely enhance the de-pendence of firms in the industry, increasing the riskthat they will face obstructions or penalties in theirown technology development efforts.

Finally, we find general support for the role thatboard interlocks perform in alleviating key mecha-nisms of knowledge dependence. Firms successful informing ties with outside organizations are shown toavoid costly and uncertain patent litigation fightswith their counterparts. They are also more likely tolaunch R&D alliance efforts with the interlocked firm.As a result, the dependent firm overcomes bar-riers and enhances avenues for access to importantknowledge resources in their core technology area.

Theoretical Contributions

The fact that technology advancement is a collec-tive effort spanning organizational boundaries con-tributes to resource dependence theory in a numberof ways. First, in contrast to previous research fo-cusing almost exclusively on financial resources,we investigate the nuances of managing resource

dependence involving technological knowledge.Technological knowledge, as one of the most im-portant sources of competitive advantage, createsconditions for resource dependence that cannot beextrapolated from studies focusing on the need toaccess other types of resources. Most important,the path-dependent nature of knowledge resourcesreinforces the notion that managing knowledge de-pendence requires unique mechanisms and strate-gies. Indeed, technological knowledge that resides indomains of particular technology trajectories leadsto the emergence of distinctive resource dependen-cies for individual firms as well as relationships be-tween the providers and acquirers of knowledge(Sydow et al., 2009).

In particular, this study makes a contribution byinvestigating the interlocking directorate from a re-source dependence theory perspective in a relativelynew role. Whereas most previous research from thisperspective considered board interlocks as a mech-anism to reduce political and competitive uncer-tainties, we show that interlock formationwith otherfirms provides two important means to manage re-source dependence. In particular, whereas the for-mation of interlock ties reduces the likelihood ofpatent litigation between partners, it increases the

TABLE 3Descriptive Statistics and Correlations for Dyad-Year Sample

Variable Mean SD 1 2 3 4 5 6

1 Board interlock tie formation 1.87E-04 0.012 Alter firm alignment to focal firm core technology

trajectory0.04 0.14 .008**

3 Alter firm patent assertions in focal firm coretechnology area

0.02 0.28 .004** .086**

4 Focal firm technological uncertainty 0.74 0.06 .001 .009** .0005 Patent litigation adversarial tie formation 1.23E-04 0.01 .000 .021** .012** .0016 R&D alliances 2.60E-04 0.02 .003** .019** .023** .002 .009**7 Citation of alter firm technology 0.01 0.11 .010** .163** .104** –.004** .060** .031**8 Ratio of number of employees (Alter firm: Focal

firm)26.31 279.43 –.001 .018** .031** –.010** –.001 .003**

9 Ratio of R&D expenditures (Alter firm: Focalfirm)

10.63 252.28 .000 .015** .026** –.012** .000 .003**

10 Ratio of Tobin’s Q (Alter firm: Focal firm) 1.64 4.26 –.002 –.003** –.001 –.004** –.001 –.00111 Industry match 0.10 0.30 .006** .207** .058** –.018** .025** .014**12 Alter firm number of patents 27.42 126.7 .006** .156** .048** .004** .012** .012**13 Focal firm number of patents 40.53 155.1 .003** –.012** –.004** –.009** .013** .011**14 Alter firm patent impact 24.19 172.3 .002** .105** .005** .016** .006** .003**15 Focal firm number of employees (thousands) 4.97 13.55 .002 .001 .000 .001 .014** .024**16 Focal firm R&D expenditures ($m) 206.1 749.8 .001 .021** .006** .007** .016** .027**17 Focal firm Tobin’s Q 2.89 2.52 .000 .014** .005** –.056** .002* .00118 Focal firm CEO duality 0.30 0.46 .008** –.014** –.002** .017** .007** .007**19 Focal firm board size 4.20 4.38 .013** –.018** –.001 .024** .011** .011**20 Focal firm board independence 0.35 0.36 .013** –.023** –.002 .007** .009** .007**21 Geographic distance between firms (km) 2328.6 1850.8 –.005** –.013** –.008** –.002 –.003** –.002**


likelihood of the engagement of partners in R&D al-liances. On the one hand, the reduced likelihoodof patent litigation is a result of firms’ cooptation oftheir potentially hostile counterparts by means ofboard interlocks. Since those counterpart firmspossess the legal means to erect barriers to the firms’technology development, forming bonds with thedirectors of the counterpart firms can be an effectivemechanism to manage knowledge dependence. Onthe other hand, R&D alliances can be conceptualizedas a mechanism of constraint absorption. The in-creased likelihoodofpursuingR&Dalliancebetweenfirms provides an opportunity for direct access toexternal knowledge resources. Although board in-terlocks may facilitate access to other types of re-sources as well, alliance formation is not necessarilyan outcome of those ties (Gulati & Westphal, 1999).The role of board ties in managing knowledge de-pendence through the formation of R&D alliancesbetween firmsmaybemoreprominent becauseof theunpredictable nature of technological trajectories.

Approaching the same idea from a network per-spective, our study offers a richer characterization ofinterlock ties as key pathways for influence between

firms. Beyond using them as simple conduits for in-formation, our findings show that organizationsmayleverage their network ties as a form of social capitalto gain access and forestall barriers to critical tech-nologies. Interlock relationships may thus serve asan important resource fromwhich the firmmaydrawwhen facing changes in technology and corre-sponding shifts in the knowledge dependencelandscape.10

Second, studying dependence involving techno-logical knowledge has allowed us to explore therole of a number of critical factors in resource de-pendence relationships. Specifically, we investi-gated the ways non market-based actions such aslitigation influence firms’ knowledge dependence.Even though non market-based actions have beenconsidered as important factors of firms’ resourcedependence, for the most part, they have beenneglected in previous research (Pfeffer, 2003). Firms’litigious actions, in the form of patent assertions, arean essential form of nonmarket actions that can

TABLE 3(Continued)

7 8 9 10 11 12 13 14 15 16 17 18 19 20

.004**

.005** .304**

–.010** –.005** .003**.131** –.006** .003** .005**.202** .090** .080** –.008** .030**.153** –.022** –.010** –.013** .016** –.001.138** .051** .044** –.006** .020** .686** .003**.090** –.033** –.013** –.013** .002 –.001 .472** –.002*.079** –.023** –.011** –.023** .030** –.002* .432** –.004** .820**

–.011** .060** .004** –.177** .027** –.002* –.035** –.018** –.069** –.012**.040** –.047** –.016** –.033** –.010** –.001 .140** –.005** .279** .208** –.088**.072** –.069** –.026** –.043** –.018** –.006** .256** –.019** .457** .355** –.131** .608**.066** –.067** –.025** –.041** –.013** –.006** .221** –.019** .337** .255** –.140** .649** .908**

–.023** .026** –.002* .002* –.037** –.016** –.011** –.012** –.020** –.013** .026** –.011** –.020** –.023**

*correlation is significant at the 0.05 level.**correlation is significant at the 0.01 level.

10 We are grateful to an anonymous reviewer in high-lighting this important point.


TABLE 4Siena Analysis Results—Interlock Tie Formation

Model Type Stochastic Actor Oriented Models

Outcome Variable Board Interlock Tie Formation

Rate parameters Model 1 Model 2 Model 32003 0.170 0.247 0.243

(0.026) (0.039) (0.038)2004 0.222 0.313 0.308

(0.032) (0.043) (0.043)2005 0.268 0.364 0.360

(0.032) (0.046) (0.046)2006 0.885 1.065 1.052

(0.087) (0.106) (0.108)Network structural effectsOutdegree (density) 26.927*** 28.283*** 28.250***

(0.322) (0.331) (0.344)Reciprocity — 10.022** 9.955***

(3.155) (3.280)Indegree popularity — 0.297* 0.322***

(0.141) (0.147)Transitive closure — 29.875*** 30.032***

(2.096) (1.999)Independent variablesAlter firm alignment to focal firm core technology trajectory (H1) — — 1.754***

(0.338)Alter firm patent assertions in focal firm core technology area (H2) — — 0.123*

(0.064)Control variablesFocal firm technological uncertainty 1.029*** 0.953*** 0.550*

(0.227) (0.245) (0.289)Citation of alter firm technology 20.163 20.189 20.571

(0.419) (0.457) (0.462)Alter firm: Focal firm difference in employees 20.883*** 20.674*** 20.721***

(0.165) (0.182) (0.180)Alter firm: Focal firm difference in R&D expenditures 20.491** 20.374* 20.400*

(0.183) (0.183) (0.182)Alter firm: Focal firm difference in Tobin’s Q 0.509** 0.496* 0.523**

(0.168) (0.164) (0.165)Alter firm number of patents 4.0E-04 1.0E-04 21.0E-04

(4.0E-04) (6.0E-04) (0.001)Focal firm number of patents 0.024 0.009 0.008

(0.019) (0.008) (0.006)Alter firm patent impact 21.0E-05 2.0E-04 2.0E-04

(3.0E-04) (3.0E-04) (4.0E-04)Focal firm number of employees 20.025 20.010 20.002

(0.025) (0.014) (0.010)Focal firm R&D expenditures 4.0E-04 3.0E-04 1.0E-04

(7.0E-04) (3.0E-04) (3.0E-04)Focal firm Tobin’s Q 0.091 0.042 0.040

(0.069) (0.046) (0.182)Focal firm CEO duality 20.446 20.421 20.349

(0.514) (0.380) (0.368)Focal firm board size 20.079 20.079 20.085

(0.102) (0.073) (0.075)Focal firm board independence 1.723 1.189 1.092

(1.137) (0.829) (0.844)Geographic distance between firms 21.0E-04*** 21.0E-04*** 21.0E-04***

(1.0E-05) (1.0E-06) (1.0E-06)Prior alliances between firms 0.812 1.624 1.9171

(1.704) (1.334) (1.110)


profoundly affect other firms’ dependencies associ-atedwith knowledge.Asour results suggest, litigiousactions by their counterparts prompt firms to forminterlock ties with those counterparts in order tomanage their knowledge dependence.

Finally, our study provides empirical evidenceas well as presents anecdotal accounts of execu-tives about the role of boards of directors inmanaging their firms’ knowledge dependence.Appointing directors with ties to other firms withtechnological knowledge may help their firmscope with dependence on external technologydevelopments in different ways. Dependent firmshave the need to understand the current andforward-looking trajectory of their core technol-ogy, the desire to influence this trajectory, and theneed to forestall the actions of their counterpartsthat may preempt the future use of knowledge intheir core technology area. We offer evidence thatboards of directors may support and potentiallyplay meaningful roles in achieving these goals. Inlight of the growing skepticism about the effec-tiveness of the board of directors in corporations,these findings have important implications. Bymanaging knowledge dependence in the envi-ronment, board members may indirectly butconsiderably contribute to their firms’ innovativecapabilities.

Limitations and Future Research

While we have attempted to empirically exam-ine the relationship between knowledge de-pendence and board interlocks, our study islimited in the ability to observe the process un-derlying interlock formation. Despite the signifi-cant attention on board interlocks as a criticalelement of corporate governance (e.g., Connelly

et al., 2011; Martin, Gozubuyuk, & Becerra, 2015),relatively few prior studies have focused specifi-cally on the mechanisms of interlock tie formation(Mizruchi & Stearns, 1988; Withers et al., 2012),and even fewer have incorporated intermediatestructural effects through network-level analysis(Harrigan & Bond, 2013; Kim et al., 2016). As a re-sult,we are somewhat limited in our understandingof the many factors that may play a role in interlockformation. Our findings show clear relationshipsbetween characteristics of knowledge dependenceand corporate interlock tie formation, but morework is needed to provide a broader picture of thedynamics of tie formation in this interorganizationalnetwork context.

Knowledge may be more complex and prone tochange in comparison to the capital and marketresources that have traditionally been examinedthrough resource dependence theory (Hillman &Dalziel, 2003; Pfeffer & Salancik, 1978, 2003). Al-though our study examines the initial formation ofties in response to knowledgedependence,wedonotobserve how this relationship plays out over broadertimespans. Firms, industries, and scientific fieldsmay follow unpredictable trajectories (Adner &Kapoor, 2016; Dosi, 1982). As a result, the de-pendence of one firm on anothermay shift over time,changing or removing its motivation to maintain tiesas a strategy of coopting its external knowledge de-pendence. Longer-range studies may yield more in-sight into the effects of technology trajectories onknowledge dependence.

Our theoretical development of and researchfindings regarding knowledge dependence pro-vide a number of promising avenues for future re-search. For example, researchmay look to examinethe dynamics of knowledge dependence in termsof power imbalance and mutual dependence

TABLE 4(Continued)



Prior patent litigation between firms 0.042 0.043 20.105(0.143) (0.153) (0.196)

Industry match 1.028*** 0.951*** 0.981***(0.126) (0.138) (0.136)

1p , .1*p , .05

**p , .01***p , .001 (standard errors in parentheses)


TABLE 5Siena Analysis Results—Patent Litigation Tie Formation


Outcome Variable Patent Litigation Adversarial Tie Formation

Rate parameters Model 4 Model 5 Model 62003 0.133 0.136 0.130

(0.036) (0.037) (0.035)2004 0.116 0.119 0.115

(0.033) (0.034) (0.033)2005 0.175 0.181 0.176

(0.041) (0.044) (0.042)2006 0.172 0.178 0.176

(0.041) (0.042) (0.042)Network structural effectsOutdegree (density) 27.608*** 28.936* 28.418***

(1.667) (3.566) (2.572)Reciprocity — 49.210 84.251

(2071) (68.354)Indegree popularity — 0.713** 0.736***

(0.262) (0.208)Transitive closure — 21.799 25.834

(25.06) (62.827)Independent variableBoard interlock tie (H3) — — 214.564***

(2.0E-04)Control variablesCitation of alter firm technology 2.374** 1.937*** 1.954***

(0.810) (0.510) (0.501)Alter firm: Focal firm difference in employees 20.379 20.629 20.620

(0.526) (1.514) (0.502)Alter firm: Focal firm difference in R&D expenditures 20.458 20.567 20.499

(0.775) (0.834) (0.562)Alter firm: Focal firm difference in Tobin’s Q 0.249 0.593 0.629

(0.435) (0.633) (0.449)Alter firm number of patents 0.001 3.0E-04 3.0E-04

(0.001) (0.001) (7.0E-04)Focal firm number of patents 0.229 0.489 0.474

(0.466) (1.974) (0.615)Alter firm patent impact 24.0E-04 24.0E-04 23.0E-04

(5.0E-04) (9.0E-04) (6.0E-04)Focal firm number of employees 0.006 0.019 0.045

(0.302) (0.730) (0.205)Focal firm R&D expenditures 20.002 20.005 20.003

(0.006) (0.063) (0.014)Focal firm Tobin’s Q 0.428 0.970 0.860

(0.652) (4.352) (1.316)Geographic distance between firms 21.0E-04 22.0E-04 21.0E-04

(1.0E-04) (1.0E-04) (1.0E-04)Industry match 1.069** 1.888*** 1.886***

(0.415) (0.562) (0.504)Prior alliances between firms 24.138 21.972 213.747***

(16.306) (7.034) (2.0E-04)Alter firm alignment to focal firm core technology trajectory 1.687* 1.375 1.152

(0.703) (0.841) (0.787)Alter firmpatent assertions in focal firm core technology area 0.112 0.006 0.010

(0.127) (0.584) (0.222)

*p , .05**p , .01

***p , .001 (standard errors in parentheses)


(Casciaro & Piskorski, 2005; Gulati & Sytch, 2007).We hypothesized that drawing upon other firms’knowledge and technological resources leads toknowledge dependence and, in turn, interlockformation. However, research also highlightsmutual dependence, or “the existence of bilateraldependencies,” as an important influence in theability to absorb external dependence (Casciaro& Piskorski, 2005: 170). From this view, bothpotential interlocking firmsmust be motivated toform the interfirm tie. Similarly, power imbal-ance between exchange partners may be partic-ularly problematic when knowledge resources

are exchanged (Easterby-Smith, Lyles, & Tsang,2008).

Future work may explore knowledge dependenceduring extreme changes, such as the overthrowof technology paradigms though disruptive in-novations (Schumpeter, 1934; Tripsas, 1997). Newentrants often trigger this process (Christensen,2013), developing innovations that significantlyalter the technological trajectory. The prevailingtrajectory of knowledge recombination in a firm’score technologies may shift through disruptive in-novations, changing the industry landscape in termsof which organizations are more or less closely

TABLE 6Regression Analysis Results—R&D Alliances

Model Type Rare Events Logistic Regression

Outcome Variable R&D Alliance (Binary)

Model 7 Model 8Independent variablesBoard interlock tie (H4) 1.291***

(0.235)Control variablesCitation of alter firm technology 1.574*** 1.535***

(0.158) (0.159)Alter firm: Focal firm difference in employees 7.64E-05*** 7.64E-05***

(3.85E-06) (3.85E-06)Alter firm: Focal firm difference in R&D expenditures 1.46E-04*** 1.47E-04***

(2.94E-06) (2.95E-06)Alter firm: Focal firm difference in Tobin’s Q 0.009 0.012

(0.012) (0.011)Alter firm number of patents 0.001*** 0.001***

(5.63E-05) (5.72E-05)Focal firm number of patents 2.14E-05 2.81E-05

(8.13E-05) (8.05E-05)Alter firm patent impact 29.46E-04*** 29.54E-04***

(5.94E-05) (6.06E-05)Focal firm number of employees 0.003 0.003

(0.003) (0.003)Focal firm R&D expenditures 2.12E-04*** 2.09E-04***

(6.21E-05) (6.12E-05)Focal firm Tobin’s Q 20.031 20.025

(0.028) (0.027)Prior litigation between firms 20.171 20.160

(0.907) (0.906)Prior alliances between firms 9.033*** 9.011***

(0.079) (0.079)Alter firm alignment to focal firm core technology trajectory 1.391*** 1.373***

(0.322) (0.320)Alter firmpatent assertions in focal firm core technology area 0.077 0.090

(0.106) (0.105)Constant 29.363*** 29.382***

(0.113) (0.112)Model Wald x2 5527.33*** 5520.28***Sample size—# of firm dyad-years 1,223,726 1,223,726

***p , .001 (robust standard errors reported in parentheses)


TABLE 7Robustness Test Results—Interlock Tie Formation

Model Type Rare Events Logistic Regression


Model 9 Model 10 Model 11Independent variablesAlter firm alignment to core technology trajectory (H1) — 1.890*** 3.095**

(0.200) (0.986)Alter firm patent assertions in core technology area (H2) — 0.137* —

(0.070)Alter firm alignment to core technology trajectory x prior patent litigation — — 0.116*

(0.053)Control variablesFocal firm technological uncertainty 21.726 0.819 22.389

(2.549) (1.593) (3.075)Citation of alter firm technology 1.691*** 0.660* 0.790**

(0.408) (0.306) (0.264)Alter firm: Focal firm difference in employees 0.001*** 0.001*** 6.03E-04***

(1.40E-05) (1.86E-05) (1.76E-05)Alter firm: Focal firm difference in R&D expenditures 20.013 0.001 20.020

(0.017) (0.001) (0.026)Alter firm: Focal firm difference in Tobin’s Q 20.132*** 20.199** 20.090*

(0.030) (0.065) (0.044)Industry match 0.387* 0.545** 0.3521

(0.177) (0.189) (0.212)Alter firm number of patents 26.47E-05 0.001*** 21.44E-04

(9.08E-05) (1.14E-04) (9.38E-05)Focal firm number of patents 1.64E-04 6.44E-04 7.57E-04***

(2.48E-04) (4.98E-04) (1.53E-04)Alter firm patent impact 22.60E-04*** 28.80E-05 22.0E-04**

(3.32E-05) (2.40E-04) (7.68E-05)Focal firm number of employees 20.031* 20.025 20.031*

(0.013) (0.017) (0.014)Focal firm R&D expenditures 21.88E-04 21.41E-04 21.57E-04

(2.64E-04) (2.61E-04) (2.40E-04)Focal firm Tobin’s Q 20.182 0.018 20.163

(0.175) (0.049) (0.164)Focal firm CEO duality 20.123 20.048 20.167

(0.181) (0.188) (0.214)Focal firm board size 0.184*** 0.201*** 0.168***

(0.037) (0.040) (0.039)Focal firm board independence 2.188*** 2.246*** 2.580***

(0.399) (0.412) (0.447)Geographic distance between firms 23.06E-04* 22.06E-04* 23.13E-041

(1.55E-04) (8.77E-05) (1.70E-04)Prior alliances between firms 2.575*** 2.165* 2.847***

(0.112) (0.961) (0.189)Prior patent litigation 0.051*** 0.018** 0.003

(0.003) (0.006) (0.018)Alter firm industry dummies included included includedConstant 27.320* 211.325*** 27.289*

(2.927) (1.341) (2.918)Model Wald x2 1,789*** 430.90*** 2,149***Sample size—# of firm dyad-years 1,223,726 1,223,726 1,223,726

1p , .1*p , .05

**p , .01***p , .001 (robust standard errors reported in parentheses, clustered by four-digit SIC code)


aligned. While the players may change, the rolescould remain the same, with different organizationsoccupying a favored location, closely aligned withthe new technological trajectory. Research may re-veal how these new firms may develop the ability tocreate or remove barriers to access, again resulting inknowledge dependence for firmsworking to developthe focal technologies.

Our work also demonstrates the importance oftesting interorganizational tie formation at a broaderlevel of analysis of knowledge dependence. Bymodeling tie formation as a social network, ratherthan a series of discrete ties between firm pairs, wehave been able to capture variance in local networkstructure and social processes such as transitivity.Our findings show that these intermediate structuraleffects are significant, suggesting that conventionalapproaches may omit critical aspects of the networkstructure. This consideration is particularly impor-tant for future research in the context of technologydevelopment as a field-level, networked effortspanning organizational boundaries. Furthermore,subsequent research may approach interlocks asa bimodal phenomenon, with firms and directorsserving as distinct types of network nodes. Thiswould allow the simultaneous study of firm-leveland director-level behaviors and characteristics, of-fering a richer view of the dynamics of directorselection.

Finally, whilewehave focused on the formation ofboard interlocks, knowledge dependence may leadfirms to pursue other strategies for reducing this de-pendence as well. In particular, research recognizesstrategic alliances and mergers and acquisitions asimportant ways to manage general dependence(Rogan & Greve, 2014; Xia, 2011) and manage andacquire knowledge (Wang, Rodan, Fruin, & Xu,2014). Knowledge dependence may be an impor-tant, yet unexplored, factor that influences allianceformation and acquisition decisions. These othermechanismsand their interactionsprovide anumberof important questions for future research. For ex-ample, when attempting to manage knowledge de-pendencies, how do firms decide between thesedifferent dependence-reducing mechanisms? Dothese mechanisms serve more as complements orsubstitutes?

CONCLUSION

We examine knowledge as a critical class of re-sources that plays an exceptional role in determiningdependence on other organizations. We develop

a theoretical framework to characterize dynamics ofdependence that are unique to the knowledge con-text and provide an empirical test of our assertions.We present key factors determining knowledge de-pendence: external firms’ alignment to core techno-logical trajectories and assertions of intellectualproperty. Our empirical study examines board ofdirector interlock tie formation among publicly-traded U.S. firms in technology industries. Consis-tent with our theoretical framework, we find thatfirms are more likely to engage in interlock tie for-mation with other organizations when they exhibitthese characteristics of knowledge dependence.Modeling firm interlocks at the network level, weaccount for the role of intermediate network struc-ture and social processes of tie formation that actbeyond the dyadic level. Our work offers importantcontributions to research on resource dependence,innovation, and social networks.

REFERENCES

Adner, R., & Kapoor, R. 2016. Innovation ecosystems andthe pace of substitution: Re-examining technologys-curves. StrategicManagement Journal, 37: 625–648.

Alexy, O., George, G., & Salter, A. J. 2013. Cui bono? Theselective revealing of knowledge and its implicationsfor innovative activity. Academy of ManagementReview, 38: 270–291.

Anderson, P., & Tushman, M. L. 1990. Technological dis-continuities and dominant designs: A cyclical modelof technological change. Administrative ScienceQuarterly, 35: 604–633.

Arora,A., Fosfuri, A., &Gambardella, A. 2004.Markets fortechnology: The economics of innovation and cor-porate strategy. Cambridge, MA: MIT press.

Beckman, C. M., Haunschild, P. R., & Phillips, D. J. 2004.Friends or strangers? Firm-specific uncertainty, mar-ket uncertainty, and network partner selection. Or-ganization Science, 15: 259–275.

Beckman,C.M., Schoonhoven,C.B., Rottner, R.M.,&Kim,S.-J. 2014. Relational pluralism in de novo organiza-tions: Boards of directors as bridges or barriers to di-verse alliance portfolios? Academy of ManagementJournal, 57: 460–483.

Boyd, B. 1990. Corporate linkages and organizational en-vironment: A test of the resource dependence model.Strategic Management Journal, 11: 419–430.

Brusoni, S., Prencipe, A., & Pavitt, K. 2001. Knowledge spe-cialization, organizational coupling, and the boundariesof the firm: Why do firms know more than they make?Administrative Science Quarterly, 46: 597–621.


Burk, W. J., Steglich, C. E. G., & Snijders, T. A. B. 2007.Beyond dyadic interdependence: Actor-orientedmodels for co-evolving social networks and individ-ual behaviors. International Journal of BehavioralDevelopment, 31: 397–404.

Burt, R. S., Christman, K. P., & Kilburn, H. C., Jr. 1980.Testing a structural theory of corporate cooptation:Interorganizational directorate ties as a strategy foravoiding market constraints on profits. AmericanSociological Review, 45: 821–841.

Carnabuci, G., & Bruggeman, J. 2009. Knowledge special-ization, knowledge brokerage and the uneven growthof technology domains. Social Forces, 88: 607–641.

Casciaro, T., & Piskorski, M. J. 2005. Power imbalance,mutual dependence, and constraint absorption: Acloser look at resource dependence theory. Adminis-trative Science Quarterly, 50: 167–199.

Cassiman, B., & Veugelers, R. 2006. In search of comple-mentarity in innovation strategy: Internal R&D andexternal knowledge acquisition. Management Sci-ence, 52: 68–82.

Cefis, E., & Marsili, O. 2005. A matter of life and death:Innovation and firm survival. Industrial and Corpo-rate Change, 14: 1167–1192.

Chatterji, A. K., & Fabrizio, K. R. 2014. Using users: Whendoes external knowledge enhance corporate productinnovation? Strategic Management Journal, 35:1427–1445.

Christensen, C. 2013. The innovator’s dilemma: Whennew technologies cause great firms to fail. Cam-bridge, MA: Harvard Business Review Press.

Christensen, C. M., & Rosenbloom, R. S. 1995. Explainingthe attacker’s advantage: Technological paradigms,organizational dynamics, and the value network. Re-search Policy, 24: 233–257.

Cohen, W. M., & Levinthal, D. A. 1990. Absorptive capac-ity: A new perspective on learning and innovation.Administrative Science Quarterly, 35: 128–152.

Cohen, W. M., Nelson, R. R., & Walsh, J. 2000. Protectingtheir intellectual assets: Appropriability conditionsand why U.S. Manufacturing firms patent (or not).Working paper no. 7552, National Bureau of Eco-nomic Research, Washington, D.C.

Collins, P., & Wyatt, S. 1988. Citations in patents to thebasic research literature. Research Policy, 17: 65–74.

Connelly, B. L., Johnson, J. L., Tihanyi, L., & Ellstrand,A. E.2011. More than adopters: Competing influences inthe interlocking directorate. Organization Science,22: 688–703.

Davis, G. F. 1991.Agentswithout principles? The spreadofthe poison pill through the intercorporate network.Administrative Science Quarterly, 36: 583–613.

Davis, G. F., & Cobb, J. A. 2010. Resource dependencetheory: Past and future. In C. B. Schoonhoven &F. Dobbin (Eds.), Research in the sociology of orga-nizations: 21–42: Bingley, UK: Emerald Group Pub-lishing Limited.

DeCarolis, D. M., & Deeds, D. L. 1999. The impact of stocksand flows of organizational knowledge on firm perfor-mance: An empirical investigation of the biotechnologyindustry.StrategicManagement Journal, 20: 953–968.

Diestre, L., Rajagopalan, N., & Dutta, S. 2015. Constraints inacquiring and utilizing directors’ experience: An em-pirical study of new-market entry in the pharmaceuticalindustry. StrategicManagement Journal, 36: 339–359.

Dosi, G. 1982. Technological paradigms and technologicaltrajectories: A suggested interpretation of the de-terminants and directions of technical change. Re-search Policy, 11: 147–162.

Dosi, G. 1988. Sources, procedures, and microeconomiceffects of innovation. Journal ofEconomicLiterature,26: 1120–1171.

Drees, J. M., & Heugens, P. P. M. A. R. 2013. Synthesizingand extending resource dependence theory: A meta-analysis. Journal of Management, 39: 1666–1698.

Dunford, R. 1987. The suppression of technology asa strategy for controlling resource dependence. Ad-ministrative Science Quarterly, 32: 512–525.

Easterby-Smith, M., Lyles, M. A., & Tsang, E. W. K. 2008.Inter-organizational knowledge transfer: Currentthemes and future prospects. Journal ofManagementStudies, 45: 677–690.

Eisenhardt, K. M., & Santos, F. M. 2002. Knowledge-basedview: A new theory of strategy? In A. Pettigrew,H. Thomas & R. Whittington (Eds.), Handbook ofstrategy and management: 139–164. London: Sage.

Fleming, L. 2001. Recombinant uncertainty in technolog-ical search. Management Science, 47: 117–132.

Fleming, L., &Sorenson,O. 2001. Technology as a complexadaptive system: Evidence frompatent data.ResearchPolicy, 30: 1019–1039.

Fleming, L., & Sorenson, O. 2004. Science as a map intechnological search. Strategic Management Jour-nal, 25: 909–928.

Galunic, D. C., & Rodan, S. 1998. Resource recombinationsin the firm:Knowledge structures and the potential forschumpeterian innovation. Strategic ManagementJournal, 19: 1193–1201.

Gatignon, H., Tushman, M. L., Smith, W., & Anderson, P.2002. A structural approach to assessing innovation:Construct development of innovation locus, type, andcharacteristics.Management Science, 48: 1103–1122.

Grant, R. M. 1996. Toward a knowledge-based theory of thefirm. StrategicManagement Journal, 17(S2): 109–122.


Green, S. 2005. Sarbanes-oxley and the board of di-rectors. Hoboken, NJ: Wiley.

Gulati, R. 1995a. Does familiarity breed trust? The impli-cations of repeated ties for contractual choice in alli-ances. Academy of Management Journal, 38:85–112.

Gulati, R. 1995b. Social structure and alliance formationpatterns: A longitudinal analysis. AdministrativeScience Quarterly, 40: 619–652.

Gulati, R., & Sytch, M. 2007. Dependence asymmetry andjoint dependence in interorganizational relationships:Effects of embeddedness on a manufacturer’s perfor-mance in procurement relationships. AdministrativeScience Quarterly, 52: 32–69.

Gulati, R., & Westphal, J. D. 1999. Cooperative or control-ling? The effects of CEO-board relations and the con-tent of interlocks on the formation of joint ventures.Administrative Science Quarterly, 44: 473–506.

Harrigan, N., & Bond, M. 2013. Differential impact of di-rectors’ social and financial capital on corporate in-terlock formation. In D. Lusher, J. Koskinen &G. Robins (Eds.), Exponential random graph modelsfor social networks: Theory, methods and applica-tions: 260–271. Cambridge: Cambridge UniversityPress.

Haunschild, P. R. 1993. Interorganizational imitation: Theimpact of interlocks on corporate acquisition activity.Administrative Science Quarterly, 38: 564–592.

Haunschild, P. R., & Beckman, C. M. 1998. When do in-terlocksmatter?: Alternate sources of information andinterlock influence. Administrative Science Quar-terly, 43: 815–844.

Helfat, C. E. 1994. Evolutionary trajectories in petroleumfirm R&D. Management Science, 40: 1720–1747.

Henderson, R. M., & Clark, K. B. 1990. Architectural in-novation: The reconfiguration of existing producttechnologies and the failure of established firms. Ad-ministrative Science Quarterly, 35: 9–30.

Hillman, A. J., Cannella, A. A., & Paetzold, R. L. 2000. Theresource dependence role of corporate directors:Strategic adaptation of board composition in responseto environmental change. Journal of ManagementStudies, 37: 235–255.

Hillman, A. J., & Dalziel, T. 2003. Boards of directors andfirm performance: Integrating agency and resourcedependence perspectives. Academy of ManagementReview, 28: 383–396.

Hillman, A. J., Withers, M. C., & Collins, B. J. 2009. Re-source dependence theory: A review. Journal ofManagement, 35: 1404–1427.

Holder-Webb, L., Cohen, J., Nath, L., & Wood, D. 2008. Asurvey of governance disclosures among U.S. Firms.Journal of Business Ethics, 83: 543–563.

Jaccard, P. 1900.Contributions auproblemede l’immigrationpost-glaciaire de la flore alpine. Bulletin de la SocieteVaudoise des Sciences Naturelles, 37: 547–579.

Jaffe, A. B., & Trajtenberg, M. 2002. Patents, citations, andinnovations: A window on the knowledge economy.Cambridge, MA: MIT press.

Johannisson, B., &Huse,M. 2000. Recruiting outside boardmembers in the small family business: An ideologicalchallenge. Entrepreneurship and Regional Devel-opment, 12: 353–378.

Joshi, A.M., &Nerkar, A. 2011.Whendo strategic alliancesinhibit innovation by firms? Evidence from patentpools in the global optical disc industry. StrategicManagement Journal, 32: 1139–1160.

Jung, H. J., & Lee, J. 2016. The quest for originality: A newtypology of knowledge search and breakthrough in-ventions. Academy of Management Journal, 59:1725–1753.

Kassinis, G., &Vafeas, N. 2002. Corporate boards andoutsidestakeholders as determinants of environmental litiga-tion. Strategic Management Journal, 23: 399–415.

Kim, J. Y., Howard,M., Cox Pahnke, E., & Boeker,W. 2016.Understanding network formation in strategy re-search: Exponential random graph models. StrategicManagement Journal, 37: 22–44.

King, G., & Zeng, L. 2001. Logistic regression in rare eventsdata. Political Analysis, 9: 137–163.

Knecht, A. B., Burk, W. J., Weesie, J., & Steglich, C. 2011.Friendship and alcohol use in early adolescence: Amultilevel social network approach. Journal of Re-search on Adolescence, 21: 475–487.

Kogut, B., & Zander, U. 1992. Knowledge of the firm,combinative capabilities, and the replication of tech-nology. Organization Science, 3: 383–397.

Kotha, R., George, G., & Srikanth, K. 2013. Bridging themutual knowledge gap: Coordination and the com-mercialization of university science. Academy ofManagement Journal, 56: 498–524.

Kotter, J. P. 1979. Managing external dependence. Acad-emy of Management Review, 4: 87–92.

Lai, R., D’Amour, A., & Fleming, L. 2009. The careers andco-authorshipnetworks ofU.S. patent-holders, since1975. Technical report, Harvard Institute for Quanti-tative Social Science.

Lanjouw, J. O., & Schankerman,M. 2001. Characteristics ofpatent litigation:Awindowoncompetition.TheRandJournal of Economics, 32: 129–151.

Lavie, D., & Rosenkopf, L. 2006. Balancing exploration andexploitation in alliance formation. Academy ofManagement Journal, 49: 797–818.

Lerner, J. 1995. Patenting in the shadow of competitors.The Journal of Law & Economics, 38: 463–495.


Liebeskind, J. P., Oliver, A. L., Zucker, L., & Brewer, M.1996. Social networks, learning, and flexibility:Sourcing scientific knowledge in new biotechnologyfirms. Organization Science, 7: 428–443.

Linck, J. S., Netter, J. M., & Yang, T. 2009. The effects andunintended consequences of the Sarbanes-Oxley Acton the supply and demand for directors. Review ofFinancial Studies, 22: 3287–3328.

Martin, G., Gozubuyuk, R., & Becerra, M. 2015. Interlocksand firm performance: The role of uncertainty in thedirectorate interlock-performance relationship. Stra-tegic Management Journal, 36: 235–253.

Mizruchi, M. S. 1996. What do interlocks do? An analysis,critique, and assessment of research on interlockingdirectorates. Annual Review of Sociology, 22:271–298.

Mizruchi,M. S., & Stearns, L. B. 1988. A longitudinal studyof the formation of interlocking directorates. Admin-istrative Science Quarterly, 33: 194–210.

Mizruchi, M. S., Stearns, L. B., & Marquis, C. 2006. Theconditional nature of embeddedness: A study of bor-rowing by large U.S. firms, 1973–1994. AmericanSociological Review, 71: 310–333.

Nelson, R. R., & Winter, S. G. 1977. In search of usefultheory of innovation. Research Policy, 6: 36–76.

Nelson, R.R., &Winter, S.G. 1982.Anevolutionary theoryof economic change. Cambridge, MA: Harvard Uni-versity Press.

Oriani, R., & Sobrero, M. 2008. Uncertainty and themarketvaluationof R&D within a real options logic.StrategicManagement Journal, 29: 343–361.

Palmer, D. 1983. Broken ties: Interlocking directorates andintercorporate coordination. Administrative ScienceQuarterly, 28: 40–55.

Pfeffer, J. 1972. Size and composition of corporate boardsof directors. Administrative Science Quarterly, 17:218–229.

Pfeffer, J. 1987. A resource dependence perspective oninterorganizational relations. In M. S. Mizruchi &M. Schwartz (Eds.), Intercorporate relations: Thestructural analysis of business: 22–55. Cambridge,UK: Cambridge University Press.

Pfeffer, J. 2003. Introduction to the classic edition. InJ. Pfeffer & G. R. Salancik (Eds.), The external controlof organizations: A resource dependence perspec-tive: xi–xxxiii. Stanford, CA: Stanford UniversityPress.

Pfeffer, J., & Nowak, P. 1976. Joint ventures and in-terorganizational interdependence. AdministrativeScience Quarterly, 21: 398–418.

Pfeffer, J., & Salancik, G. R. 1978. The external control oforganizations: A resource dependence perspective.New York, NY: Harper & Row.

Pfeffer, J., & Salancik, G. R. 2003. The external control oforganizations: A resource dependence perspective.Stanford, CA: Stanford University Press.

Podolny, J. M., & Stuart, T. E. 1995. A role-based ecology oftechnological change. American Journal of Sociol-ogy, 100: 1224–1260.

Powell, W., Koput, K., & Smith-Doerr, L. 1996. In-terorganizational collaboration and the locus of in-novation: Networks of learning in biotechnology.Administrative Science Quarterly, 41: 116–145.

Rao, H., & Sivakumar, K. 1999. Institutional sources ofboundary-spanning structures: The establishment ofinvestor relations departments in the fortune 500 in-dustrials. Organization Science, 10: 27–42.

Ripley, R. M., Snijders, T. A. B., Boda, Z., Voros, A., &Preciado, P. 2015. Manual for Siena version 4.0(version February 23, 2016). Oxford: University ofOxford, Department of Statistics; Nuffield College.http://www.stats.ox.ac.uk/siena/.

Rogan, M., & Greve, H. R. 2014. Resource dependencedynamics: Partner reactions tomergers.OrganizationScience, 26: 239–255.

Rosenberg, N. 1979. Technological interdependence in theAmerican economy. Technology and Culture, 20:25–50.

Roy, R., & Sarkar,M. B. 2016. Knowledge, firm boundaries,and innovation: Mitigating the incumbent’s curseduring radical technological change. Strategic Man-agement Journal, 37: 835–854.

Schilling, M. A. 2002. Technology success and failure inwinner-take-all markets: The impact of learning ori-entation, timing, and network externalities.Academyof Management Journal, 45: 387–398.

Schumpeter, J. A. 1934. The theory of economic devel-opment: An inquiry into profits, capital, credit, in-terest, and the business cycle. Cambridge, MA:Harvard University Press.

Schweinberger, M. 2012. Statistical modelling of networkpanel data: Goodness of fit. British Journal of Math-ematical & Statistical Psychology, 65: 263–281.

Schweinberger, M., & Snijders, T. A. B. 2007. Markovmodels for digraph panel data: Monte Carlo-basedderivative estimation. Computational Statistics &Data Analysis, 51: 4465–4483.

Scotchmer, S. 1991. Standing on the shoulders of giants:Cumulative research and the patent law. The Journalof Economic Perspectives, 5: 29–41.


http://www.stats.ox.ac.uk/siena/

Shropshire, C. 2010. The role of the interlocking directorand board receptivity in the diffusion of practices.Academy of Management Review, 35: 246–264.

Snijders, T. A. B. 2005. Models for longitudinal networkdata. In P. J. Carrington, J. Scott & S.Wasserman (Eds.),Models and methods in social network analysis:215–247. Cambridge: Cambridge University Press.

Snijders, T. A. B., Steglich, C. E. G., Schweinberger, M., &Huisman, M. 2008. Manual for Siena version 3.2.ICS, University of Groningen, Groningen; De-partment of Statistics. Oxford: University of Oxford,Available from http://stat.gamma.rug.nl/snijders/siena.html.

Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. E. G.2010. Introduction to stochastic actor-based modelsfor network dynamics. Social Networks, 32: 44–60.

Soh, P.-H. 2010. Network patterns and competitive ad-vantage before the emergence of a dominant design.Strategic Management Journal, 31: 438–461.

Sorenson, O., & Stuart, T. E. 2001. Syndication networksand the spatial distribution of venture capital in-vestments. American Journal of Sociology, 106:1546–1588.

Spencer, J. W. 2003. Firms’ knowledge-sharing strategiesin the global innovation system: Empirical evidencefrom the flat panel display industry. Strategic Man-agement Journal, 24: 217–233.

Steensma, H. K., Marino, L., Weaver, K. M., & Dickson,P. H. 2000. The influence of national culture on theformation of technology alliances by entrepreneurialfirms. Academy of Management Journal, 43:951–973.

Stuart, T. E. 1998. Network positions and propensities tocollaborate: An investigation of strategic alliance for-mation in a high-technology industry.AdministrativeScience Quarterly, 43: 668–698.

Suarez, F. F., & Utterback, J. M. 1995. Dominant designsand the survival of firms. Strategic ManagementJournal, 16: 415–430.

Sydow, J., Schreyogg, G., & Koch, J. 2009. Organizationalpathdependence:Opening theblackbox.AcademyofManagement Review, 34: 689–709.

Thornton, E., Burrows, P., & Crockett, R. O. 2008. Whena buyout goes bad. BloombergBusiness, April 2:http://www.bloomberg.com/bw/stories/2008-2004-2002/when-a-buyout-goes-bad.

Trajtenberg, M. 1990. A penny for your quotes: Patent ci-tations and the value of innovations. The Rand Jour-nal of Economics, 21: 172–187.

Tripsas, M. 1997. Unraveling the process of creative de-struction: Complementary assets and incumbent sur-vival in the typesetter industry. StrategicManagementJournal, 18: 119–142.

Wang, C., Rodan, S., Fruin, M., & Xu, X. 2014. Knowledgenetworks, collaboration networks, and exploratoryinnovation. Academy of Management Journal, 57:484–514.

Wasserman, S., & Faust, K. 1994.Social network analysis:Methods and applications. Cambridge, England:Cambridge University Press.

Wasserman, S., & Pattison, P. 1996. Logit models and logisticregressions for social networks: I. An introduction toMarkov graphs and p*. Psychometrika, 61: 401–425.

Westphal, J. D., Seidel, M.-D. L., & Stewart, K. J. 2001.Second-order imitation: Uncovering latent effects ofboard network ties. Administrative Science Quar-terly, 46: 717–747.

Withers, M. C., Hillman, A. J., & Cannella, A. A. 2012. Amultidisciplinary review of the director selection lit-erature. Journal of Management, 38: 243–277.

Wolter, C., & Veloso, F. M. 2008. The effects of innovationonvertical structure: Perspectives on transaction costsand competences. Academy of Management Re-view, 33: 586–605.

Wry, T., Cobb, J. A., & Aldrich, H. E. 2013. More thanametaphor:Assessing thehistorical legacyof resourcedependence and its contemporary promise as a theoryof environmental complexity. The Academy ofManagement Annals, 7: 441–488.

Xia, J. 2011. Mutual dependence, partner substitutability,and repeated partnership: The survival of cross-borderalliances.StrategicManagement Journal, 32: 229–253.

Zhou,K.Z., &Li, C. B. 2012.Howknowledge affects radicalinnovation: Knowledge base, market knowledge ac-quisition, and internal knowledge sharing. StrategicManagement Journal, 33: 1090–1102.

Ziedonis, R. H. 2004. Don’t fence me in: Fragmented mar-kets for technology and the patent acquisition strate-gies of firms. Management Science, 50: 804–820.

Zucker, L.G.,Darby,M.R.,&Brewer,M.B. 1999. IntellectualcapitalandthebirthofU.S.biotechnologyenterprises.ResearchDocumentnumberW4653,NationalBureauofEconomic Research, Cambridge, MA.

BIOGRAPHICAL SKETCHES

Michael D. Howard ([email protected]) is an as-sistant professor of management in the Mays BusinessSchool atTexasA&MUniversity.He receivedhisPhD fromthe University of Washington. His research interests in-clude innovation, entrepreneurship, and the analysis ofsocial networks among firms.

Michael C. Withers ([email protected]) is an as-sistant professor of management in the Mays Business


http://stat.gamma.rug.nl/snijders/siena.html

http://stat.gamma.rug.nl/snijders/siena.html

http://www.bloomberg.com/bw/stories/2008-2004-2002/when-a-buyout-goes-bad

http://www.bloomberg.com/bw/stories/2008-2004-2002/when-a-buyout-goes-bad

mailto:[email protected]


School atTexasA&MUniversity.He receivedhis PhD fromArizona State University. His research interests includethe management of resource dependencies, corporategovernance, and director selection and mobility.

LaszloTihanyi ([email protected]) is theRoberts Chair inBusiness and Professor in the Mays Business School atTexas A&M University. He received his PhD from IndianaUniversity. His research interests include corporate gov-ernance in multinational firms, international strategies,and organizational adaptation in emerging economies.

APPENDIX: STOCHASTICACTOR-ORIENTED MODELS

Stochastic Actor-Oriented Models (SAOMs) are a classof models that are focused on characterizing network dy-namics based on observed longitudinal network data,allowing tests of statistical inference (Snijders et al., 2010).They permit the analysis of multiple, simultaneous socialprocesses of network tie evolution at the actor, dyadic, andbroader network levels. These models have been recentlyintroduced in sociology research (Burk et al., 2007;Knecht,Burk, Weesie, & Steglich, 2011), and their usefulness hasbeen noted by researchers in the management field (Kimet al., 2016). SAOMsoffer a solution to shortcomingsofpriorresearch on dynamic tie evolution among network mem-bers, which has relied almost exclusively on statisticalmethods that assume independence between observations.For example, methods such as conventional panel-basedlogistic regression do not consider potential transitiveproperties of network ties: if A is tied to B and B is tied to C,this may lead to tie formation between A and C. Manyscholars have noted problems with the assumption of tieindependence (Gulati, 1995b; Stuart, 1998). In contrast,SAOMs allow us to explicitly model changes in networkstructure, capturing the tendencies of actors in creating,maintaining, and dissolving ties based on local andnetwork-level influences.

The stochastic approach observes sequential changes inthe status of actor-level ties from period to period acrosspanels of the observed network data. The network actor

behaves according to preferences and constraints thatcomprise short-term objectives in the choice ofwhether/how to change its network state (e.g., form newties, abandon existing ties, etc.). Mathematically, this ob-jective function can be represented through the followingexpression (Snijders et al., 2010):

fiðb, xÞ5 +kbkskiðxÞ

The value of the objective function for an actor i is given byfiðb, xÞ. It is based on x, representing the network state interms of both network tie structure and values of actorcovariates, and skiðxÞ, the effects potentially impacting thegoals of actor i in changing its network state. The statisticalparameters associated with the effects are represented bybk . bk 5 0 when the corresponding effect has no influenceon network dynamics. When bk . 0, there is a higherprobability of network evolution moving in the directionwhere the effect is higher, while the reverse is true whenbk , 0.

SAOMs require several key assumptions. First, dynam-ics of tie formation are assumed to occur in discrete wavesof activity. For example, actor characteristics and networkstructures in period 1 are assumed to influence changes inperiod 2. This is a familiar assumption from non-networkpanel survey methods used in strategy research. Changeis also modeled as emerging from a Markov process, inwhich future probabilities of network state depend on themost recent observed structure. This assumes a relativelysteady, continuous flow of network evolution. Actorsare also assumed to control the formation, maintenance,or dissolution of outgoing ties. Finally, tie formationis sequential and not simultaneous. For example, re-ciprocal ties between actors are not assumed to formconcurrently—one tie forms and is then reciprocated bythe counterpart actor.

The computer program, Siena (simulation investigationfor empirical network analysis), provides a convenientplatform for the implementation of SAOMs (Ripley,Snijders, Boda, Voros, & Preciado, 2015). The devel-opers offer a number of useful references (Snijders, 2005),tutorials (Snijders et al., 2010), and analysis guides asso-ciated with the Siena program (Schweinberger, 2012;Schweinberger & Snijders, 2007).



Date post:	24-May-2018
Category:	Documents
Upload:	vuongkhanh
View:	213 times
Download:	0 times

Knowledge Dependence and the Formation of Director...

Documents