A LONGITUDINAL STUDY OF THE INFLUENCE OF ALLIANCE … · (Phelps, Heidl, & Wadhwa, 2010) showed the liter-ature on alliances and firm innovation is limited in at least four important

A LONGITUDINAL STUDY OF THE INFLUENCE OF ALLIANCENETWORK STRUCTURE AND COMPOSITION ON FIRM

EXPLORATORY INNOVATION

COREY C. PHELPSHEC Paris

This study examines the influence of the structure and composition of a firm’s alliancenetwork on its exploratory innovation—innovation embodying knowledge that isnovel relative to the firm’s extant knowledge. A longitudinal investigation of 77telecommunications equipment manufacturers indicated that the technological diver-sity of a firm’s alliance partners increases its exploratory innovation. Further, networkdensity among a firm’s alliance partners strengthens the influence of diversity. Theseresults suggest the benefits of network “closure” (wherein a firm’s partners are alsopartners) and access to diverse information can coexist in an alliance network and thatthese combined benefits increase exploratory innovation.

A core area of research on strategic alliances con-cerns their influence on firm performance (Gulati,1998). Within this domain of inquiry, researchershave often characterized alliances as wellsprings ofinnovation and new capabilities (e.g., Hamel, 1991;Leonard-Barton, 1995). Many studies have shownthe alliance networks in which firms are embeddedcan enhance firm learning and innovation (e.g.,Ahuja, 2000; Shan, Walker, & Kogut, 1994; Smith-Doerr et al., 1999; Soh, 2003). Despite this evi-dence, substantial opportunity exists to expand un-derstanding of how and under what conditionsalliance networks influence firm innovation. A re-view of nearly 40 years of research published in 12leading management and social science journals(Phelps, Heidl, & Wadhwa, 2010) showed the liter-

ature on alliances and firm innovation is limited inat least four important respects.

First, although some research has examined theinfluence of alliance network structure on firm in-novation, the composition of firms in these net-works has received little attention. Network struc-ture refers to the pattern of relationships that existamong a set of actors, and network compositionrefers to the types of actors in a network character-ized in terms of their stable traits, features, or re-source endowments (Wasserman & Faust, 1994).Recent research has recognized that alliance net-work studies have largely ignored network composi-tion and has called for more attention in networkresearch to the heterogeneity of the resources of firmsin networks (Lavie, 2006; Maurer & Ebers, 2006). Thefew studies that have examined both structure andcomposition have focused on the depth of partnertechnological resources and found that they improvea firm’s innovation performance (Baum, Calabrese, &Silverman, 2000; Stuart, 2000). Although dyad-levelresearch has examined the influence of technologicaldifferences between partners on firm innovation(Sampson, 2007), research has largely overlooked theinfluence of network-level technological diversity—the technological differences between a firm and itspartners and among the partners. Such compositionaldiversity is relevant to a current debate in the socialnetwork and alliance literatures.

Second, research has yielded conflicting resultsabout the influence of alliance network structureon firm innovation. Research that examines theinfluence of social networks on creativity and in-novation has stressed the benefits actors derivefrom network structure and explored how thesebenefits, or “structural social capital” (Nahapiet &

This article is based on my doctoral dissertation, whichreceived the Academy of Management’s TIM Division’sBest Dissertation Award and the State Farm CompaniesFoundation Dissertation Award. I thank my dissertationcommittee members, Raghu Garud, Theresa Lant, and J.Myles Shaver, for their advice and guidance. I am gratefulfor comments on drafts of this article by Sanjay Jain, Mel-issa Schilling, J. Myles Shaver, Kevin Steensma, KateStovel, Anu Wadhwa, and Mina Yoo. I thank Simon Rodanfor his help with computing the diversity measure usedhere. I also thank Associate Editor Chet Miller for his me-ticulous feedback and thank the three anonymous review-ers for their comments, all of which greatly contributed tothe improvement of the article. I acknowledge financialsupport from the State Farm Companies Foundation andthe Berkley Center for Entrepreneurship at the Stern Schoolof Business, NYU. Finally, I am grateful for the extraordi-nary database development and programming skills ofRalph Heidl and Tim Nali. All errors are my responsibility.

� Academy of Management Journal2010, Vol. 53, No. 4, 890–913.

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Ghoshal, 1998), influence knowledge creation. Inparticular, the configuration of an actor’s set ofdirect ties (i.e., the actor’s “egocentric networkstructure”) has received some attention. This re-search has focused on triadic closure (i.e., whetheran actor’s partners are partners), but two competingperspectives exist, each with different causal mech-anisms linking network structure to innovation.The argument of one view is that disconnectednetworks increase creativity and innovation be-cause they provide actors with timely access todiverse information (Burt, 1992, 2004).1 An alter-native view suggests dense networks, in which tri-ads are closed and “structural holes” (unconnectedpartners) are absent, provide social capital becausesuch structures generate trust, reciprocity norms,and a shared identity, which increase cooperationand knowledge sharing (Coleman, 1988; Portes,1998). Research has found support for both views,yielding conflicting results. Although studies havefound structural holes in a firm’s network enhanceits knowledge creation (Hargadon & Sutton, 1997;McEvily & Zaheer, 1999), other research has sug-gested that network closure improves knowledgetransfer and innovation (Ahuja, 2000; Dyer & No-beoka, 2000; Schilling & Phelps, 2007).

One plausible reason for these conflicting resultsis that most studies have examined the influence ofnetwork structure and largely overlooked networkcomposition. An examination of network composi-tion may help resolve these conflicting results andlead to a better understanding of how alliance net-works influence firm innovation. Another possibleexplanation is that different studies have examineddifferent types of ties, different institutional con-texts, and different outcome variables. It is unlikelya particular network structure is universally bene-ficial (see Adler & Kwon, 2002). Research has sug-gested that the value of open versus closed net-works for innovation and creativity is contingenton the type of task (Hansen, 1999), type of tie(Ahuja, 2000), and particular institutional environ-ment (Owen-Smith & Powell, 2004). In contrast,network structure may act as a contingency vari-able and moderate the influence of network com-position on firm innovation. Moreover, this effectmay depend on the type of learning and innovationactors pursue. Both of these contingencies have

been largely unexplored in prior research and areexamined in this study.

A third limitation of research on alliances andfirm innovation concerns an often-used, yet largelyunexamined, assumption about the benefits ofstructural holes. Although a principal benefit at-tributed to structural holes is timely access to di-verse information (Burt, 1992), structural holes areneither a necessary nor a sufficient condition forsuch access (Reagans, Zuckerman, & McEvily,2004). The informational benefits contacts providecan be directly observed by examining the extent towhich they specialize in different domains ofknowledge (Reagans & McEvily, 2003; Rodan &Galunic, 2004). Observing differences in competen-cies also allows a finer-grained measure of diversitythan simply counting structural holes. Becausecompetencies are stable and durable properties offirms (Patel & Pavitt, 1997), they are a composi-tional variable. Ties to partners with dissimilarknowledge stocks provide a firm with access todiverse information and know-how, independentof the structure of its local network. Thus, the so-cial control benefits of network closure and accessto diverse information and know-how can coexist.Research on interfirm alliances (Ahuja, 2000) andinterpersonal networks (Rodan & Galunic, 2004)has shown network density and knowledge diver-sity are empirically distinct. However, alliance re-search has not examined the independent and in-teractive effects of network structure and networkknowledge diversity on firm innovation.

A final limitation of research on alliance net-works and firm innovation is that it largely ignoresthe novelty of the knowledge created and embodiedin the innovations measured. Instead, studies havefocused on the amount of innovation indicated bysurvey items or counts of new products and pat-ents. This approach implicitly rests on the as-sumption that innovations are similar in theirknowledge content. Although research has sug-gested firms typically search for innovative solu-tions to problems in the domains of their existingexpertise (“local search”) and produce “exploi-tive” innovations that represent incremental im-provements to their prior innovative efforts (e.g.,Dosi, 1988; Martin & Mitchell, 1998), some re-search has shown firms vary in the scope of theirsearch and the exploratory content of their inno-vations (Ahuja & Lampert, 2001; Rosenkopf &Nerkar, 2001). A few studies have examined howorganizational design decisions influence ex-ploratory knowledge creation (Jansen, Van DenBosch, & Volberda, 2006; Sigelkow & Rivkin,2005). However, with the exception of some qual-itative case study research (Dittrich, Duysters, &

1 Burt (1992) also argued structural holes allow actorsfreedom from the normative expectations of others in anetwork, yet research into the influence of network struc-ture on innovation and creativity has stressed informationalbenefits, rather than control benefits, as the primary causalmotor (e.g., Ahuja, 2000; Burt, 2004; Obstfeld, 2005).

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de Man, 2007; Gilsing & Noteboom, 2006), re-search has generally ignored the effects of alli-ance network structure and network diversity onexploratory knowledge creation.

The purpose of this study is to address theselimitations. I do so by examining the influence ofthe structure and composition of a firm’s networkof horizontal technology alliances on its explor-atory innovation. I focus on horizontal technologyalliances for theoretical clarity. Exploratory inno-vation is the creation of technological knowledgethat is novel relative to a firm’s extant knowledgestock. Research has often portrayed exploration as aprocess (March, 1991), yet the manifestation of thisprocess can be observed by examining the explor-atory content of a firm’s innovations (Benner &Tushman, 2002; Rosenkopf & Nerkar, 2001). Ex-ploratory innovations embody knowledge that dif-fers from knowledge used by the firm in prior in-novations and shows the firm has broadened itstechnical competence (Greve, 2007; Rosenkopf &Nerkar, 2001).2

Understanding the origins of exploratory innova-tion is an important endeavor. Because the resultsof exploration (versus exploitation) typically takelonger to realize, are more variable, and producelower average returns, organizations generally pur-sue exploitative innovation at the expense of ex-plorative innovation (March, 1991). They face afundamental challenge: although exploitation im-proves an organization’s short-term performance,exploration increases its long-term adaptability andsurvival (Levinthal & March, 1993). This formula-tion does not suggest that exploratory innovation ispreferred over incremental innovation, only that abalance is necessary (March, 1991). The strong in-centives to pursue exploitation at the expense ofexploration raise the question of how and whenfirms are able to explore effectively. Research hasdocumented the propensity of firms to pursue localsearch and exploitative innovation (e.g., Dosi,1988; Helfat, 1994), but much less is known abouthow and when firms overcome this predispositionand develop exploratory innovations. Explainingthe production of exploratory innovations shouldprovide a better understanding of how organiza-tions are able to thrive and survive.

I derive two predictions about the effect of hori-zontal technology alliances on firm exploratory in-novation. First, in highlighting the role of networkcomposition, I draw on the recombinatory searchliterature (e.g., Fleming, 2001) and examine thebenefits and costs of increasing network technolog-ical diversity for exploratory innovation. I predictnetwork diversity has an inverted U-shaped effecton firm exploratory innovation performance. Sec-ond, building on interfirm learning and networkresearch, I argue that the extent to which a firm’spartners are densely interconnected generates trustand reciprocity, which enhance the benefits of net-work diversity and mitigate some of its costs. Ipredict the density of a firm’s alliance networkpositively moderates the effect of network diversityon the firm’s exploratory innovation performance.

In the empirical work reported here, I testedthese predictions on a panel of 77 leading commu-nications equipment manufacturers during 1987–97 and found partial, yet robust, support for bothhypotheses. A positive linear effect of network di-versity emerged, rather than a curvilinear effect,and a positive linear interaction between diversityand density, rather than a curvilinear interaction.This study contributes to the alliance and innova-tion literatures by addressing significant gaps inresearch on the influence of alliance networks onfirm innovation. This is the first study of which Iam aware that investigates the influence of alliancenetwork structure and composition on firm explor-atory innovation. The results show the technologi-cal diversity in a firm’s alliance network and thedensity of the network increase exploratory inno-vation, independently and in combination. The re-sults also suggest the presence of structural holes ina firm’s network is not a necessary condition forproviding the firm with access to diverse informa-tion. The extent to which an actor’s network iscomposed of alters with diverse knowledge basesprovides it access to diverse information, indepen-dent of network structure. The benefits of networkclosure and access to diverse information andknow-how can coexist in a firm’s alliance network,and combining the two increases the firm’s explor-atory innovation. Because I find network diversitybegets diverse innovations (Kauffman, 1995), theresults suggest that dense networks populated bydiverse actors generate more, rather than less, di-verse knowledge.

THEORY AND HYPOTHESES

To understand when alliances influence a firm’sexploratory innovation, I build on two complemen-tary theoretical bases: recombinatory search and

2 As He and Wong stated, “Exploration versus exploi-tation should be used with reference to a firm itself andits existing capabilities, resources and processes, not to acompetitor or at the industry level” (2004: 485). Thus,“one can only view acts of exploration or exploitationrelative to a particular actor’s vantage point” (Adner &Levinthal, 2008: 49).

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social capital. The recombinatory search literaturecasts innovation as a problem-solving process inwhich solutions to valuable problems are discov-ered via search (Dosi, 1988). Search processes lead-ing to the creation of new knowledge typically in-volve the novel recombination of existing elementsof knowledge, problems, or solutions (Fleming,2001; Nelson & Winter, 1982) or reconfiguring theways knowledge elements are linked (Henderson &Clark, 1990). Search is uncertain, costly, and guidedby prior experience (Dosi, 1988). Over time, feedbackfrom past search efforts becomes embodied in organ-izational routines, which efficiently guide currentsearch efforts (Nelson & Winter, 1982).

Firms create knowledge by engaging in local anddistant search (March, 1991). Local search, whichis synonymous with exploitation, produces recom-binations of familiar and well-known knowledgeelements and is often the preferred mode of search(March, 1991; Stuart & Podolny, 1996). In contrast,distant search, or exploration, involves recombina-tions of novel, unfamiliar knowledge and involveshigher costs and uncertainty (March, 1991). Althoughdistant search can be less efficient and less certainthan local search, it increases the variance of searchand the potential for highly novel recombinations(Fleming, 2001; Levinthal & March, 1981).

Innovation search research has primarily focusedon where firms search for solutions (i.e., local ver-sus distant); the interfirm learning literature, on theother hand, has emphasized how firms search. Ac-cording to this research, interfirm relationships area mechanism for search and a medium of knowl-edge transfer (Ingram, 2002). Because knowledgeis widely and heterogeneously distributed (vonHayek, 1945), the exchange of knowledge is neces-sary for recombination (Nahapiet & Ghoshal, 1998).Yet the nature of knowledge involved in innovationposes challenges to exchange. Technical innova-tion involves tacit and socially embedded knowl-edge (Dosi, 1988). Technology is knowledge em-bedded in communities of practitioners (Layton,1974) who develop tacit understandings of how tosolve problems related to its use and reproduction(von Hippel, 1988). Such knowledge is also storedin organizational routines (Nelson & Winter, 1982).The specialized, tacit, and embedded nature oftechnical knowledge makes market trading for itsubject to severe exchange problems (Teece, 1992).Firms that can identify potentially useful elementsof technological knowledge, conceive of how theseelements can be fruitfully combined, and effectivelyaccess and assimilate this knowledge increase theirpotential for knowledge creation (Galunic & Rodan,1998). Strategic alliances are important in each ofthese aspects of successful recombination.

Strategic alliances are a means of accessingknowledge a firm does not have and can be aneffective medium of knowledge transfer and inte-gration (Hamel, 1991). Alliances provide a firmwith direct and repeatable access to its partners’organizational routines, which reduces its ambigu-ity about a partner’s knowledge and increases theefficacy of its transfer and assimilation (Jensen &Szulanski, 2007). Because of the increased socialinteraction and enhanced incentive alignment andmonitoring features they provide, alliances are in-stitutions better suited than market transactions forthe repeated exchange of tacit, routine-embeddedknowledge (Teece, 1992).

Although alliances provide access to externalknowledge, they do not guarantee its effective de-tection, transfer, and assimilation. These processes,and thus the odds of successful recombination, areinfluenced by the incentives partners have to coop-erate and share knowledge with each other (Hamel,1991). Because the risk of opportunism is pro-nounced in horizontal technology alliances, effec-tive cooperation and knowledge sharing are diffi-cult to achieve (Gulati & Singh, 1998). Allianceresearch has typically emphasized the role of for-mal governance mechanisms—such as detailedcontracts, the use of equity as a “hostage,” and jointventure structures—in curbing opportunism andincreasing cooperation (e.g., Kogut, 1988; Mowery,Oxley, & Silverman, 1996; Sampson, 2007). Otherresearch has suggested that mutual trust and reci-procity norms between partners provide effectiveand efficient informal governance (Dyer & Singh,1998; Kale, Singh, & Perlmutter, 2000).

Trust and reciprocity serve as social controlmechanisms that mitigate opportunism and safe-guard exchange in alliances (Dyer & Singh, 1998).As such, they are forms of social capital, becausethey represent resources that are instrumentallyvaluable for, and appropriable by, partners in asocial exchange relationship (Coleman, 1988). Theextent to which social capital exists in a firm’snetwork of alliances can increase the firm’s accessto its partners’ knowledge, the motivation of itspartners to transfer knowledge, and the efficiencyof knowledge exchange and transfer (Inkpen &Tsang, 2005), resulting in more successful recom-binations (Galunic & Rodan, 1998). In the next twosections, I build on the recombinatory search lit-erature and research on interfirm networks andsocial capital to develop predictions about howand when network technological diversity andnetwork density influence a firm’s exploratoryinnovation performance.

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Network Technological Diversity

Diversity refers to the extent to which a systemconsists of uniquely different elements, the fre-quency distribution of these elements, and the de-gree of difference among the elements (Stirling,2007). Thus, I define alliance network technologi-cal diversity as the extent to which the technologiespursued by a firm’s alliance partners are differentfrom one another and from those of the focal firm.Although network diversity provides benefits for afirm’s exploratory innovation efforts, it also posessignificant costs. Diversity affects the relative nov-elty of knowledge available in a network and theease with which a firm can recognize, assimilate,and utilize this knowledge.

Increasing network diversity increases the rela-tive novelty of the knowledge a firm can access.Because exploratory innovations embody relativelynovel knowledge, a necessary condition for firmexploratory innovation is access to dissimilarknowledge (Greve, 2007; Jansen et al., 2006). Diver-sity increases the number and variety of possiblecombinations and the potential for highly novelsolutions (Fleming, 2001). The “value of variance”(Mezias & Glynn, 1993) in distant search is thatthough it increases failures, it also increases thenumber of highly novel solutions (Levinthal &March, 1981). In contrast, individuals and organi-zations that exploit established competences intheir innovative problem-solving efforts typicallyexperience more certain and immediate returns,but produce mostly incrementally innovative solu-tions (Audia & Goncalo, 2007; Dosi, 1988). Search-ing diverse knowledge domains challenges existingcognitive structures, including premises and be-liefs about cause-effect relationships (Duncker,1945), which can promote new associations andlead to highly novel insights and solutions (Simon-ton, 1999). By searching diverse and novel do-mains, firms can develop multiple conceptualiza-tions of problems and solutions and applysolutions from one domain to problems in another(Hargadon & Sutton, 1997). Diverse knowledgesources also provide firms with access to diverseproblem-solving heuristics (Page, 2007), which canincrease the exploratory content of new combina-tions of knowledge (Audia & Goncalo, 2007). Fi-nally, searching diverse, nonredundant knowledgecan stimulate intensive experimentation with newcombinations, leading to highly novel innovations(Ahuja & Lampert, 2001).

Network diversity also influences a firm’s relativeabsorptive capacity. As the technological distance be-tween partners increases, their ability to recognize,assimilate, and apply each other’s knowledge de-

clines (Lane & Lubatkin, 1998), increasing the costsof recombinatory innovation (Weitzman, 1998). Afirm must expend greater effort and resources tounderstand and integrate dissimilar knowledge(Cohen & Levinthal, 1990). This can manifest incostly, excessive, and inconclusive experimenta-tion (Ahuja & Lampert, 2001). A firm’s cognitivecapacity constraints and its relative inexperiencewith dissimilar knowledge components will limitits ability to comprehend increasingly complex in-teractions among these components (Fleming &Sorenson, 2001). Moreover, integrating novelknowledge from dissimilar sources often requireschanging existing patterns of communication andsocial exchange, which is difficult in establishedorganizations (Kogut & Zander, 1992). Attemptingto assimilate and integrate highly diverse knowl-edge components can lead to information overload,confusion, and diseconomies of scale in innovationefforts (Ahuja & Lampert, 2001). Thus, as a firm’snetwork diversity increases, its costs of absorbingand utilizing this knowledge greatly increase.

Given these benefits and costs of network diver-sity, I expect it to exhibit a curvilinear effect on afirm’s exploratory innovation. At low levels of di-versity, a firm has a high degree of relative absorp-tive capacity in its portfolio of partners, but theknowledge to which it has access provides littlenovelty. At high levels of network diversity, ab-sorptive capacity costs are likely to outweigh thebenefits of highly novel knowledge. Although in-creasing diversity exponentially increases opportu-nities for novel recombinations (Fleming, 2001), anorganization is greatly constrained in its ability toprocess an abundance of potentially novel recom-binations into usable innovations (Weitzman,1998). Research has shown that as knowledge com-ponents become more diverse, the chance of theirrecombination into useful innovations declines,with excessive diversity reducing innovation(Fleming & Sorenson, 2001). In contrast, at a mod-erate level of network diversity a firm’s exploratoryinnovation efforts benefit from a balance of accessto a moderate degree of novel knowledge and mod-erately efficient relative absorptive capacity. Thus,some degree of diversity is valuable for exploratoryinnovation; too much can be detrimental.

Hypothesis 1. The technological diversity ina firm’s alliance network has an inverted U-shaped relationship with the firm’s subsequentdegree of exploratory innovation.

Network Density

Although an alliance provides access to a part-ner’s knowledge, it does not guarantee the effective


detection, transfer, and assimilation of this knowl-edge (Hamel, 1991). The tacit and embedded natureof technological knowledge makes it difficult forpartners to detect, transfer, and assimilate (Teece,1992), reducing its potential for successful recom-bination (Galunic & Rodan, 1998). Increasing net-work diversity worsens this problem, since a firm’sabsorptive capacity in relation to its partners willdecline (Lane & Lubatkin, 1998). Greater diversityreduces the odds partners share a common under-standing of technical issues, a language for discuss-ing them, and an approach to codifying knowledge(Cohen & Levinthal, 1990). The exchange hazardsin horizontal technology alliances compound theseproblems. Because partners have incentives tocompete, the risk of opportunism is elevated. Suchalliances are also inherently uncertain and poselarge measurement and monitoring problems(Pisano, 1989). Partners are at risk of involuntaryknowledge leakage, the withholding of effort andresources needed to achieve alliance goals, misrep-resentation of newly discovered knowledge, andchallenges in transferring tacit knowledge devel-oped during the relationships (Gulati & Singh,1998). Network diversity also compounds theseproblems. Increasing diversity increases the rela-tive novelty of knowledge and the variety of tacitknowledge, thereby increasing the amount ofunique tacit knowledge. High novelty and tacitnessincrease partner uncertainty and contractual haz-ards (Pisano, 1989). Technological diversity in-creases coordination problems and the potential forcostly contractual renegotiations (Sampson, 2004).These exchange hazards can reduce cooperationand knowledge sharing, hindering a firm’s recom-bination efforts.

The extent to which a firm’s alliance partners aredensely interconnected mitigates some of the costsand amplifies some of the benefits of increasingnetwork diversity, thus positively moderating itseffect on exploratory innovation. Dense networksfacilitate the production of trust and reciprocityamong networked firms, which decrease exchangehazards in alliances, increase cooperation amongpartners, and mitigate absorptive capacity prob-lems. These problems become more challenging,and thus more important to resolve, as networkdiversity grows.

Network density promotes trust and reciprocitybetween partners because they share commonthird-party partners. Dense networks allow firms tolearn about current and prospective partnersthrough common third parties, reducing informa-tion asymmetries among firms and increasing their“knowledge-based trust” in one another (Gulati,Nohria, & Zaheer, 2000). Network closure also pro-

motes trust by increasing the costs of opportunism(Coleman, 1988). Because a firm’s behavior is morevisible in a dense network, an act of opportunismcan damage its reputation, jeopardizing its existingalliances and reducing future alliance opportuni-ties (Gulati, 1998). Because the costs of opportun-ism can outweigh the benefits, firms will refrainfrom such behavior. Thus, dense networks alsogenerate “enforceable” or “deterrence-based” trust(Kreps, 1990; Raubb & Weesie, 1990). Research hasprovided empirical support for these arguments(Gulati & Sytch, 2008; Holm, Eriksson, & Johanson,1999; Husted, 1994; Robinson & Stuart, 2007;Rooks, Raub, Selten, & Tazelaar, 2000; Uzzi, 1996).Network density also generates reciprocity exchangesin which partners share privileged resources becausethey expect recipients will repay them with some-thing of equivalent value (Coleman, 1988). A firmcan encourage reciprocity between two of its part-ners by transferring reciprocal obligations one part-ner owes to the firm to the other partner (Uzzi,1997). Dense networks also promote reciprocity byprotecting relationships from opportunism, in-creasing actors’ confidence that obligations for re-payment will eventually be met (Coleman, 1988).

The trust and reciprocity benefits of dense net-works can mitigate some of the exchange hazardsand challenges to effective interfirm cooperationassociated with greater network diversity. Trustand reciprocity generated by network density act asinformal safeguards of dyadic exchange, supple-menting formal alliance governance mechanisms(Powell, 1990). Given the challenges of formal gov-ernance in horizontal technology alliances amongtechnologically diverse firms, informal governancebecomes more important in mitigating opportun-ism and promoting cooperation as diversity in-creases. Informal governance reduces the threat ofopportunism and increases each partner’s motiva-tion to cooperate and share resources (Dyer &Singh, 1998). Trust reduces the extent to whichalliance partners protect knowledge, increases theirwillingness to share knowledge, and increases in-terfirm learning and knowledge creation (Kale etal., 2000; Larson, 1992). Reciprocity norms rein-force this motivation to share, since firms can beconfident partners will reciprocate (Dyer & No-beoka, 2000). As a result, the information and know-how shared will be less distorted, richer, and ofhigher quality (Dyer & Nobeoka, 2000; Uzzi, 1997).Research has suggested dense interfirm networks arebetter for transferring and integrating complex andtacit knowledge than networks with structural holes(Dyer & Nobeoka, 2000; Kogut, 2000).

Alliance network density also reduces absorptivecapacity problems related to growing network di-

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versity. Network closure promotes intense socialinteraction, experimentation, joint problem solv-ing, and triangulation, which enhance a firm’s abil-ity to absorb and apply increasingly diverse partnerknowledge. The trust and reciprocity benefits ofnetwork closure promote intense interactionamong personnel from partnered firms (Larson,1992), which improves the detection and transfer oftacit and embedded knowledge (Zander & Kogut,1995). Intense interaction can also lead to the cre-ation of partner-specific knowledge-sharing rou-tines that facilitate knowledge transfer (Lane & Lu-batkin, 1998). The social capital produced in densealliance networks encourages such relation-spe-cific investments (Walker, Kogut, & Shan, 1997).

The trust and reciprocity benefits of network clo-sure also increase partners’ joint problem-solvingefforts and stimulate experimentation with differ-ent knowledge combinations, improving knowl-edge detection and transfer from diverse partners(Dyer & Nobeoka, 2000; Uzzi, 1997). Trust and rec-iprocity can also increase a partner’s motivation to“teach” (Szulanski, 1996), which is more importantfor student firms as partner diversity increases,since they should find it easier to learn unaidedfrom similar partners (Szulanski, 1996). Alliancepartners also provide alternative interpretations oftechnical problems and solutions, allowing a firmto compare, contrast, and triangulate these perspec-tives (Nonaka, 1994). Alternative perspectives dif-fuse rapidly in dense networks (Smith-Doerr &Powell, 2005) and are more valuable when partnersare diverse, since a variety of perspectives in-creases the chances some will be useful in a firm’srecombination efforts (Nonaka, 1994). Finally, therapid flow of information in dense networks pro-vides firms with more opportunities to share andexpand their understanding of technical issues andcan help establish a shared mode of discourse(Smith-Doerr & Powell, 2005), allowing diversepartners to more efficiently communicate with andlearn from one another (Kogut & Zander, 1996).

In sum, increasing network density improves afirm’s ability to absorb and utilize knowledge frommore diverse partners. I expect these benefits ofnetwork density to moderate the curvilinear effectof network diversity on firm exploratory innova-tion in four distinct ways. First, increasing densitywill increase the slope (i.e., strength) of the positiverelationship between diversity and exploratory in-novation (i.e., the positive slope to the left of thepeak of the curve). Second, increasing density willincrease the amplitude of the curvilinear effect ofdiversity. That is, as density increases, the maxi-mum value of exploratory innovation achieved willincrease. Third, the value of diversity that maxi-

mizes exploratory innovation will increase as den-sity increases, shifting the peak of the curve tohigher values of diversity. Finally, increasing den-sity will reduce the slope of the negative relation-ship between diversity and exploratory innovation.That is, after the effect of diversity turns negative,increasing density will dampen the negative effectof diversity on exploratory innovation.

Hypothesis 2. The density of a firm’s alliancenetwork moderates the curvilinear relationshipbetween network diversity and exploratory in-novation in such a fashion that increasing den-sity will: (a) increase the slope of the positiveeffect of diversity, (b) increase the amplitude ofthe effect of diversity, (c) increase the valueof diversity that maximizes exploratory inno-vation, and (d) reduce the negative effect ofdiversity.

METHODOLOGY

Sample and Data

The research setting for this study was the globaltelecommunications equipment industry (SIC 366).Firms in this industry produce and market hard-ware and software that enable the transmission,switching, and reception of voice, images, and dataover both short and long distances using digital,analog, wire line, and wireless technology. I chosethis setting for two reasons. First, during the 1980sand 1990s this industry experienced significantchanges in technology and competition, resultingin a growing use of technology alliances by incum-bents (Amesse, Latour, Rebolledo, & Seguin-Du-lude, 2004). Second, since I used patent data, Ichose to study an industry in which firms routinelyand systematically patent their inventions (Hage-doorn & Cloodt, 2003; Levin, Klevorick, Nelson, &Winter, 1987).

To minimize survivor bias and right censoring, Ilimited the study period to 1987–97. I limited thesample frame to public companies to ensure theavailability and reliability of financial data. I lim-ited the sample to the firms in the industry with thelargest sales because complete and accurate alli-ance data are more available for industry leadersthan for smaller firms (Gulati, 1995). To minimizesurvivor bias, I identified the top-selling firms inthe industry at the beginning of the study periodrather than the end because numerous mergers, re-structurings, and failures occurred during the studyperiod (Amesse et al., 2004). To minimize the influ-ence of right censoring, I ended the study period in1997 to allow sufficient time for the (non)approval ofpatent applications that sample firms made during


the period (also see footnote 4). Following prescrip-tions for establishing network boundaries in empiri-cal research (Laumann, Marsden, & Prensky, 1983), Irestricted the network to both firms and alliances thatfocused on the telecommunications equipment in-dustry. Recent alliance network research has usedsimilar network construction criteria (Rowley, Beh-rens, & Krackhardt, 2000; Schilling & Phelps, 2007).These sampling criteria resulted in a sample of 77firms headquartered in 13 countries.

I used patent data to measure technologicalknowledge because patents are valid and robustindicators of knowledge creation (Trajtenberg,1987). Knowledge is instantiated in inventions, andpatents are measures of novel inventions externallyvalidated through the patent examination process(Griliches, 1990). A patent application represents apositive expectation by an inventor of the eco-nomic significance of his or her invention, sincegetting such protection is costly (Griliches, 1990).Patents measure a codifiable portion of a firm’stechnical knowledge, yet they correlate with mea-sures that incorporate tacit knowledge (Brouwer &Kleinknecht, 1999). For these various reasons, pat-ents are a reliable and valid measure of innovationin the telecom equipment industry (Hagedoorn &Cloodt, 2003).

Information on U.S. patents was obtained fromDelphion. Using patents from a single countrymaintains consistency, reliability, and comparabil-ity across firms (Griliches, 1990). U.S. patents are agood data source because of the rigor and proce-dural fairness used in granting them, the large in-centives firms have to obtain patent protection inthe world’s largest market for high-tech products,the high quality of services provided by the U.S.Patent and Trademark Office (USPTO), and the rep-utation of the United States for providing effectiveintellectual property protection (Pavitt, 1988; Riv-ette, 1993). I used the application date to assign agranted patent to a firm because this date closelycaptures the timing of knowledge creation (Grili-ches, 1990). Because patents are often assigned tosubsidiaries, I carefully aggregated patents to thefirm level.3

The collaboration data were obtained from mul-

tiple sources. I initially collected alliance data fromthe SDC Alliance Database. Although this databaseprovided substantial content, it had many limita-tions. I overcame these limitations through system-atic archival research using annual reports, 10Kand 20F filings, Moody’s Manuals, Factiva, Lexis-Nexis, and Dialog. These last three databases indexthe historical full texts of hundreds of businesspublications from all regions of the world and in-clude articles translated to English from their orig-inal languages, and non-English publications. Iconducted broad keyword searches to identify allinstances of interfirm cooperation involving thesample firms. Individuals fluent in the respectivelanguage read non-English articles and reports,identified instances of interfirm cooperation, andtranslated the documents into English. I recordedonly collaborations that could be confirmed in mul-tiple sources. Around 1,200 annual reports and Se-curities and Exchange Commission (SEC) filingsand over 180,000 electronic articles were exam-ined, and over 8,500 relevant news stories wereprinted out. Overall, the data set from which thisstudy draws includes 7,904 alliances and 1,967acquisitions initiated during 1980-96. I reviewedevery record from the SDC data and corrected du-plicate entries and other errors and omissions usingsecondary sources.

Firm attribute data were collected from Compus-tat, annual reports, SEC filings, the Japan CompanyHandbook, Worldscope, and Global Vantage.

Measurement: Dependent Variable

Exploratory innovation. Exploratory innovationis the creation of technological knowledge by a firmthat is novel relative to its existing knowledge stock(Benner & Tushman, 2002; Rosenkopf & Nerkar,2001). Following prior research (Benner & Tush-man, 2002, Katila & Ahuja, 2002; Rosenkopf &Nerkar, 2001), I measured exploratory innovationusing patent citations. I began with the list of U.S.patent classes that corresponded to the telecommu-nications equipment industry at the beginning ofthe sample period (see Table 1). I assessed theexploratory innovation of firm i in year t by classi-fying and tabulating all citations in the firm’s tele-communications equipment patents applied for inyear t (and eventually granted). I traced each cita-tion to determine if the firm had used the samecitation or if the citation was to a patent developedby the firm during the seven years before the focalyear. I used a seven-year window because organi-zational memory in high-tech firms is imperfect,causing the value of knowledge to depreciate rap-idly over time (Argote, 1999) and creating signifi-

3 I identified all divisions, subsidiaries, and joint ven-tures of each sample firm (using Who Owns Whom andthe Directory of Corporate Affiliations) as of 1980. I thentraced each firm’s history to account for name changes,division names, divestments, acquisitions, and joint ven-tures and obtained information on the timing of theseevents. This procedure yielded a master list of entitiesthat I used to identify all patents belonging to samplefirms for the period of study.

2010 897Phelps

cant problems for intertemporal knowledge transfer(Nerkar, 2003). Although prior research (e.g., Katila& Ahuja, 2002) has used a 5-year window to assessexploration, I chose a 7-year window because themedian age of cited patents in telecom technologiesis about 6.5 years (Hicks, Breitzman, Olivastro, &Hamilton, 2001). Using this window, I classifiedeach citation as “new” or “used.” I computed thevariable as the result of dividing new citations bytotal citations (exploratory innovationsit � new cita-tionsit/total citationsit.) Because this formula mea-sures a share of new citations, rather than their fullcount, it captures a firm’s propensity to produce ex-ploratory innovations, independent of firm scale.4

The extent to which a firm draws on elements ofknowledge (e.g., patent citations) it has previouslyused reflects its practice of local search and exploi-tation of its extant knowledge stock. The extent towhich it uses citations with which it has no expe-rience is indicative of distant search and explor-atory innovation (Benner & Tushman, 2002). Thismeasure ranges from pure exploitation (no explo-ration) at the low end to pure exploration (no ex-ploitation) at the high end. It is consistent withresearch that has conceptualized and measured ex-ploitation and exploration, or local and distantsearch, as the ends of a continuum5 (Benner &

4 Two aspects of the patent data used to construct thismeasure merit discussion. First, during the period ofstudy, the USPTO did not publish patent applications. Apatent application date was only observable when apatent was granted. Because I observed patents usingtheir date of application and because there is a delaybetween the date of application for a patent and its even-tual granting, I may not have observed all patents appliedfor in a particular year and eventually granted, becausethe USPTO had not rendered a decision by the time Icollected my patent data. The influence of such a right-censoring bias, caused by the delay between patent ap-plication and issuance, is likely to be negligible in thisstudy. Around 99 percent of all applications are re-viewed within five years of application (Hall et al., 2001),which is the period between the end of the sample (1997)and the last year of patent data collection (2002). Second,patent examiners often add citations to patent applications(Alcacer & Gittelman, 2006), which suggests applicant firmsare not necessarily aware of all cited patents. Third-partycitations often manifest as noise in the measurement ofpatent-based variables (Jaffe, Trajtenberg, & Fogarty, 2002).Noise in the measurement of a dependent variable in-creases standard errors and reduces the likelihood of find-ing statistically significant effects (Gujarati, 1995).

5 Though I focus on one domain of search (i.e., tech-nological knowledge), firms search multiple domains,such as customer and geographic space (Gupta, Smith, &Shalley, 2006; Sidhu et al., 2007). Portraying exploitation

TABLE 1Primary U.S. Patent Classes Used to Represent

Telecommunications Equipmenta

Class Number Title

178 Telegraphy179 (discontinued) Telephony329 Demodulators332 Modulators333 Wave transmission lines and networks334 Tuners340 Communications: electrical341 Coded data generation or conversion342 Communications: directive radio wave

systems & devices343 Communications: radio wave antennas348 Television358 Facsimile and static presentation

processing359 Optics: systems (including

communication) and elements367 Communications, electrical: acoustic

wave systems and devices370 Multiplex communications375 Pulse or digital communications379 Telephonic communications381 Electrical audio signal processing

systems and devices382 Image analysis385 Optical waveguides455 Telecommunications725 Interactive video distribution systems

a Because patents are classified by technological and func-tional principles, they do not map easily to product-based in-dustrial definitions such as SIC codes (Griliches, 1990). That is,there is not a one-to-one mapping between primary patentclasses and industries. Multiple patent classes are used in asingle industry, and a single patent class can be used in multipleindustries. Consequently, to identify the areas of technology thatconstitute telecommunications equipment, I needed to developa concordance between primary patent classes and the three-digit SIC code 366, “communications equipment.” To do so, Iutilized both Silverman’s (1996) concordance method and con-cordances provided by experts. I used the concordance for com-munications equipment developed by scholars at Science andTechnology Policy Research (SPRU), a unit of Sussex Universityin the United Kingdom, and the concordance developed by theCommunity of Science Inc., an internet company that providescollaborative tools and services for research scientists and engi-neers. I identified the primary patent classes common to both ofthese expert-based concordances as a baseline and then com-pared this list of classes with a rank-ordered list delineating thedegree to which specific international patent classes (IPCs) wereassociated with SIC 366 as the industry of manufacture as of1988. To make this comparison, I used the USPTO’s USPC-IPCconcordance. The primary classes listed in the baseline concor-dance were associated with the highest ranked IPC classes as-sociated with U.S. SIC 366 (except for class 725, which did notexist in the late 1980s). This indicated that the 22 primaryclasses used in this study to represent communications equip-ment technology in this table are most frequently associatedwith SIC 366.


Tushman, 2002; Greve, 2007; Sidhu, Commandeur,& Volberda, 2007).

As a robustness check, I applied an alternativemeasure of exploratory innovation from prior re-search (e.g., Ahuja & Lampert, 2001; McGrath &Nerkar, 2004). I computed this measure as the num-ber of new three-digit technology classes in whichfirm i patented in year t, classifying a technologyclass as new if the firm had not patented in thatclass in the past seven years. The USPTO assignspatents to about 450 technology classes, with eachclass demarcating an area of technology. The extentto which a firm enters new technological domainsis indicative of exploration (Ahuja & Lampert,2001; McGrath & Nerkar, 2004). This measure wasbroader than the citation-based measure since ittook into account all technology classes in which afirm might patent.

Measurement: Explanatory Variables

Following prior research (e.g., Ahuja, 2000; Stu-art, 2000), I sampled alliances involving technologydevelopment or exchange because my phenome-non of interest and theory concerned the transferand creation of technological knowledge. I ex-cluded unilateral licensing deals and alliancesformed for the sole purpose of marketing, distribu-tion, or manufacturing.

Network technological diversity. To measurenetwork technological diversity, I employed Rodanand Galunic’s (2004) measure of knowledge hetero-geneity. This measure incorporates information aboutthe knowledge distance between a focal actor andeach of its partners and the distances among the part-ners. I began at the dyad level and measured thetechnological distance between pairs of firms usingJaffe’s (1986) index. For each firm-year, I measuredthe distribution of a firm’s patents across primarypatent classes. Following Sampson (2007), I used amoving four-year window to establish a firm’s patent-ing profile. This distribution located a firm in a mul-tidimensional technology space, captured by a K-di-mensional vector (fi � [fi1 . . . fik], where fik representsthe fraction of firm i’s patents that are in patentclass k). This approach rests on an assumption thatthe distribution of a firm’s patents across classesreflects the distribution of its technical knowledge

(Jaffe, 1986). The technological distance, d, be-tween firms i and j in year t was calculated as:

dijt � 1 � � �k � 1

K

fikfjk�� k � 1

K

fik2� 1/ 2� �

k � 1

K

fjk2� 1/ 2� .

This measure was bounded between 0 (completesimilarity) and 1 (maximum diversity) and sym-metric for the two firms. I used these pairwisedistance values to construct annual distance matri-ces, Dt, which reflected the technological distancesbetween all possible pairs of sample firms.

Next, I computed the uniqueness of the knowl-edge of each partner j in firm i’s alliance network inyear t. The uniqueness of firm j is a function of theuniqueness of its partners, k, and firm j’s distancefrom them. Following Rodan and Galunic (2004), Idefined the uniqueness of firm j, uj, as:

�uj � �k

djk � uk.

The uniqueness of each firm is found in the solu-tion of the eigen equation(�U � DU), where U is aneigenvector of D and � is its associated eigenvalue.The elements of U are the uniqueness values foreach firm, and D is the matrix of pairwise techno-logical distances. I measured the technological di-versity available to firm i in its (ego) network ofalliance partners in year t as:

Network technological diversityit �1N �

j � 1

N

dij�uj,

where dij is partner j’s distance from i and �uj is j’suniqueness score computed for i’s N partners. The1/N term compensates for the fact that lambda in-creases linearly with network size. This measureincreases linearly with the distances among i andits partners (Rodan & Galunic, 2004).

Network density. To measure ego network den-sity, I constructed annual adjacency matrices forthe period 1987–96 that indicated the presence of atechnology alliance, in existence at the end of afocal year, between all possible undirected pair-wise combinations of sample firms. An alliancewith more than 2 firms entered the adjacency ma-trix as separate dyadic combinations of all firms inthe alliance. Of all sample alliances, 89 percentinvolved only 2 firms, and the average alliance had2.38 firms. Because alliances often endure longerthan one year, constructing adjacency matrices us-ing only alliances formed in a focal year wouldhave understated the true connectivity of the net-work. Consequently, I collected alliance data foreach firm beginning in 1980 and researched each

and exploration as ends of a continuum in one domain ofsearch does not preclude the possibility that firms cansimultaneously achieve high levels of both exploitationand exploration in multiple domains (Gupta et al., 2006).A universal argument about the mutual exclusivity orindependence of exploitation and exploration may beimpossible (Gupta et al., 2006).

2010 899Phelps

alliance to identify its date of dissolution or con-tinuance through the last sample year.6

Ego network density was the percentage of allpossible ties among an ego’s alters that had beenformed (Scott, 1991). Ego networks in which afirm’s alliance partners are themselves allied implyhigher values of density. To test the robustness ofthe effect of density, I substituted Burt’s (1992)measures of efficiency and then constraint into al-ternative specifications. The Appendix presentsthese specifications. Both efficiency and constraintare measures of triadic closure (see Borgatti [1997]for a comparison). Figure 1 presents an example ofa sample firm’s ego network, specifically, Motoro-la’s network of technology alliances at the end of1992, and lists the values for the density, effi-ciency, and constraint of this network. Algebraicexplanations of each measure are also shown.

Control Variables

To minimize alternative explanations and isolatethe marginal effects of the explanatory variables, Icontrolled for several firm- and alliance-level vari-ables whose influence on exploratory innovationmight be confounded with the explanatory vari-ables. Given the firm-level analysis used in thisstudy, I aggregated alliance-level observations tothe firm level. I used multiple-year moving win-dows of differing lengths to compute five controlvariables. These window lengths ranged from fourto seven years and differed by control variable. Ibased the choice of window length for each controlvariable on prior research. Using alternative win-

dow lengths (�1 year) for these control variablesdid not substantively change the results of the ex-planatory variables presented in Table 2.

Network size. More alliance partners may pro-vide a firm with access to greater technical diver-sity. Moreover, measures of ego network densityare sensitive to network size, making network sizean important control variable (Friedkin, 1981). Icomputed network size as the natural logarithm ofthe number of telecom technology alliance partnersmaintained by firm i in year t.

Alliance duration. Alliance longevity can lead togreater interfirm trust (Gulati, 1995), stronger reci-procity norms (Larson, 1992) and relation-specificroutines (Levinthal & Fichman, 1988), increasinginterfirm learning (Simontin, 1999). I measured al-liance duration as the average number of years firmi had participated in its existing telecom technol-ogy alliances at the end of year t (see footnote 6).

Repeated ties. Prior ties between firms can in-crease interfirm trust (Gulati, 1995), the develop-ment of relation-specific learning heuristics, andinterfirm learning (Lane & Lubatkin, 1998). Follow-ing Gulati and Gargiulo (1999), I calculated re-peated ties as the average number of alliances firmi had formed with its current group of alliancepartners in the five years prior to year t.

Joint venture. Research has suggested equityjoint ventures are superior governance mechanismsfor interfirm learning and knowledge transfer(Kogut, 1988; Mowery et al., 1996). I computed thevariable as the proportion of firm i’s telecom tech-nology alliances governed by equity joint venturesin year t.

International alliance. International alliancesprovide access to diverse knowledge (Rosenkopf &Almeida, 2003), but they experience greater coor-dination and communication problems and cul-tural conflicts than domestic alliances, and thisexperience diminishes interfirm learning (Lyles &Salk, 1996). I measured this variable as the fractionof firm i’s telecom technology alliances in year tinvolving foreign firms.

Partners’ market overlap. Because partnerstend to protect their knowledge when they areproduct-market competitors, overlaps in partners’markets can impede interfirm knowledge transfer(Dutta & Weiss, 1997). I computed market overlapas the proportion of firm i’s portfolio of telecomtechnology alliances in year t having partners withthe same primary four-digit SIC code as firm i.

Firm sales. Firm size can have both negative andpositive effects on firm innovation (Teece, 1992). Icontrolled for firm size using the natural log of sales(in millions of U.S. dollars) for firm i in year t.

6 I researched each alliance using the sources de-scribed previously. I also contacted company personnelto identify dissolution dates, which proved very useful inidentifying the termination or ongoing status of jointventures (JVs). For nearly all JVs, I was able to identifythe months they were ended or their ongoing status at theend of the sample period. For each remaining JV, I as-sumed it existed until the end of the last year in which itwas documented or until the end of the year after the yearit was founded, whichever was later. For non-JV alli-ances, I recorded termination on the basis of specifiedtenure, if mentioned in the archival sources, or an-nouncement of dissolution (either from archival sourcesor company contact). In cases in which I could not es-tablish precise dissolution, I followed Ahuja (2000) andpresumed an alliance to exist until the end of the lastyear in which it was documented or until the end of theyear after the year it was founded, whichever was later. Iperformed a t-test of the difference in mean durationbetween alliances with formal dissolution announce-ments and those with assumed dissolution dates andfound no significant difference.


FIGURE 1Motorola’s 1992 Ego Network Structure of Technology Alliancesa

a In the figure, Motorola is the focal actor, or ego. Below are the values of ego network density, efficiency and constraint for Motorola’s1992 technology alliance network and an explanation of each measure. Burt (1992) provides a detailed explanation of the measures ofefficiency and constraint and Borgatti (1997) provides a comparison of the three measures.

The values for the density, efficiency, and constraint of this network and their algebraic computation are as follows:Density � 26.67%

Ego network densityi � ��j�qxjq��N�N � 1��2�� 100, j � q,

where xjq represents the relative strength of the tie between alter j and alter q, and N represents the number of alters to which ego i is connected.Because I treated alliances as either present or absent (i.e., they do not vary in terms of strength), all values of xjq were set to 1 if a relationshipexisted and 0 otherwise. The term [N(N � 1)] was divided by 2 to reflect that alliances are undirected ties. Variable range, 0–100%.

Efficiency � 0.75

Ego network efficiencyi � ��j�1 � �qpiqmiq��N, j � q,

where piq is the proportion of i’s ties invested in the relationship with q, mjq is the marginal strength of the relationship between alter j and alterq (as I used binary data, all values of mjq were set to 1 if a tie existed and 0 otherwise), and N represented the number of alliance partners to whichfocal firm was connected. This measure could vary from 0 to 1, with higher values indicative of greater efficiency (i.e., structural holes).

Constraint � 0.15

Ego network constrainti � �j�pij � �qpiqpqj�2

, q � i, j,

where pij is the proportion of i’s ties invested in the relationship with j, piq is the proportion of i’s ties invested in the relationship withq, and pqj is the proportional strength of alter q’s relationship with alter j. This measure can vary from of 0 to 1, with higher valuesindicative of greater constraint (i.e., fewer structural holes).

2010 901Phelps

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Firm current ratio. The availability of slackresources can increase exploratory search (Singh,1986) and lead to greater innovative performance(Nohria & Gulati, 1996). I controlled for the un-absorbed slack resources of firm i in year t usingits current ratio (current assets/current liabilities)(Singh, 1986).

Firm R&D intensity. A firm’s R&D expendituresare investments in knowledge creation (Griliches,1990) and contribute to its ability to absorb extra-mural knowledge (Cohen & Levinthal, 1990). I mea-sured R&D intensity by dividing firm i’s R&D ex-penses by its sales in year t.

Firm patent stock. The more patents a firm has,the more patents and references it can cite; a largepatent stock could thus negatively affect the pri-mary measure of exploratory innovation here. Afirm’s patent stock also reflects the depth of itstechnological resources and absorptive capacity(Silverman, 1999). I controlled for the number offirm i’s patents obtained in the four years prior tothe end of year t.

Firm age. As firms age, they tend to exploit theirexisting technological competencies rather than ex-plore new and unfamiliar technologies (Sorensen &Stuart, 2000). I operationalized firm age as thenumber of years from the date of founding of firm ito year t.

Firm alliance experience. Alliance experienceenhances the collaborative capability of a firm,which facilitates interfirm knowledge transfer(Sampson, 2005). I controlled for the number of alltypes of alliances formed by firm i in the sevenyears before year t, divided by its sales in year t.

Firm technological diversity. Technologicallydiverse firms may be more innovative because ofdiverse internal knowledge flows (Garcia-Vega,2006), and they may be more able to absorb externalknowledge (Cohen & Levinthal, 1990). I measuredfirm i’s diversity in year t using a modified Herfin-dahl index (Hall, 2002):

Technological diversityit � �1 � �j � 1

J �Njit

Nit� 2�

�Nit

Nit � 1,

where Nit is the number of patents obtained by firmi in the past four years. Njit is the number of patentsin technology class j in firm i’s four-year patentstock. This variable could range from 0 to 1 (max-imum diversity).

Firm acquisitions. Acquisitions can enhance ac-quirer innovation (Ahuja & Katila, 2001). Telecom

equipment firms often use both acquisitions andalliances to source knowledge (Amesse et al.,2004). I controlled for the number of telecom equip-ment acquisitions (i.e., those in which the targetcompany’s primary SIC code was 366) made byfirm i during the four years prior to and includingyear t.

U.S.-Canada/Europe/Asia. I used dummies de-noting the regional origin of a firm to control forregional effects. “U.S.-Canada” was coded 1 if afirm was headquartered in the United States orCanada. “Europe” was coded 1 if the firm washeadquartered in Europe. Asia was the omittedcategory.

Model Specification and Estimation

The dependent variable was a proportion andpresented several challenges to linear regression(Gujarati, 1995). Thus, I used three alternative mod-eling approaches. First, I estimated the models withexploratory innovation as the dependent variableusing panel linear regression and robust standarderrors. Following common econometric practice(Greene, 1997), I also estimated models with a log-odds transformation of exploratory innovation.7 Fi-nally, I estimated models using a generalized esti-mating equation (GEE) approach in which Ispecified a probit link function and an exchange-able correlation matrix and computed robust errors(Papke & Wooldridge, 2005). As a robustness check,I compared the results from these alternative spec-ifications. I included year dummies to control forperiod effects, such as differences in macroeco-nomic conditions or industry technological oppor-tunity. Either firm-specific fixed or random effectscan be used to control for unobserved firm hetero-geneity (Greene, 1997), such as differences in mo-tivations to pursue, and abilities to develop, explor-atory innovations. Because the use of randomeffects relies on an assumption that errors and re-gressors are uncorrelated, I used a Hausman (1978)test to choose between fixed and random effects. Ialso checked for first-order serial autocorrelation inthe errors. I lagged all independent variables oneyear, which reduced concerns of reverse causalityand avoided simultaneity.

7 The transformed variable is as follows: ln(explor-atory innovation/1 – exploratory innovation). Becausethe transformation is undefined when exploratory inno-vation is equal to 0 or 1, I recoded these values as follows:0 � 0.0001 and 1 � 0.9999.

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RESULTS

Table 2 reports descriptive statistics and correla-tions. The panel was unbalanced and consisted of77 firms and 707 firm-year observations. Table 3presents the results of the panel regression analysisused to test the hypotheses. I report the results foruntransformed exploratory innovation for ease ofinterpretation. The results using a logit transforma-tion and those from GEE estimation are consistentwith those reported in Table 3. I estimated models1–7 using firm random effects for three reasons: (1)significant unobserved heterogeneity was present,(2) Hausman specification tests were not signifi-cant, supporting the use of random effects, and (3)significant serial correlation was not present. Hu-ber-White (or “sandwich”) robust standard errors

are reported, and all significance levels are for two-tailed tests. Multicollinearity does not seem to haveunduly influenced the regression results becausethe average variance inflation factor (VIF) for eachmodel and the VIFs for all variables were below therule-of-thumb value of ten (Gujarati, 1995).

Hypothesis 1 predicts an inverted U-shaped ef-fect of network technological diversity on firm ex-ploratory innovation. Models 2–6 in Table 3 pro-vide partial support for this hypothesis. In each ofthese models, network technological diversityit �1

exhibited a positive and significant effect on ex-ploratory innovation. However, the squared termwas not significant in any model in which it wasentered. Thus, although I found evidence of a pos-itive linear effect of network diversity, I did not

TABLE 3Results of Random-Effects Panel Linear Regression Analysis Predicting Firm Exploratory Innovationa

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Constant 0.85** (0.06) 0.89** (0.07) 0.89** (0.07) 0.89** (0.07) 0.86** (0.07) 0.85** (0.07)Network sizeb �0.03** (0.01) �0.05** (0.01) �0.05** (0.01) �0.04** (0.01) �0.04** (0.01) �0.04** (0.01)Alliance duration �0.01 (0.01) �0.01 (0.01) �0.01 (0.01) �0.01 (0.01) �0.01 (0.01) �0.01 (0.01)Repeated ties 0.01 (0.02) 0.01 (0.02) 0.02 (0.02) 0.01 (0.02) 0.02 (0.02) 0.01 (0.02)Joint venture 0.08 (0.06) 0.08 (0.06) 0.08 (0.06) 0.07 (0.06) 0.05 (0.06) 0.05 (0.06)International alliance �0.09* (0.04) �0.08* (0.04) �0.08* (0.04) �0.08* (0.04) �0.07* (0.04) �0.08* (0.04)Partners’ market overlap �0.04 (0.04) �0.03 (0.04) �0.03 (0.04) �0.02 (0.04) �0.02 (0.04) �0.01 (0.04)Firm salesb �0.02* (0.01) �0.03* (0.01) �0.03* (0.01) �0.02* (0.01) 0.00 (0.01) 0.00 (0.01)Firm current ratio 0.00 (0.01) 0.00 (0.01) 0.00 (0.01) 0.00 (0.01) 0.00 (0.01) 0.00 (0.01)Firm R&D intensity �0.08 (0.08) �0.08 (0.08) �0.08 (0.08) �0.08 (0.08) �0.07 (0.08) �0.06 (0.08)Firm patent stock/1,000 �0.004** (0.00) �0.002** (0.00) �0.002** (0.00) �0.002** (0.00) �0.003** (0.00) �0.003** (0.00)Firm age 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00)Firm alliance

experience�0.01 (0.03) �0.02 (0.03) �0.02 (0.03) �0.02 (0.03) �0.02 (0.03) �0.02 (0.03)

Firm technologicaldiversity

�0.05 (0.04) �0.05 (0.04) �0.05 (0.04) �0.05 (0.04) �0.05 (0.04) �0.05 (0.04)

Firm acquisitions 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00)U.S.-Canada �0.01 (0.02) �0.01 (0.02) �0.01 (0.02) �0.01 (0.02) �0.01 (0.02) �0.01 (0.02)Europe 0.01 (0.03) 0.00 (0.03) 0.00 (0.03) 0.00 (0.03) 0.01 (0.03) 0.00 (0.03)Network technological

diversity0.06* (0.03) 0.06* (0.03) 0.06† (0.03) 0.07* (0.04) 0.08* (0.04)

Network technologicaldiversity squared

0.00 (0.04) 0.00 (0.04) 0.05 (0.04) 0.06 (0.09)

Network density 0.05* (0.02) 0.10* (0.04) 0.07 (0.05)Network technological

diversity � density0.46** (0.13) 0.53** (0.14)

Network technologicaldiversity squared �density

0.48 (0.36)

Year dummies included Yes Yes Yes Yes Yes Yes

R2 0.13 0.14 0.14 0.16 0.17 0.18Wald �2 (df) 4.67** (1) 4.66* (2) 6.18* (3) 9.94** (4) 11.71** (5)

a n(firms) � 77; n(observations) � 707. Huber-White robust standard errors are in parentheses.b Logarithm.

† p � .10* p � .05

** p � .01Two-tailed tests.


find evidence of a curvilinear effect. Hypothesis 2predicts network density strengthens the effect ofnetwork technological diversity on exploratory in-novation. Models 5–6 show the interaction had asignificant, positive effect on exploratory innova-tion, supporting Hypothesis 2. The interaction ofnetwork diversity squared and network densitywas not significant (model 6). Although not pre-dicted, network density had a positive and signifi-cant effect on exploratory innovation, independentof diversity (models 4–5). The Wald statistics at thebottom of Table 3 indicate models 2–6 providesignificant improvement in fit relative to model 1. Iconstructed each test for incremental improve-ment in fit relative to the baseline model, becausemaking it relative to the previous model wouldhave provided the same information as the sig-nificance level of the newly entered variable,since each explanatory variable was enteredalone (Gujarati, 1995). The Appendix contains anassessment of the robustness of the results andalternative explanations.

DISCUSSION

This study was motivated by important limita-tions of research on alliance networks and firminnovation. This literature has largely ignored thepotential influence of network composition, partic-ularly the technological diversity of a firm’s part-ners. This research also draws on seemingly incom-patible theoretical arguments and has producedconflicting empirical results regarding the influ-ence of network structure. These conflicts stemfrom an assumption that a firm’s access to diverseinformation and the innovation benefits of networkclosure are mutually exclusive. In part because ofthis assumption, potential complementarities be-tween network structure and composition havebeen largely unexamined. Finally, research on alli-ance networks and firm innovation has focused onthe volume of firm innovation, with little consid-eration of its exploratory content.

This study addressed these limitations by exam-ining the influence of the composition and struc-ture of a firm’s network of horizontal technologyalliances on its degree of exploratory innovation.The theoretical framework suggested network com-position and structure play different, yet comple-mentary, roles in exploratory innovation. Regard-ing network composition, I drew on research onrecombinatory search to predict that the technolog-ical diversity in a firm’s alliance network has aninverted U-shaped relationship with its exploratoryinnovation. Although increasing diversity in-creases the number, variety, and novelty of poten-

tial innovative combinations, excessive diversityimpairs a firm’s ability to recognize and utilizeknowledge components in its network, reducing itsability to produce exploratory innovations. Regard-ing network structure, I built on research on inter-firm networks and interfirm learning and arguedthe density of a firm’s horizontal alliance networkincreases its ability to access, mobilize, and inte-grate its partners’ knowledge, thus increasing itsability to benefit from technologically diverse part-ners. In so doing, this study moved beyond thedyadic perspective typically used in interfirmlearning research (cf. Tiwana, 2008).

The results are mostly consistent with the pre-dictions of the theoretical framework. I predicted acurvilinear effect of network diversity, yet I foundevidence of a positive linear effect on exploratoryinnovation. I speculate on this result below. I alsofound the density of a firm’s network of horizontaltechnology alliances strengthened the effect of di-versity. These results do not seem to be biased byendogeneity and are robust to the use of many firm-and alliance-level controls, alternative specifica-tions and estimation routines, firm fixed and ran-dom effects, and the use of alternative measures.

Although I predicted an inverted U-shaped effectof network technological diversity, I found a posi-tive, linear effect. There are at least three possibleexplanations for this result. First, sample firms mayhave avoided alliances with excessively diversepartners. Indeed, Mowery et al. (1998) found thatfirms typically avoid forming alliances with highlydissimilar partners. Without a sufficient number ofexcessively diverse networks, only a linear rela-tionship can be observed. Although this argumentsuggests the parameter estimates for network diver-sity and its square might be biased by sample self-selection, I tested for such endogeneity and foundnone (see the Appendix). Second, Rosenkopf andAlmeida (2003) found that once a firm had formedan alliance, it was just as likely to learn from tech-nologically dissimilar firms as from similar firms.They theorized firms typically make the necessaryinvestments in interfirm learning mechanisms tolearn effectively from highly diverse partners. Ifsample firms typically made such investments,then they would have been able to mitigate, to someextent, the absorptive capacity problems associatedwith increasingly diverse partners. Finally, increas-ing technological distance among a firm’s partnersmay have increased their willingness to shareknowledge with the focal firm because they wereless concerned their knowledge would leak to ri-vals via a common partner. When a firm’s networkconsists of partners with similar and thus substi-tutable knowledge stocks, competitive concerns

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can lead them to withhold information and knowl-edge from a common partner to prevent its leakageto rivals via this common intermediary (Khanna,Gulati, & Nohria, 1998).

This study has important implications for re-search and practice. First, this study contributes toa debate in the literature concerning the networkstructure of social capital by suggesting that re-search has overemphasized the informational ben-efits of structural holes for firm innovation. Theprior research assumption has been that structuralholes increase an actor’s timely access to diverseinformation. Because structural holes and networkclosure are inversely related, this argument impliesthe informational benefits of structural holes mustcome at the expense of the benefits of networkclosure, and vice versa. Prior conflicting findingsabout the effect of structural holes on firm innova-tion may be influenced by a confounding of thestructural holes effect with an unobserved compo-sitional effect of partner knowledge diversity. Thisstudy suggests the extent to which an actor’s net-work is composed of alters with diverse knowledgebases will provide it access to informational diver-sity, independent of network structure. The bene-fits of network closure and access to diverse infor-mation and know-how can coexist in a firm’salliance network, and the combination of the twoenhances its exploratory innovation. This findingcoincides with the results of a recent longitudinalqualitative study of interfirm networks. In theirexamination of six biotechnology firms, Maurerand Ebers (2006) found firms with dense networksof partners with diverse resources experiencedgreater growth and development.

Second, this study contributes to the innovationsearch literature. Much of this literature stressesthe proclivity of firms to practice local search. Lit-tle research explores how firms are able to over-come the inertial tendencies of local search. The re-sults of this study suggest having access to diverseknowledge is important. This finding reinforces andcomplements the results of recent alliance-level re-search, which shows partner technological diversityaffects the rate of firm innovation (Sampson, 2007).While Sampson (2007) also found that the use ofequity joint venture, a formal alliance governancemechanism, positively moderated the influence ofpartner dissimilarity on firm innovation, my resultssuggest informal governance provided by networkclosure positively moderated the influence of net-work-level diversity on firm exploratory innova-tion performance. Research has shown alliancesenhance firm innovation performance, but it is dif-ficult to establish from these past studies whetherfirms expanded their technical competencies in the

process. The findings of this study suggest alliancescan spur exploratory innovation when they provideaccess to technologically diverse partners that aredensely connected to one another.

Finally, the results of this study have managerialimplications. The findings confirm alliances canimprove a firm’s development of exploratory inno-vations. The theory and results point to the benefitsof forming alliances with technologically diversepartners in densely connected networks. Thus,managers should attend to the structure of the alli-ance networks in which their firms are embedded,because these structures have implications for firmperformance. Although technology alliance part-ners are often selected based on their technologicalcapabilities (Stuart, 1998), the results of this studysuggest a firm’s ability to learn from technologi-cally diverse partners depends on the degree ofnetwork closure around these relationships. Man-agers should evaluate how their choices aboutforming new alliances and ending existing relation-ships will affect the structure of their networks.Moving from the dyad level of analysis to the net-work level can sensitize managers to the impor-tance of understanding how social structure influ-ences firm performance (Gulati, 1998).

The results and contributions of this studyshould be considered in light of its limitations.First, although I emphasized the benefits of denseand diverse alliance networks for firm innovation, Idid not consider their long-term costs. Researchsuggests network density reduces the diversity ofinformation available in a network over time (Lazer& Friedman, 2007). Dense links provide redundantpaths to the same information sources. Soon every-one in the network comes to have the same infor-mation (Burt, 1992). Over time, this homogeneitywould harm innovation. This argument impliesthat the diversity of information in a network isfixed and results from the diversity of informationpossessed by actors when the network was formed.Thus, the only way to inject novel information intoa network is to add connections to new actors who,as a function of their ties to others outside the focalnetwork, can provide such novelty (Burt, 1992;Granovetter, 1973). Access to diverse informationis determined solely by the connective structure ofties among actors (Obstfeld, 2005).

These are unrealistic assumptions. Not only doesthis argument assume actors are equally and easilyable to absorb or imitate the information they donot initially possess, it also rules out the possibilityof recombinant innovation. Given some degree ofheterogeneity among actors in the information andknowledge they possess, the sharing and diffusionof these resources provides the potential for their


novel recombination into new knowledge that didnot previously exist (Fleming, 2001). If innovationis a process of the recombination of existing knowl-edge, then innovations actually increase the poten-tial for subsequent innovations. In short, recombi-nations beget more recombinations (Fleming,2001). From this perspective, a network establishedwith some degree of diversity in the informationand knowledge actors possess will facilitate thedevelopment of even more diverse information andknowledge. Thus, diversity begets diversity (Kauff-man, 1995: 291). Moreover, as the results of thisstudy suggest, network density can facilitate thisprocess. Rather than driving out diversity, densenetworks that begin with specialized and thereforediverse actors may generate more, rather than less,diversity. Although a detailed investigation of thisissue was beyond the scope of this study, it repre-sents an important topic for future research.

Next, because I used patents to assess exploratoryinnovation, the measure may not capture all of afirm’s exploratory innovations. If firms systemati-cally patent explorative knowledge for unobservedreasons, parameter estimates may be biased. I at-tempted to control for this potential source of biasusing control variables and firm effects. Addition-ally, firms may patent knowledge in anticipation ofentering alliances because of concerns about futureleakage of this knowledge to partners (Brouwer &Kleinknecht, 1999). Exploratory inventions tend tohave a greater impact on subsequent technologicaldevelopment (Rosenkopf & Nerkar, 2001) and maytherefore be of greater economic value (Narin,Noma, & Perry, 1987). Thus, firms may patent ex-ploratory inventions before entering alliances toappropriate their greater economic value. The useof a one-year lag between collaboration and patent-ing and the use of firm effects reduces the likeli-hood of such a bias.

Another possible limitation is that an alliancesurvivor bias may have influenced the results. Ifsample firms formed alliances with the intent ofexploratory learning and if successful alliances sur-vived, then observed alliances will be those thatyielded the greatest exploratory benefit. Such aself-selection bias is unlikely in this study. First,because I have time-varying data on alliances and Iobserve alliance formation and dissolution, mydata include both successful and unsuccessful alli-ances. Second, research shows firms often exit al-liances before they yield knowledge transfer bene-fits (Deeds & Rothaermel, 2003) and often maintainalliances that negatively affect interfirm knowl-edge transfer (Gomes-Casseres, Hagedoorn, &Jaffe, 2006). Third, firms enter technology alli-ances for reasons other than technological explora-

tion (Hagedoorn, 1993). Finally, if alliances that arebeneficial for exploration tend to survive, I wouldexpect a positive effect of the number of alliancesmaintained by a firm on its exploratory innovation.I do not observe such an effect.

Finally, the archival data used in this study can-not provide direct evidence of the causal processesand mechanisms that I hypothesized. Although myhypothesis concerning network density relied onan established and empirically validated argumentthat density promotes trust and reciprocity, mydata did not allow me to observe trust and reciproc-ity among personnel involved in the sample alli-ances. The results are consistent with theoreticalexpectations, yet a better understanding of the mi-crosociological foundations that underlie the ob-served effects of alliance network structure andcomposition is needed to validate the causal infer-ences of this study. In particular, longitudinal quali-tative research should explore how interorganization-al and interpersonal networks interact to producesocial capital and how this social capital influencesknowledge transfer and innovation.

Conclusion

Because firms have strong incentives to pursueexploitation at the expense of exploration, thequestion of how and when firms are able to exploreeffectively is fundamental to understanding howorganizations adapt, thrive, and survive. As Moranand Ghoshal concluded, “An organization that isnot adequately enabling and motivating new possi-bilities is more likely to witness its own decline”(1999: 410). The results of this study reinforce the“relational view” of firm resource creation and ad-vantage (Dyer & Singh, 1998) by helping to identifythe conditions under which alliances enable a firmto create exploratory technological innovations thatcan provide it with the technological foundationsfor new commercial possibilities. The results sug-gest the benefits of network closure and access todiverse information can coexist in a firm’s alliancenetwork and the combination of the two increasesexploratory innovation.

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APPENDIX

Alternative Explanations and Robustness Checks

I considered several alternative explanations and as-sessed the robustness of the results. First, I removed thetime-invariant variables and used firm fixed-effects. Theresults were similar to those obtained using random ef-fects, which is consistent with the insignificant Hausmantests (1978) mentioned in the main text. Next, I consid-ered the potential endogeneity of network structure andnetwork diversity. The formation and dissolution of alli-ances reflect choices made by firms. These choices maybe based on expectations of the exploration-enhancingbenefits of alliances. This introduces the possibility of anunobserved sample self-selection process causing an en-dogeneity bias. Network structure may, however, be ex-ogenous for a few reasons. Firms form technology alli-ances for reasons other than exploratory innovation(Hagedoorn, 1993) and do not easily or quickly alter theiralliances to optimize their networks for particular objec-tives (Maurer & Ebers, 2006). Thus, at any point in time,alliance networks are not necessarily structured to max-imize exploratory innovation and are, at least weakly,exogenous. Last, the structure of a firm’s alliance net-work is beyond the sole control and influence of any onefirm in the network and is therefore not a firm choicevariable. Although network diversity may change slowlybecause of inertia in a firm’s alliance relationships andthus may not be optimized for exploratory innovation ata given point in time, the level of diversity in a firm’s egonetwork is largely under its control.

Because endogeneity is an empirical question, I testedfor the presence of deleterious endogeneity related toboth network density and network diversity. I used Da-vidson and MacKinnon’s test (1993), as implemented bythe “dmexogxt” procedure in Stata 10. This test com-pares the estimated coefficient for the assumed endoge-nous regressor (e.g., density or network diversity) ob-tained from ordinary least square (OLS) fixed-effectsregression with the estimate obtained from a two-stageinstrumental variables fixed-effects regression. The nullhypothesis is that OLS fixed effects yields a consistentparameter estimate. This procedure requires a valid in-strumental variable for the two-stage estimator so that thesecond-stage estimates can be identified. I used firmtechnological diversity to instrument for network densityand network diversity in separate regressions because itwas not significantly correlated with exploratory innova-


tion but was correlated with density and network diver-sity. Neither the endogeneity test associated with net-work density nor that associated with network diversitywas significant. Thus, the parameter estimates for thesevariables in Table 3 do not appear to be unduly influ-enced by endogeneity.

I performed additional unreported analyses to assessthe robustness of my findings. First, I experimented withalternative specifications by removing insignificant vari-ables and then removing all control variables. The resultsrelated to the three explanatory variables were robust tothese alternative specifications. Second, I estimated thefull model using a GEE approach in which I specified aprobit link function and an exchangeable working corre-lation matrix and computed robust standard errors(Papke & Wooldridge, 2005). Results from this analysisfor the three explanatory variables were consistent withthose reported in Table 3. Third, I substituted Burt’s(1992) measures of network efficiency and constraint forthe density measure discussed above. The results ob-tained using these alternative measures of ego networkclosure were statistically stronger but otherwise consis-

tent with those reported in Table 3. Finally, I used thealternative measure of exploratory innovation discussedin the main text. Because this variable was a count andtook on only nonnegative integer values, I estimated thefull model with negative binomial panel regression, us-ing year dummies and firm random effects (Greene,1997). The results were consistent with those reported inTable 3. Overall, the results of the various robustnessanalyses converged and provided added support for bothhypotheses.

Corey Phelps ([email protected]) is an associate professor ofstrategy and business policy at HEC Paris. He completedhis Ph.D. in strategic management at the Stern School ofBusiness, New York University. Dr. Phelps’s current re-search is focused on understanding how companies learnfrom extramural sources of knowledge.

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