Partner Geographic and Organizational Proximity and the Innovative Performance of Knowledge-Creating Alliances

Partner Geographic and OrganizationalProximity and the Innovative Performance

of Knowledge-Creating Alliances

Antonio Capaldo1 and Antonio Messeni Petruzzelli2

1S.E.GEST.A. Department of Management, Catholic University of the Sacred Heart, Milan and Rome, Italy2Department of Mechanics, Mathematics, and Management, Politecnico di Bari, Bari, Italy

We adopt a knowledge-based perspective to investigate the individual and interaction effects of partner geo-graphic and organizational proximity on the innovative performance of knowledge-creating alliances. Our econo-metric analysis on a sample of 1,515 interfirm dyadic knowledge-creating R&D alliances shows that bothgeographic distance between allied firms and their affiliation with the same business group negatively affect thealliance innovative performance. However, when jointly considered, the two examined partner characteristicspositively moderate each other’s effect on alliance innovative performance, so revealing a complementary effecton it. We argue that, while the existence of group ties between geographically distant organizations reduces thenegative influence of geographic distance on the partners’ ability to integrate their knowledge within the alliance,collaborating with remote partners weakens the negative influence of the existence of group ties between partnerson knowledge diversity in alliances. We conclude that geographic distance and organizational proximity arecontingent upon one another in their effect on the innovative performance of knowledge-creating alliances and thatdistance (proximity) in one dimension can be bridged (overcome) by proximity (distance) in another dimension.

Keywords: strategic R&D alliances; proximity; partner selection; joint patents

Introduction

Recent resource-based approaches to strategic manage-ment have pointed out that firm resources are the funda-mental source of competitive advantage (Barney, 1991;Peteraf, 1993). In particular, scholars who have advanceda knowledge-based view of strategy (Eisenhardt andSantos, 2002) maintain that knowledge is the most stra-tegically important resource (Grant, 1996). A majorreason for this is that knowledge is the primary input toinnovation, which in turn has become of the utmostimportance for firms in order to quickly adapt to, andco-evolve with, increasingly dynamic environments(Teece, 1986; Rothaermel and Deeds, 2004). Advocatesof the ‘recombinant perspective’ (Schumpeter, 1934;Henderson and Clark, 1990) have argued that innovationresults from combining several different pieces of knowl-edge. As environmental uncertainty, knowledge speciali-zation and dispersion, and task complexity increase,

however, firms find it increasingly difficult to developin-house the large variety of complementary knowledgeresources needed to innovate effectively. At the sametime, as the body of knowledge and information neededfor innovation purposes becomes more divisible, theensuing ‘changing technology of technological change’allows for an increasing division of innovative laboramong large numbers of actors (Arora and Gambardella,1994). Thus, firms resort to ‘open’ strategies in order todraw from various external sources the large variety ofspecialized knowledge inputs needed for them to pursuecompetitive strategies based on innovation (Chesbrough,2003; Von Hippel, 2005). In particular, knowledge-intensive strategic alliances, such as R&D alliances ornew product development alliances, are increasingly usedby firms for innovation purposes (Kotabe and Swan,1995; Hagedoorn, 2002; Schilling, 2009), and previousstudies have offered evidence of success of these alli-ances (Hoang and Rothaermel, 2005; Kim and Song,2007). However, analogously to strategic alliances ingeneral (Harrigan, 1988; Park and Ungson, 1997),knowledge-intensive alliances often fail or fall short ofexpectations (e.g., Narula, 2004), thus motivating schol-ars to shed more light on why this happens. Doing so

Correspondence: Antonio Capaldo, Catholic University of the SacredHeart, S.E.GEST.A. Department of Management, School of Economics.1, Largo Francesco Vito, Rome 00168, Italy. E-mail: [email protected]

European Management Review, Vol. 11, 63–84 (2014)DOI: 10.1111/emre.12024

© 2014 European Academy of Management

mailto:[email protected]

mailto:[email protected]

requires a deeper understanding of the determinants ofperformance in these alliances.

Extant research offers a number of explanations of therelationships between knowledge-intensive alliances andseveral performance outcomes, including alliance gov-ernance (Dyer and Singh, 1998; Sampson, 2004), thecharacteristics of the search processes conducted withinthe alliances (Capaldo and Messeni Petruzzelli, 2011),and numerous aspects of the interorganizational networksin which the alliances are embedded (Ahuja, 2000; Baumet al., 2000; Capaldo, 2007). However, the performanceof knowledge-intensive alliances remains still relativelyunexplored (Osborn and Hagedoorn, 1997; Hoang andRothaermel, 2005), and some other possible explanationsof it have been underestimated so far. In particular, whileprevious alliance studies have shown that a variety ofpartner characteristics can offer valuable contributions toour understanding of several alliance-level performanceoutcomes (Dacin et al., 1997; Saxton, 1997; Sarkar et al.,2001), research in the field of knowledge-intensive alli-ances has rarely examined alliance performance, andspecifically the innovative performance of alliances, inthe light of the characteristics of the participating organi-zations (Hoang and Rothaermel, 2005). In an attempt tocontribute to fill these gaps, we point to the role of partnerproximity and investigate its impact on the innovativeperformance of a specific category of knowledge-intensive alliances, namely, knowledge-creating alli-ances. For the purposes of our study, knowledge-creatingalliances are dyadic (formal or informal) interfirm col-laborative ventures aimed at the joint development of newknowledge.

Proximity is a major determinant of innovation. This isespecially so in knowledge-intensive interorganizationalrelationships (Knoben and Oerlemans, 2006). On the onehand, proximity between organizations reduces uncer-tainty and enhances coordination, thus facilitating inter-active learning and new knowledge creation (Gertler,1995; Boschma, 2005). On the other hand, however,proximity may entail undesired knowledge spillovers or alack of flexibility and openness toward distant knowledgesources, which hinder innovation (Boschma, 2005).Moreover, while research in economic geography hastraditionally focused on geographic proximity exclu-sively, a considerable literature has argued that otherdimensions of proximity, that is, organizational, cogni-tive, social, cultural, institutional, and technologicalproximity, may provide alternative or complementarysolutions to the problem of coordinating knowledge-intensive activity (Torre and Gilly, 2000; Freel, 2003;Boschma, 2005; Knoben and Oerlemans, 2006). Thus,studies have tried to clarify the various dimensions ofinterorganizational proximity and ascertain their (posi-tive or negative) influence on innovation (Bell, 2005;Ganesan et al., 2005; Oerlemans and Meeus, 2005;Gomes-Casseres et al., 2006; Nooteboom et al., 2007;

Presutti et al., 2013). In a related vein, scholars haverecently started to investigate the relationships betweenthe various proximity dimensions, and the complemen-tary or substitutive effects between them (Boschma,2005; Huber, 2012).

The primary purpose of this paper is to contribute toour understanding of the determinants of performance inknowledge-creating alliances by investigating the impactexerted on it, both individually and jointly, by two differ-ent dimensions of partner proximity. We do so byadopting a knowledge-based view of interorganizationalcollaboration. Based on the characteristics of theinnovative tasks to be performed by firms involved ininterorganizational collaboration aimed at the jointdevelopment of new knowledge, we initially argue thattwo major drivers of the innovative performance ofknowledge-creating alliances are: (1) the degree ofdiversity of the knowledge resources that the alliedorganizations contribute to their joint innovative endeav-ors; and (2) the effectiveness of the interorganizationalprocesses of knowledge integration that occur at thealliance level. Then we focus on two dimensions ofpartner proximity having the potential to exert consider-able influence on the two drivers above, namely, geo-graphic and organizational proximity. Specifically, wepoint to the role of geographic distance between partnersand their affiliation with the same business group (i.e., theexistence of group ties between partners). We contendthat, when individually considered, both the examinedpartner characteristics exert a negative influence on theinnovative performance of knowledge-creating alliances.On the one hand, geographic distance between partnershinders interfirm knowledge sharing and the develop-ment of interorganizational routines for effective knowl-edge integration at the interorganizational level. On theother hand, the existence of group ties between alliedfirms, and the ensuing organizational proximity, reducesfor them the opportunities to exchange and pool non-redundant knowledge. However, the two examinedpartner characteristics are not independent of one anotherin their impact on alliance outcomes. When jointly con-sidered, they weaken each other’s negative effect oninnovative performance at the alliance level, thus exertinga complementary effect on it. Indeed, while the existenceof group ties between geographically distant organiza-tions reduces the negative influence of spatial distanceon the partners’ ability to integrate their knowledgewithin the alliance, collaborating with remote partnersweakens the negative influence of the existence of groupties between partners on knowledge diversity in alliances.

Building on the above arguments, we develop a set oftestable hypotheses and conduct empirical analysis on asample of 1,515 interfirm dyadic knowledge-creatingR&D alliances. We employ joint patents (i.e., patentsjointly filed with a patent office by, and co-assigned to,two or more organizations), and in particular interfirm

64 A. Capaldo and A. Messeni Petruzzelli


dyadic (i.e., co-assigned to two co-assignee firms) jointpatents, to identify our sample alliances, and we use dataavailable in joint patent documents in our analyses. Thus,while the level of analysis of the present study is thealliance, our unit of analysis is the resulting joint patent.Consistent with our hypotheses, we find that the moregeographically distant the partner firms, the lower theinnovative performance at the alliance level. In addition,when the allied firms are affiliated with the same businessgroup, the alliance innovative performance decreases.Finally, our results show that geographic distancebetween allied organizations and their affiliation with thesame group have a positive interaction effect on theinnovative performance of knowledge-creating alliances.

Our study makes a number of contributions to boththeory and practice. We add to the knowledge-based viewof strategic alliances by drawing a distinction betweenthree different categories of knowledge-intensive alli-ances, that is, knowledge-accessing, knowledge transfer,and knowledge-creating alliances based on the alliance’sunderlying primary logic of value creation that are,knowledge access, transfer, or coproduction, respec-tively. Moreover, we argue that knowledge diversity andknowledge integration are two major drivers of the inno-vative performance of knowledge-creating alliances andwe shed light on the trade-offs between them. We also addto the intriguing discourse on proximity and innovation.In particular, when seen in the light of the nascent litera-ture on the relationships between different dimensions ofproximity, our study suggests that geographic distanceand organizational proximity are contingent upon oneanother in their effect on the innovative performance ofknowledge-creating alliances and that distance (proxim-ity) in one dimension can be bridged (overcome) byproximity (distance) in another dimension. Finally, wecontribute to the literature on partner selection by point-ing to the importance of looking simultaneously atseveral partner characteristics when selecting alliancepartners, so as to take into account possible complemen-tary or substitutive effects between different characteris-tics. Based on our results, we recommend that, in the caseof knowledge-creating alliances, firms collaborating atlong geographic distance select their partners inside theirown group; conversely, when partners are geographicallyproximate, alliances between firms that are not affiliatedwith the same group have a comparatively higher inno-vative potential.

Theory and hypotheses

Understanding knowledge-creating alliances and theirinnovative performance

In the last twenty-five years, the increasing competitiveimportance of knowledge has led a number of scholars

to develop and embrace a knowledge-based view of stra-tegic alliances (Badaracco, 1991; Hamel, 1991; Inkpenand Crossan, 1995; Lane and Lubatkin, 1998; Khannaet al., 1998; Simonin, 1999; Grant and Baden Fuller,2004). In particular, several alliance scholars havefocused on alliances being knowledge-intensive by theirvery nature, such as R&D alliances or new productdevelopment alliances (Kumar and Nti, 1998), and cred-ited these alliances with a variety of knowledge benefitsfor the participating organizations (e.g., Powell et al.,1996; Sakakibara, 1997; Rindfleisch and Moorman,2001), but often without clearly distinguishing betweenthem (Capaldo, 2014). However, an attentive reading ofthe existing knowledge-based literature suggests thatfirms may resort to interorganizational collaboration fordifferent reasons, that is, in order to access the knowl-edge of partners, to acquire the partners’ knowledge,or to jointly create (i.e., coproduce) new knowledgethrough interorganizational interaction, so that theresulting alliances exhibit different underlying primarylogics of value creation (Capaldo, 2010).

Thus, in the case of knowledge-intensive alliances(or simply alliances hereinafter), while knowledge-accessing alliances allow firms to use (rather than inter-nalize) the knowledge of partners, so as to more quicklyassemble the complex combinations of competenciesneeded to develop innovation and bring new products tomarkets (Grant and Baden Fuller, 2004; Grunwald andKieser, 2007), knowledge transfer alliances are pri-marily aimed at transferring existing tacit and/or explicitknowledge between allied organizations so as to allowthem acquire (i.e., internalize) their partners’ knowl-edge, thereby increasing their own knowledge base(Hamel, 1991; Larsson et al., 1998). Finally, knowledge-creating alliances are primarily aimed at the jointdevelopment of new knowledge (i.e., new technologies,processes, or products) through interorganizationalinteraction by combining the participating organiza-tions’ knowledge bases (Inkpen, 1996; Larsson et al.,1998).1 Combining the partners’ knowledge signifi-

1Knowledge-accessing, knowledge-transfer, and knowledge-creating alliances should not be considered as strictly separatecategories. Conversely, they are composed of alliances pri-marily (rather than exclusively) aimed at knowledge access,transfer, and coproduction, respectively. For our purposes here,this implies that firms participating in knowledge-creating alli-ances may also gain access to the knowledge of partnersand/or transfer knowledge between them, yet this is not theprimary objective of the alliance and is merely instrumental tothe joint development of new knowledge. In fact, the copro-duction of new knowledge in alliances often requires alliedfirms to access technologies or use components already devel-oped by the other party, and/or to exchange knowledge typi-cally in order to set up a shared knowledge base which servesas a ‘platform’ upon which they can more easily develop newknowledge resources through interorganizational collaboration(Capaldo, 2010).

Proximity and Innovative Performance in Knowledge-Creating Alliances 65


cantly increases the alliance innovative potential, andhence the allied organizations’ ability to co-producetruly new knowledge, while also reducing the develop-ment costs of novel technologies and products andspeeding up their time-to-market (Capaldo, 2014); this,in turn, positively affects innovative performance at thealliance and/or firm levels (Sampson, 2007; Capaldo andMesseni Petruzzelli, 2011). Examples of knowledge-creating alliances drawn from the sample employed inthis study include a joint venture established in 1994between LG and Alps Electric (Japan) to expedite thejoint development of next generation display technol-ogies, as well as an R&D partnership started in 1998 byMitsubishi Electric and Mitsubishi Motors to jointlydevelop a new device for controlling fuel supply andignition timing of multi-cylinder engines. In the case ofR&D alliances, while licensing and technology transferare examples of knowledge-accessing and knowledgetransfer alliances respectively, knowledge-creating alli-ances may result into newly joint-patented technologiesor products (Rocha, 1999).

A considerable literature has focused on the nature,processes, and outcomes of the three categories ofalliances described above (Mowery et al., 1996;Sakakibara, 1997; Lane and Lubatkin, 1998; Simonin,1999; Grant and Baden Fuller, 2004; Masiello et al.,2013). Borrowing the categories introduced by DiGuardo and Harrigan (2012) in their analysis of 27years of research on alliances and innovation, this lit-erature resides mostly within the ‘technological changegroup’, as it conceives of alliances as vehicles to facili-tate knowledge and capability development by leverag-ing and combining knowledge developed by partnerorganizations, and adopts a “learning and knowledgeapproach” to joint innovation. Drawing on this literatureand approach, we concentrate on knowledge-creatingalliances to shed more light on the drivers of their inno-vative performance.

Being aimed at the joint development of new knowl-edge, knowledge-creating alliances require partners tocombine heterogeneous knowledge and share knowl-edge resources that are complex and tacit to a largeextent (Coff, 2003). Therefore, knowledge-creatingalliances are also characterized by high reciprocal inter-dependence and so require extensive coordination(Thompson, 1967; Krishnan et al., 2006), which asks forclose and continuous interaction and for considerableon-going mutual adjustment between the participatingindividuals and organizations (Gulati and Singh, 1998).Accordingly, we argue that two major drivers of theinnovative performance of knowledge-creating alliancesreside in the characteristics of the knowledge contrib-uted by the allied firms to their joint innovative endeav-ors, and in the effectiveness of the interorganizationalprocesses by which that knowledge is integrated withinthe alliance.

Regarding the first point, studies rooted in the recom-binant perspective to innovation have claimed thatknowledge diversity is a prerequisite for significantinnovation outcomes (Fleming, 2002; Phene et al.,2006). In the field of alliance studies, Sampson (2007)has noted that the diversity of partners’ knowledgeincreases the possible number of new recombinations,thereby enhancing the innovative potential of R&Dalliances. As for the second point, successfulknowledge-creating collaboration in alliances requires anumber of (inter)organizational conditions. First, acertain degree of organizational and cognitive similar-ity between partners has the potential to facilitate effec-tive interorganizational interactions (Boschma, 2005;Knoben and Oerlemans, 2006; Schulze and Brojerdi,2012) and reduce coordination costs in alliances (Gulatiand Singh, 1998). Indeed, the sharing of cultural values,organizational routines, and control systems betweenpartner organizations may facilitate mutual understand-ing, cooperation, and new knowledge creation in alli-ances (Nooteboom et al., 2007). Second, innovation inalliances is enhanced by the overall quality of the under-lying interorganizational relationships (Arino et al.,2005). Accordingly, long-term collaboration, interfirmtrust, and a rich social fabric of interpersonal relation-ships among the participating individuals across theboundaries of allied firms all have been shown to posi-tively affect knowledge-intensive cooperation and thedevelopment of new knowledge and capabilities in alli-ances, with positive effects on the ensuing innovativeoutcomes (Ingram and Roberts, 2000; McEvily andMarcus, 2005; Capaldo, 2007).

In the present paper, we contend that the diversity ofthe knowledge inputs combined within knowledge-creating alliances as well as organizational and cognitivesimilarity between allied firms and the development ofsocial networks across their boundaries can be signifi-cantly influenced by proximity (or distance) betweenpartners.

Partner proximity and the innovative performance ofknowledge-creating alliances

Proximity is a major determinant of innovation (Aminand Wilkinson, 1999; Oerlemans et al., 2000), andseveral dimensions of proximity, that is, geographic,organizational, cognitive, social, cultural, institutional,and technological proximity have been outlined in pre-vious studies as relevant in interorganizational collabo-ration (Knoben and Oerlemans, 2006). However, theproliferation of the dimensions of proximity has gener-ated conceptual ambiguity that risks diluting the signifi-cance and impact of the proximity notion, while alsohindering empirical research due to substantial concep-tual overlap among the different dimensions. We thusfollow previous scholars who have suggested distin-



guishing between two fundamental dimensions of prox-imity, namely, geographic and organizational proximity(e.g., Kirat and Lung, 1999; Torre and Gilly, 2000;Oerlemans and Meeus, 2005)2.

The notion of geographic proximity is straightforward.It refers to the spatial or physical distance between eco-nomic actors (alliance partners, for our purposes here)and so ‘expresses the kilometric distance that separatestwo units (individuals, organizations, towns . . . ) ingeographic space’ (Torre and Rallet, 2005: 49).Organizational proximity can be understood as based onboth a ‘logic of belonging’, according to which ‘actorsclose in organizational terms belong to the same space ofrelations’ (Torre and Gilly, 2000: 174), and a ‘logic ofsimilarity’, according to which organizationally proxi-mate partners share the same ‘system of representations,or set of beliefs, and the same knowledge’ (Torre andRallet, 2005: 50). A typical example of organizationallyproximate firms is those belonging to the same businessgroup (Kirat and Lung, 1999; Torre and Gilly, 2000).Indeed, member organizations of the same group interacteasily with each other. Moreover, and more importantlyhere, they are at low ‘cognitive distance’ (Nooteboom etal., 2007), thus sharing beliefs and cultural values, as wellas organizational routines and some basic knowledge(Torre and Rallet, 2005), and share innovation infrastruc-tures (e.g., internal labor markets for knowledge workers)that facilitate knowledge integration (Mahmood andMitchell, 2004; Chang et al., 2006).

In order to investigate the impact of geographic andorganizational proximity on the innovative performanceof knowledge-creating alliances, we focus on the follow-ing two partner characteristics: geographic distancebetween partners and their affiliation with the same busi-ness group. Geographic distance is a widely studieddeterminant of innovation (e.g., Maskell and Malmberg,1999; Bell and Zaheer, 2007; Whittington et al., 2009).In particular, scholars have argued for a positive rela-tionship between geographic proximity and knowledgetransfer and innovation in alliances (Rosenkopf andAlmeida, 2003; Knoben and Oerlemans, 2006). Thisview is based to a large extent on the observation that, asrevealed by several patent-based studies, knowledgespillovers are geographically bounded (Jaffe et al., 1993;Audretsch, 1998; Singh, 2005). In fact, firms tend toagglomerate in a handful of more innovative locationswherein spatial proximity facilitates interorganizationallearning and the development of new knowledge

(Saxenian, 1994; Baptista and Swann, 1998; Bell, 2005;Folta et al., 2006; Capello, 2009). However, the liabil-ities of collaborating with distant partners are still inneed of further research. Moreover, we know tantalizinglittle about the innovation advantages of geographic dis-tance between allied firms, and in particular, about thecontingencies under which such positive effects mayarise (but notable exceptions include, among others,Ganesan et al., 2005; Bell and Zaheer, 2007).

Business groups (or simply groups hereinafter) areorganizations composed of legally independent firms,bound together by formal (i.e., equity) and informal(e.g., family and social network) ties, that operate undercommon administrative and financial management(Ghemawat and Khanna, 1998; Mahmood and Mitchell,2004; Besley and Brigham, 2008). Although with somedifferences in their structure, business groups are ubiqui-tous in emerging markets as well as in some developedeconomies. Examples of business groups include SouthKorean chaebols such as Samsung and LG, Japanesekeiretsus such as Mitsubishi, and European industrialgroups such as Siemens and Philips. Business groupshave been shown to be a major determinant of innova-tion at the firm (Belenzon and Berkovitz, 2010; Hsiehet al., 2010), industry (Mahmood and Mitchell, 2004),and macroeconomic (Khanna and Yafeh, 2007) levels.Thus, we have substantial knowledge about the positiveand negative effects of firms’ group affiliation on knowl-edge sharing and innovation (Mahmood and Mitchell,2004; Chang et al., 2006). To our knowledge, however,previous scholars have not directly examined the impactof partners’ same business group affiliation on alliance-level innovation.3 Extant literature has offered evidenceof the innovation advantages of integrating knowledgethrough interorganizational collaboration within thegroup’s boundaries (Lincoln et al., 1998; Chang andHong, 2000; Hsieh et al., 2010), but scant attention hasbeen paid to the contingencies under which such positiveeffects may arise and the possible detrimental effects ofthe existence of group ties between partners on the alli-ance innovative performance.

In an attempt to fill the above voids in previousresearch, in the following sections we advanceknowledge-based arguments in support of three hypoth-eses concerning the individual and joint impact that geo-graphic distance between partners and their affiliation

2Indeed, cognitive, social, cultural, and institutional proximitycan be subsumed under the notion of organizational proximity(Kirat and Lung, 1999; Torre and Gilly, 2000; Knoben andOerlemans, 2006). This is only marginally so for technologicalproximity, which however is not organic to the ‘core’ literatureon proximity (e.g., Torre and Gilly, 2000; Boschma, 2005; Torreand Rallet, 2005). Thus, we control for this last dimension ofproximity in our econometric analysis.

3Nevertheless, previous studies on business groups offer someclues that we will draw upon to substantiate our hypotheses. Tothis end, we will also draw on research on large diversifiedmulti-unit corporations (e.g., Williamson, 1985; Hansen, 1999;Rosenkopf and Nerkar, 2001). Indeed, as observed by Changand Hong (2000: 430), ‘although group-affiliated companies arelegally independent firms with their own intangible and tangibleresources, they function as operating divisions under the tightcontrol of group headquarters’ (see also Chang and Choi, 1988).



with the same business group exert on the innovativeperformance of knowledge-creating alliances.

Geographic distance between partners and the innova-tive performance of knowledge-creating alliances.Exploration across geographic neighborhoods can bevaluable in achieving innovation (Sidhu et al., 2007). Aprimary reason for this is that knowledge tends todevelop along different technological trajectories indistant geographic contexts. However, despite differ-ences in the knowledge bases of distant partners maybe conducive to innovation, distant firms may find itdifficult to effectively exploit this higher innovativepotential. In fact, the liabilities of combining geo-graphically distant knowledge in alliances lie in the(inter)organizational processes by which allied firmsintegrate their knowledge bases to develop new tech-nologies, processes, or products.

Previous literature on the virtues of geographic prox-imity has revealed that firms tend to draw upon theknowledge stock of geographically proximate partners(Rosenkopf and Almeida, 2003) and that R&D alliancesbetween proximate partners are valuable sources ofknowledge and innovation (Gomes-Casseres et al.,2006). A major explanation for the relationships betweengeographic proximity of allied firms and innovation inknowledge-creating alliances lies in that spatial propin-quity encourages frequent face-to-face interactions, thusstimulating the emergence of dense interpersonal net-works across the boundaries of partner organizations(Feldman, 1994; Uzzi, 1997). This, in turn, increases theopportunities for formal and informal knowledgeexchange, and for the effective combination of the part-ners’ knowledge, by facilitating the development ofrelational trust (Kale et al., 2000; Capaldo, 2007), theemergence of idiosyncratic languages (Romo andSchwartz, 1995; Uzzi, 1997), and the accumulation onboth sides of a deep mutual understanding of the otherparty’s organizational routines (Romo and Schwartz,1995; Dyer and Hatch, 2006).

The above arguments suggest that, when firms partici-pating in alliances are geographically distant, the allianceinnovative performance may suffer. As geographic dis-tance increases, the opportunities to create and strengthenover time social relationships across the boundaries ofallied organizations tend to decrease (Boschma, 2005).This exerts a negative influence on knowledge sharingand innovation development for at least three reasons.First, geographic distance undermines the developmentof trust-based relationships. In fact, especially in theabsence of a rich social fabric of interpersonal relation-ships across their boundaries, geographically distantpartners are more likely to behave opportunistically andengage in ‘learning races’ (Hamel et al., 1989) within thealliance. While such behaviors may produce positiveeffects on learning and innovation development at the

firm level, they hamper knowledge-intensive cooperationat the interorganizational level, leading the parties tomake extensive use of organizational safeguards in orderto protect their proprietary knowledge and avoid uncon-trolled information disclosure (Inkpen and Tsang, 2005),with detrimental effects on the alliance innovative out-comes. Second, geographic distance typically limits thepartnered organizations’ ability to interact repeatedly andtherefore develop, learn, and adjust over time the idiosyn-cratic languages needed for the sharing of ‘fine-grainedinformation’(Uzzi, 1997) between firms and for allowingsmooth side-by-side cooperation and knowledge copro-duction. Third, spatially distant partners will find itdifficult to develop interorganizational routines forknowledge-intensive cooperation as they will likelylack detailed knowledge of their respective orga-nizational practices. In addition, the development ofinterorganizational routines requires ‘trial-and-errorexperiments’ that are difficult to conduct at long geo-graphic distance. All this leads to our first hypothesis:

Hypothesis 1: In knowledge-creating alliances, geo-graphic distance between partners negatively affectsthe alliance innovative performance.

Affiliation with the same business group and the inno-vative performance of knowledge-creating alliances.The notion of organizational proximity introduced earlierimplies that firms affiliated with the same business groupcan interact more easily with each other and share beliefs,cultural values, organizational routines, and some basicknowledge, as well as innovation infrastructures forknowledge integration. In the next subsection we willclaim that all this can have beneficial impacts on innova-tion in alliances by lessening the liabilities of geographicdistance between partners. However, building onNooteboom et al. (2007), we contend here that the lowercognitive distance between member companies of thesame group and the ensuing lower diversity of theirorganizational knowledge bases can hurt the innovativeperformance of their joint innovative endeavors.4

4This may seem inconsistent with a tenet of the literature on thegovernance of R&D alliances, according to which equity-basedarrangements are conducive to higher innovative performance inthat they make alliance partners more willing and able to shareknowledge (e.g., Mowery et al., 1996; Gulati and Singh, 1998).However, several scholars have showed equity-based alliancegovernance to be important to innovation in the presence ofconsiderable diversity among the knowledge bases of the par-ticipating organizations (Colombo, 2003; Sampson, 2007). Inparticular, previous research has suggested that, since more hier-archical organization, such as equity-based alliance governance,is not without its costs, especially in terms of excessive bureau-cracy and low flexibility (e.g., Hagedoorn and Duysters, 2002;Sampson, 2004; Van de Vrande, 2013), its use is best reservedfor situations in which the diversity of the partners’ knowledgeis high (Sampson, 2007). Following a similar line of reasoning,



Whereas sharing some amount of basic knowledgeand the existence of similar knowledge-processingsystems and dominant logics can sustain knowledgeabsorption and innovation in alliances (Mowery et al.,1996; Lane and Lubatkin, 1998), too much similarity inthe knowledge bases of partner firms hurts indeed theirability to innovate (e.g., Sampson, 2007). This is espe-cially the case with alliances between firms affiliatedwith the same business group. Previous literature sug-gests that bounded rationality of decision makers, thesmooth functioning of the existing organizational rou-tines, and the cumulative nature of organizational learn-ing encourage economic actors to search for innovationin the neighborhoods of what they already know, thusobscuring the view of new knowledge and novel tech-nologies (Nelson and Winter, 1982; Stuart and Podolny,1996). Over time, this may lead allied firms belonging tothe same group to fall into ‘competency traps’ (Levittand March, 1988), with detrimental effects on innova-tion. Accordingly, Rosenkopf and Nerkar (2001) foundthat the use of knowledge from inside the company,including the use of knowledge from other subunits ofthe same group, has a limited impact on innovation,substantially lower than the use of knowledge from firmsoutside the group. The reason for this is that theorganizational knowledge bases of partner firms affili-ated with the same group tend to be characterized by alower degree of diversity when compared to those ofoutside firms. Conversely, firms outside the group mayprovide the diversity of ideas needed to stimulate inno-vation (Mahmood and Mitchell, 2004). Accordingly,based on a longitudinal study of alliance formation byJapanese firms, Lincoln and Guillot (2009) have shownthat, especially in periods of increased uncertainty, Japa-nese electronics firms tended to venture beyond theirkeiretsu networks for R&D alliances in search of hetero-geneous knowledge resources needed to innovate.

In addition, alliances between firms belonging to thesame business group are grounded into tight relationsbetween the participating firms and embedded in theaccompanying context of social relationships among thefirms’ organizational actors. Moreover, in some contexts(e.g., chaebols and keiretzus), business groups appear associal communities wherein behavior is governed bysocial norms (e.g., principles of solidarity, reciprocity,and trust) which originate in family, kinship, or ethnicties among the group members (Khanna and Palepu,2000; Granovetter, 2005). These linkages and norms

sustain both the creation of the alliance and the initialdevelopment of the underlying interorganizational rela-tionship and may ensure equitable and profitablerelations. Over time, however, they risk becomingcounterproductive in that they tend to make the relation-ship extremely resilient to losses in its instrumentalvalue – a phenomenon that has been labeled ‘relationalinertia’ by Gargiulo and Benassi (2000). In such circum-stances, the partners will find it difficult to disband thealliance even after substantial knowledge transfer hastaken place between them, so reducing the innovativepotential of their partnership.

Finally, the risk of being locked-in into a specificexchange relation with an organizationally proximatepartner also entails for allied firms belonging to the samegroup, the danger of finding themselves cognitivelylocked-in into the existing shared practices and routines,as well as into specific technologies and knowledgedomains. In particular, interfirm alliances in whichgroup ties exist between partners risk evolving over timeinto closed and inward-looking systems (i.e., overlystrong relationships) that insulate the participatingorganizations from other external sources of usefulknowledge (Boschma, 2005). This is in line withMahmood and Mitchell’s (2004) finding that businessgroups hinder innovation by creating entry barriers tonon-group firms, thereby inhibiting the proliferation ofnew ideas. Being locked out of the knowledge flowsfrom the ‘outside world’, allied firms affiliated with thesame group might see the growth rate of their respectiveknowledge bases slow down. This will prevent themfrom both developing in-house new useful knowledgeand gaining access to further external sources of hetero-geneous knowledge outside the alliance, which in turnwill negatively affect their ability to jointly developinnovation within the alliance (Capaldo, 2007). Theabove arguments suggest our second hypothesis:

Hypothesis 2: In knowledge-creating alliances, theexistence of group ties between partners negativelyaffects the alliance innovative performance.

Interaction effect of geographic distance between part-ners and their affiliation with the same business groupon the innovative performance of knowledge-creatingalliances. So far we have argued that, when consideredindividually, both geographic distance between partnersand their belonging to the same group are negativelyrelated to the innovative performance of knowledge-creating alliances. Nevertheless, when jointly consid-ered, the two examined partner characteristics weakenone another’s negative effect on the innovative perfor-mance of alliances – or in other words, they reveal acomplementary effect on it. Two independent variableshave a complementary effect on a dependent variable(i.e., they interact as complements) if the positive (nega-

while we do not neglect the benefits that allied organizationsaffiliated with the same group can enjoy in terms of a higherability to transfer knowledge across their boundaries and controlopportunistic behaviors, we argue that, in the case of intra-groupknowledge-intensive alliances, given that the degree of diversitybetween the partners’ knowledge bases is typically low, theliabilities of equity-based governance, in terms of an excess ofbureaucracy and reduced flexibility, outweigh its benefits.



tive) marginal effect of each independent variable on thedependent variable increases (decreases) in the presence,or for increasing levels, of the other (e.g., Rothaermel andHess, 2007).

The existence of group ties between allied organiza-tions has the potential to reduce the negative influenceof geographic distance on the partners’ ability to integratetheir knowledge within the alliance. In a recent study,Singh (2008) has shown that the use of inter-unitintegration mechanisms in multinational corporationsmoderates positively the negative effect of geographicdispersion of R&D activities on innovation quality. In asimilar vein, but focusing on alliances, we contend thatorganizational proximity and the richer networks of inter-personal relationships that characterize firms belongingto the same group may lessen the obstacles to knowledgetransfer and combination and reduce the threat of oppor-tunism that afflict alliances between spatially distantfirms (Boschma, 2005; Knoben and Oerlemans, 2006).

First, being organizationally proximate, member com-panies of a group share some amount of basic knowledgeand a common set of assumptions, cultural values, andcommunication codes (Williamson, 1985; Kogut andZander, 1992; Chang and Hong, 2000). This may impactpositively their ability to effectively identify, assimilate,and exploit each other’s knowledge, or in other words,their ‘relative’ absorptive capacity (Lane and Lubatkin,1998), which is needed for them to jointly develop newknowledge effectively, especially at long geographic dis-tance (Boschma, 2005; Phene et al., 2006).

Second, while the development of innovation betweengeographically distant partners goes along with consid-erable uncertainty and with the risk of opportunisticbehavior by the participating actors and relatedappropriability hazards (Oxley, 1997; Deeds and Hill,1999), business groups can offer formal (e.g., transfer ofkey personnel among affiliated companies, internal labormarkets, meetings, and conferences) and informal (i.e.,social networks) mechanisms to easily identify, gainaccess to, transfer, and integrate useful knowledgewithin the group (Lincoln et al., 1998; Khanna andPalepu, 2000; Chang and Hong, 2000; Inkpen andTsang, 2005). In addition, formal control mechanismsand rewarding systems reduce transaction costs andallow the partners to yield adequate returns on theirR&D investments (Williamson, 1985; Lincoln et al.,1998), while social control discourages opportunism,reduces the probability of contractual disputes, and pro-vides low-cost mechanisms for dispute resolution(Khanna and Palepu, 2000). Moreover, the higher easeand frequency of interaction between individuals acrosspartner firms affiliated with the same group sustain thedevelopment of interpersonal relationships, which inturn may create the conditions for the emergence of trustat both the interpersonal and interorganizational levelsand reduce the risk of opportunistic behaviors that other-

wise hinder knowledge sharing and combination at longgeographic distance. All this positively influences thepropensity of spatially distant firms affiliated with thesame group to openly share valuable knowledge, as wellas their ability to effectively learn from each other andbuild new knowledge by combining their existingknowledge bases, with positive effects on innovation.

Finally, firms belonging to the same group also shareorganizational rules and routines. This encourages inter-action between them, making it easier than with outsideentities (Torre and Rallet, 2005), and in particular facili-tates knowledge flow and assimilation (Phene andAlmeida, 2008). In fact, business groups appears asplatforms for knowledge sharing (Hsieh et al., 2010)wherein cooperation and knowledge transfer tend todevelop easily, thereby facilitating the combination ofknowledge from affiliated firms and their ability tocoproduce innovation, even at long geographic distance.The existence of group ties between remote alliancepartners seems to be particularly beneficial for them totransfer the highly complex and tacit knowledge thatcharacterizes knowledge-intensive collaboration, whoseexchange has been found to require strong ties betweensource and recipient entities (Hansen, 1999).

At the same time as the existence of group ties betweenpartners positively moderates the negative effect of col-laborating with spatially distant firms on alliance perfor-mance, increasing distance between allied organizationscan reduce the liabilities of alliances between membercompanies of the same group. Indeed, alliances withremote partners may benefit from a more heterogeneousset of knowledge resources (Meyer-Krahmer and Reger,1999). For example, Inkpen (1998) noted that, bringingtogether firms with different skills, knowledge bases,cultures, and international alliances create unique learn-ing opportunities for the participating organizations.Other scholars (e.g., Owen-Smith and Powell, 2004) havenoted that creating and leveraging extra-local linkages(especially alliances: Whittington et al., 2009) withdistant partners allow firms operating in regional clustersto sustain innovation by continuously accessing newways to interpret and use their existing knowledgeand novel information about external technologies andmarkets.

Combining knowledge from remote partners can beespecially beneficial in weakening the negative influenceof the existence of group ties between partners onknowledge diversity in alliances. The propensity tosearch locally that may afflict allied organizationsbelonging to the same group can have technological aswell as geographic origins (Stuart and Podolny, 1996;Phene et al., 2006). Indeed, besides being influenced bytechnologically proximate conditions, the ability ofinterfirm alliances to develop innovation may bebounded by geographic factors. Conversely, collaborat-ing with remote others creates a potential for non-



overlapping knowledge bases (Narula and Hagedoorn,1999; Singh, 2008), thus increasing the opportunities forgeographically distant partners to jointly develop newcombinations. In addition, combining the knowledgebases of remote partners increases the opportunity set ofnew combinations that can be tried with existing com-ponents, as inventors from distant contexts may utilizethe same knowledge in different, and sometimes com-plementary, ways (Phene et al., 2006).

Allying with geographically distant partners mayameliorate other liabilities of collaborating with firmsaffiliated with the same business group. As previouslydiscussed, the development of social networks is in partimpeded at long geographic distance. Although this canhave detrimental effects on innovation, it may alsoreduce the risk that interorganizational relationshipsbetween member companies of the same group becomeexcessively resilient to losses in their instrumental value,and the ensuing phenomena of ‘relational inertia’(Gargiulo and Benassi, 2000). In addition, hindering thedevelopment of overly strong interorganizational rela-tionships, geographic distance may reduce the risks ofbeing cognitively locked-in into alliances with firmsaffiliated with the same group, and the ensuing negativeeffects on the diversity of the knowledge resources com-bined within the relationships.

The positive interaction effect of the two examinedpartner characteristics on the innovative performance ofknowledge-creating alliances is captured in our finalhypothesis:

Hypothesis 3: In knowledge-creating alliances, geo-graphic distance and group ties between partners havea complementary effect on the alliance innovativeperformance.

Methods

Sample and data

We tested the hypotheses outlined above on a sample ofknowledge-creating R&D alliances established, between

1998 and 2003, by ten ‘focal’ 2008 Fortune Global 500companies operating in the electric and electronic equip-ment (EEE) industry. The selected focal companies arethe most innovative firms in the EEE industry in terms ofthe number of granted patents by the European PatentOffice (EPO) during the selected time-period (Table 1).Testing our hypotheses on a sample of knowledge-creating R&D alliances established by highly innovativefirms operating in the EEE industry is in line with thepurposes and methods of our study for the followingreasons. First, firms in the electric and electronic fieldhave been reported to largely and increasingly use R&Dalliances to generate new technologies, processes, andproducts (Duysters and Hagedoorn, 1996; Oxley andSampson, 2004). Second, previous research has shownthat patents are effective means for firms to protect theirintellectual property in the examined field (Levin et al.,1987; Baudry and Dumont, 2006), which supports ourchoice to rely on a patent-based measure of innovationperformance.

We relied on joint patents for both sample selectionand data collection. Joint patents are a specific form ofa standard patent jointly filed with a patent office by,and co-assigned to, two or more organizations, whothus share the property rights on a jointly developedinvention (Hagedoorn, 2003). Drawing on interviewswith companies heavily involved in joint-patenting,Hagedoorn (2003) reported that joint patents are largelythe result of formal, or more often informal, interfirmR&D collaboration where companies are unable todivide the inventions among the partners due to thecomplex, non-separable, and at least in part tacit natureof the newly created knowledge. In one of the fewempirical investigations on a sample of joint patents,Kim and Song (2007) focused on joint patents as outputof alliances aimed at creating new technology andobserved that joint patents arise ‘from formal interfirmcollaboration . . . wherein researchers from partner firmsinteract face-to-face, exchange their ideas, and solveproblems jointly’ (Kim and Song, 2007: 468–469). SuchR&D collaboration may involve fully autonomous firmsas well as firms belonging to the same business group. In

Table 1 The focal companies (1998–2003)

Focal company Headquarter Total number of patentsregistered at the EPO

Average annual revenues($ millions)

Average numberof employees

Matsushita Osaka (Japan) 154,897 77,871 313,594Hitachi Tokyo (Japan) 122,438 87,615 360,194Samsung Seoul (South Korea) 119,707 89,476 84,721Toshiba Tokyo (Japan) 101,781 60,842 204,958Sony Tokyo (Japan) 89,749 70,925 185,800LG Seoul (South Korea) 81,080 68,754 29,496Mitsubishi Electric Tokyo (Japan) 69,652 32,965 108,500Siemens Munich (Germany) 58,888 107,342 428,000Philips Eindhoven (Netherlands) 43,206 38,707 128,011Sharp Osaka (Japan) 42,107 26,741 54,765



the latter cases, the resulting joint patents represent theoutput of collaborative technical activities carried outjointly by R&D personnel from different units within thesame corporation, an organizational practice that hasbeen shown to be critical for knowledge integrationespecially in multinational groups (e.g., Frost and Zhou,2005).

Thus, previous literature suggests that joint patentscan serve as a proxy for R&D knowledge-creating alli-ances. Indeed, Rocha (1999) noted that joint patentsallow us to specifically capture R&D interorganizationalcooperation aimed at the development of new knowl-edge, while leaving out of the analysis other types ofR&D collaborative efforts, such as licensing and tech-nology transfer, which more closely resemble R&Dknowledge-accessing and knowledge transfer alliances,respectively. Previous studies also suggest that jointpatents can be employed to evaluate the innovative per-formance of interorganizational relationships (MesseniPetruzzelli, 2011). In particular, Kim and Song (2007)and Capaldo and Messeni Petruzzelli (2011) relied onjoint patents to measure the innovation output of R&Dalliances directed to the joint development of newknowledge. Based on the above contributions, we reliedon joint patents to both identify our sample R&Dknowledge-creating alliances and gauge their innovativeperformance.

In order to identify the sample alliances, we first gath-ered all the joint patents filed by the ten focal companiesbetween 1998 and 2003. We considered joint patentsfiled with the European Patent Office (EPO) exclu-sively.5 This led us to identify a set of 1,552 joint patents.The period 1998–2003 was chosen because the numberof joint patents started to increase significantly at the endof the 1990s (Hicks and Narin, 2001; Hagedoorn, 2003).Second, being interested in interfirm dyadic alliancesonly, we dropped the joint patents co-assigned to morethan two co-assignee firms (9 joint patents) and thoseinvolving non-firm partners, namely, public organiza-tions (13 joint patents), research centers (10 jointpatents), and universities (5 joint patents). This leftus with 1,515 interfirm dyadic (i.e., co-assigned totwo co-assignee firms) joint patents. For our purposeshere, each selected dyadic joint patent represents adyadic knowledge-creating R&D alliance. Thus, 1,515interfirm dyadic knowledge-creating R&D alliancesestablished by the ten focal companies with 225

different partner firms were recognized and analyzed. Inline with the notion of knowledge-creating alliance illus-trated in a previous section, the sample alliances weremainly devoted to develop novel products and techno-logical solutions in the electric and electronic field andtherefore required intensive knowledge-based interac-tion across the participating organizations’ boundaries.Examples include the following partnerships: betweenHitachi and Sony to develop new systems for preventingmusic and film illegal copying, between Toshiba andIBM to develop new semiconductors based on Soi(silicon on insulator) technology, and between SamsungElectro Mechanics and Samsung Fine Chemicals todevelop a new method for preparing high qualitybarium titanate. Table 2 reports the distribution of oursample alliances per focal company and year. Table 3shows some characteristics of the focal companies’partners.

We also employed data available in joint patent docu-ments to measure our independent and dependent vari-ables and some control variables. Thus, while the levelof analysis of the present study is the alliance, our basicunit of analysis is the individual joint patent and itsassociated content.

5Our study comes out of a larger research program on the tech-nological base of the European EEE industry. One section of theprogram focused on collaborative (alliance-based) innovation,specifically on the alliance behavior (and the ensuing innovationperformance outcomes) of the most innovative companies in theEEE industry at the European level. The present paper is part ofthe research output of this specific section of the overallprogram.

Table 2 The sample alliances

Focalcompany

Number of alliances

1998 1999 2000 2001 2002 2003 Total

Philips 93 122 28 36 26 28 333Siemens 101 42 52 59 26 23 303Toshiba 42 44 39 26 18 28 197Hitachi 33 32 18 16 21 31 151Mitsubishi 28 24 29 23 31 16 151Sharp 25 31 30 23 15 12 136Samsung 5 2 12 28 21 24 92Matsushita 9 5 6 8 19 14 61LG 6 5 7 5 11 12 46Sony 6 4 3 5 11 16 45Total 348 311 224 229 199 204 1,515

Table 3 The focal companies’ partners (1998–2003)

Focalcompany

Number ofdifferentpartners

Average size ofpartners (numberof employees)

Percentage of partnersoperating in the EEEindustry

Philips 13 831.25 87Siemens 33 2130.25 71Toshiba 43 9907.48 72Hitachi 36 2301.86 48Mitsubishi 19 927.35 69Sharp 21 3345.77 68Samsung 18 1157.03 64Matsushita 14 1964.63 60LG 16 7751.03 53Sony 12 8010.41 81



VariablesDependent variable. Our dependent variable is theinnovative performance of knowledge-creating alliances(InnPerf). We measured it by using the number of cita-tions each sampled joint patent had received fromsubsequent patents. The number of citations receivedby patents (i.e., forward citations) has been largelyemployed to evaluate innovation performance (e.g.,Miller et al. 2007; Sampson, 2007; Singh, 2008). In anearly contribution, Trajtenberg (1990) found patent cita-tions to be highly and significantly correlated with inde-pendent measures of the value of innovations in the fieldof computed tomography scanners. Subsequent studieshave supported and extended this finding, showing thatforward citations are positively related to patent value(Harhoff et al., 2003) and that highly cited patents yieldmore economic profits than patents that are less fre-quently cited (Hall et al., 2005). Thus, we measured thealliance innovative performance by the number of cita-tions each joint patent had received within five years ofthe issue date from subsequent patents, excluding self-citations of the co-assignees. In accordance with previ-ous studies (e.g., Stuart and Podolny, 1996; Ahuja, 2000;Bonaccorsi and Thoma, 2007; Nooteboom et al., 2007),an equal 5-year moving window to be cited was assignedto each joint patent in an attempt to eliminate possiblebiases in the number of citations received. In fact,patents granted in earlier years are exposed to the risk ofbeing cited by subsequent patents for a longer timeperiod. A 5-year window was deemed appropriatebecause, as suggested by Griliches (1979), knowledgecapital tends to lose most of its economic value withinfive years. Self-citations were excluded in that, ratherthan gauging the overall societal value of innovations,we are interested here in capturing the extent to whichallied firms (i.e., joint patent co-assignees) may profitfrom their jointly-developed innovative solutions. Thisis better reflected by non-self-citations. Indeed, whereasself-citations express the extent to which firms build ontheir own previous R&D efforts, citations from othereconomic actors provide a more objective estimation ofthe actual market relevance of patents (Cattani, 2005).

Independent variables. We measured geographic dis-tance between firms participating in alliances(GeoDistance) as a continuous positive variable express-ing the spatial distance between their location sites.Using patent data to measure spatial distance betweencompanies may be misleading, however. This is espe-cially so when patent assignees are multinational com-panies, in that patent officers may assign patents tothe company headquarters regardless of the specificcompany subsidiaries involved in the invention process.In order to remedy this situation, when at least one of thepatent co-assignees were multinational companies andwe noted that the inventors’ addresses, reported in the

patent documents, were different from those of theco-assignees, we focused on the inventors’ addresses.Specifically, starting with the first inventor and then pro-ceeding with the remaining ones, we matched the inve-ntor’s address with a list containing the addresses of thetwo co-assignees and all their subsidiaries, that we drewfrom multiple sources including corporate reports, pressreleases, Who Owns Who directories, and the Internet.This allowed us to identify, with a reasonable degree ofconfidence, the organization (headquarter or companysubsidiary) to which each inventor belonged, and hencethe partnered organizations that actually participatedin each sample joint patent.6 We then measuredGeoDistance as the natural logarithm of the spatial dis-tance (in kilometers), at the city level, between the iden-tified partners.

We gauged affiliation of allied firms with the samebusiness group by including in our model, for eachsample alliance, a binary variable (SameGroup) takingvalue one if the co-assignees of the corresponding jointpatent belonged to the same business group at the jointpatent issue date, zero otherwise (e.g., de Faria et al.,2010). Drawing on previous literature (Belenzon andBerkovitz, 2010; Hsieh et al., 2010), we considered abusiness group as including all the firms controlled by anaffiliated firm. Thus, for example, in the case of the jointpatents filed by Tokyo Shibaura Electric Co. and ToshibaMicroelectronics KK, SameGroup was coded one asboth companies belonged to the Toshiba Group. Wecollected group membership data from multiple datasources including the companies’ annual reports andwebsites, Who Owns Who directories, and World’vestBase, an on-line database containing detailed financialinformation on over 51,000 public companies in morethan 130 countries.

Control variables. In order to control for the effectsof other factors on the innovative performance ofknowledge-intensive alliances, we introduced severalcontrol variables. First, previous research has suggestedthat firm size is significantly associated to innovation(Cohen and Levin, 1989). Large size yields availabilityof financial resources to invest heavily in innovationactivities, scale advantages in R&D, and scope econo-mies due to complementarities between R&D and otherorganizational activities such as marketing and finance.However, increasing firm size may result in decreasinginnovation performance (Ahuja and Lampert, 2001). Infact, large size may entail higher coordination costs, lessflexibility, and less motivation for individual scientists.We therefore controlled for both focal company size

6Note, however, that we needed to apply this procedure to onlya restricted number of cases (7% of our sample).



(FocCompSize) and partner size (PartnerSize), meas-ured as the natural logarithm of the number ofemployees of our focal companies and their partners,respectively.

Second, the organizational technological capital,which reflects an organization’s technological compe-tence and propensity to innovate, has been shownto positively influence innovative performance(Nooteboom et al., 2007). Accordingly, we con-trolled for technological capital of both the focal compa-nies (FocCompTechCapital) and their partners(PartTechCapital), measured as the natural logarithm ofthe number of patents previously filed with the EPO bythe focal companies and their partners, respectively. Onlypatents filed during the five years prior to the issue date ofeach examined joint patent were considered (e.g., Pheneet al., 2006; Nooteboom et al., 2007).

Third, analogously to Singh (2008), we consideredthat the innovative performance of an alliance might beaffected by the size of the research team involved in thecollaborative project. A larger team size yields econo-mies of specialization and access to a larger and morediverse pool of knowledge. We therefore controlled forteam size (TeamSize) by counting the number of inven-tors on each joint patent.

Fourth, since the outcome of innovative activities maybe influenced by the stock of accumulated knowledgerelated to the specific activities to be carried out (Cohenand Levinthal, 1990), we controlled for relatednessbetween the focal companies’ technological capabilitiesand the collaborative project at hand. To do so, we intro-duced a dummy variable (InnovRelatedness) gaugingwhether each sampled joint patent was related to patentspreviously filed with the EPO by the focal company.Drawing on Nooteboom et al. (2007), we considered ajoint patent to be related to the issuing focal company’stechnological capabilities (i.e., InnovRelatedness = 1)provided it had been assigned a main patent class inwhich the focal company had already patented duringthe five years preceding the joint patent’s issue date.Otherwise, the joint patent was deemed not related tothe focal company’s technological capabilities (i.e.,InnovRelatedness = 0).

Fifth, while our study is focused on the role of geo-graphic and organizational proximity between alliancepartners, similarities and differences between partners’technological capabilities have been shown to exert con-siderable influence on innovative outcomes in alliances(Knoben and Oerlemans, 2006; Sampson, 2007). Thus,we controlled for technological proximity between ourfocal companies and their partners. Based on previousliterature (Sampson, 2007), we measured technologicalproximity (TechProximity) by examining the extent towhich allied firms had patented with the EPO in thesame technology classes. Specifically, we employed thefollowing index:

Tech oximityf f

f f f fi j

i j

i i j j

Pr , =′

′( ) ′( )

where fi and fj are multidimensional vectors capturingthe distribution of all the patents filed by the focalcompany (i) and by its partner (j) across the n (n = 1, . . .,129) three-digit patent classes during the five years pre-ceding the issue date of the examined joint patent.Apexes indicate transposed vectors. TechProximityi,j

varies between zero and one, with a value of one indi-cating the greatest possible technological proximitybetween partners.

Sixth, the existence and number of prior alliancesamong partner organizations are generally deemed toinfluence collaborative performance (Kogut, 1989). Prioralliances allow partners to develop over time trust-basedrelationships (Gulati, 1995a) and interorganizational rou-tines for knowledge-intensive interactions (Powell et al.,1996), thus sustaining and speeding up innovation.Nevertheless, under certain conditions, repeated relation-ships may over time reduce the innovative potential ofinterfirm collaboration (e.g., Capaldo, 2007). We there-fore controlled for the number of prior R&D knowledge-creating alliances between the organizations involved inour sample alliances (PriorAlliances). To do so, weemployed the number of joint patents the two organiza-tions had jointly filed with the EPO during the fiveyears prior to each examined joint patent’s issuedate. Following Gulati (1995b), we employed a 5-yearmoving window based on previous research suggestingthat the lifespan for alliances is usually no more than fiveyears.

Seventh, in order to account for differences amongour focal companies in terms of the countries in whichthey are based, we included three dummy variabliestaking value one if the focal company’s headquarter waslocated in Japan (Japan), South Korea (SouthKorea), orEurope (Europe), respectively. Europe was the omittedcategory.

Eighth, we controlled for diversity between alliedfirms in terms of the industries in which they operate bya dummy variable (PartnerIndustry) set to one when ourfocal companies’ partners operated in the EEE industry,zero otherwise.

Finally, we included year dummies (Year) and focalfirm dummies (FocComp) in our models. The omittedcategories were Year 1998 and Focal Firm Philips,respectively.

Statistical method

Our dependent variable is a non-negative, integer countvariable, thus violating the OLS assumption of a nor-mally distributed dependent variable. A Poisson regres-sion approach provides a natural baseline model for such



data. However, while the Poisson model assumes thatthe conditional mean and variance of the dependent vari-able are equal, patent data often present over-dispersion.This was also the case for our data. We thereforeemployed an extension of the Poisson estimation, thenegative binomial that we deemed more suitable to ourdata in that it allows for a different mean and varianceof the dependent variable, and thus can handle over-dispersion (Hausman et al., 1984; Cameron and Trivedi,1986). We applied the following specification:

E y x xi i j i i j i j i, , ,, expε α β ε[ ] = + +( )

where yi is a non-negative integer count variable captur-ing innovation performance in each of the i alliances andxi,j describes the j-th independent (control) variable. Weconducted the analysis via the NBREG procedure inSTATA. We report Huber-White robust standard errorsto control for heteroskedasticity issues (White, 1980).

Results

Table 4 displays descriptive statistics and correlations foreach of the variables described above.All the correlationsbetween the independent variables fall well below the0.70 threshold, thus indicating acceptable discriminantvalidity (Cohen et al., 2003). Table 5 reports the resultsfor negative binomial regression of geographic distancebetween partners and their affiliation with the same busi-ness group on the alliance innovative performance.Model 1 depicts the baseline model including the controlvariables only. Models 2 and 3 also include the effects ofGeoDistance and SameGroup, respectively. Both modelsshow significant improvement in fit over model 1. Model4 adds the two independent variables and the interactionterm GeoDistance × SameGroup. It reveals a better fit incomparison with the previous ones. We thus focus onmodel 4. The coefficients and levels of significanceremain consistent across the models. Hence, multicollin-earity is not an issue in our analysis.

Results show that the coefficient for GeoDistance isnegative and significant (β = –0.21, ρ < 0.01), thusconfirming Hypothesis 1. Analogously, the coefficientfor SameGroup is negative and significant (β = –0.27,ρ < 0.001), which supports Hypothesis 2. Finally, thecoefficient for GeoDistance × SameGroup is positive(β = 0.08) and significant, although only at ρ < 0.05,thus supporting Hypothesis 3. Taken together, ourresults indicate that both the geographic distance thatseparates allied firms and their affiliation with the samebusiness group are negatively related to the innovativeperformance of knowledge-creating alliances. However,when jointly considered, the two independent variablesdisplay a positive interaction effect on the dependentvariable, so weakening each other’s negative influenceon alliance innovative performance. Ta

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To illustrate and gain more insight into the interactioneffect between our two independent variables, we con-ducted a simple slope test (Aiken and West, 1991). Weestimated the effect of geographic distance betweenpartners for SameGroup = 1 and SameGroup = 0 respec-tively, and plotted the results in Figure 1. The Figureindicates that the negative effect of geographic distancebetween allied firms on the alliance innovative perfor-mance is weaker when partners are affiliated with thesame business group than when they are not part of the

same group. This suggests that geographic distance andthe existence of group ties between alliance partnersfunction as complements in influencing the innovativeperformance of knowledge-creating alliances. We alsonote that, in our sample, the intersection between the twocurves in Figure 1 corresponds to a very low level ofgeographic distance between partners (about 33 km).7

Regarding the control variables, the focal company’ssize has a significant negative effect on the allianceinnovative performance, while the effect of the focalcompany’s technological capital is significant and posi-tive. The coefficients for both InnovRelatedness andPriorAlliances are positive and significant, suggestingthat relatedness between the focal company’s techno-logical capabilities and the collaborative project at hand,as well as the number of prior alliances between collabo-rating firms, affect positively the innovative performanceof knowledge-creating alliances. Also the coefficient forPartnerIndustry is positive and significant. The effectsof the remaining controls are not significant. Four yeardummies and eight focal firm dummies are statisticallysignificant in relation to the omitted ones.

Robustness tests

We tested the robustness of our results under alternativemodel specifications. Results are shown in Table 6. First,

7The maximum value of GeoDistance in our sample is14,482 km.

Table 5 Negative binomial regression results for alliance innovative performance

Model 1(Baseline)

Model 2 Model 3 Model 4(Comprehensive)

Independent variablesGeoDistance∼ −0.17 (0.09)** −0.21 (0.10)**SameGroup −0.22 (0.05)*** −0.27 (0.07)***GeoDistance∼ × SameGroup 0.08 (0.05)*

Control variablesFocCompSize∼ −1.19 (0.28)*** −1.17 (0.27)*** −1.16 (0.27)*** −1.14 (0.27)***PartnerSize∼ −1.05 (1.00) −0.97 (0.90) −1.01 (0.93) −0.93 (0.93)FocCompTechCapital∼ 0.13 (0.07)** 0.11 (0.05)** 0.11 (0.05)** 0.08 (0.04)**PartTechCapital∼ −0.01 (0.01) −0.01 (0.01) −0.01 (0.01) −0.01 (0.01)TeamSize −0.01 (0.00) −0.01 (0.00) −0.01 (0.01) −0.01 (0.01)InnovRelatedness 0.64 (0.33)** 0.50 (0.25)** 0.59 (0.29)** 0.48 (0.24)**TechProximity 0.14 (0.25) 00.11 (0.25) 0.11 (0.25) 0.09 (0.25)PriorAlliances 0.18 (0.11)* 0.13 (0.07)* 0.13 (0.07)* 0.11 (0.06)*Japan −0.02 (0.10) −0.02 (0.10) −0.01 (0.10) −0.01 (0.10)SouthKorea −0.03 (0.17) −0.03 (0.16) −0.03 (0.16) −0.03 (0.16)PartnerIndustry 0.21 (0.12)* 0.19 (0.10)* 0.17 (0.09)* 0.17 (0.09)*Year dummies (5) 4 years*** 4 years*** 4 years*** 4 years***FocComp dummies (9) 8 companies** 8 companies** 8 companies** 8 companies**Log pseudo-likelihood −2110.52 −2105.35 −2103.47 −2100.31Improvement over base (Δ χ2) – 5.17 7.05 10.21No. of Obs. 1,515 1,515 1,515 1,515

*p < .05; **p < .01; ***p < .001.∼Logged variables.

2 4 6 8 10

3

6

9

GeoDistance

InnPerf

Same Group = 0

Same Group = 1

Figure 1 Interaction effects of geographic distance between partnersand same group affiliation on alliance innovative performance



in order to deal with the ‘excess zero’ issue afflicting ourdata, we used zero-inflated negative binomial regressionand found support to our hypotheses. Results arereported in Table 6, model 1.

Second, in order to account for endogeneity issues,we ran a two-stage model. We reasoned that firms, ratherthan assigning R&D projects to geographic locationsand to partners inside or outside their groups in a randomfashion, tend to make these decision strategically, basedon specific criteria such as the projects’ perceivedimportance. This would introduce a selection bias in ouranalysis. Therefore, we resorted to the Heckman model(Heckman, 1979), a two-step statistical approach whichallows us to correct for non-randomly selected samples.In the first stage of analysis, we used a probit model toassess the likelihood that an alliance was consideredmore important, which in turn was expressed by adummy variable taking value one if the family size of theresulting joint patent exceeded the average joint patentfamily size in our sample, that is, 7.93. Results arereported in Table 6, model 2. Patent family size, namely,the number of jurisdictions where a patent grant hasbeen sought, has been shown to capture the extent towhich firms invest money to protect their patented inno-vations (Lanjouw et al., 1998; Bonaccorsi and Thoma,2007), thus providing useful information about the per-ceived importance of the underlying innovative projects.The number of claims of each joint patent was employedas the instrumental variable in the first stage. Indeed,previous studies have shown that a higher number of

claims indicates that an innovation is of greater potentialprofitability (e.g., Lanjouw and Schankerman, 2004;Reitzig, 2004), and hence of greater perceived impor-tance. Finally, the inverse Mills ratio estimates resultingfrom the first-stage model were incorporated in thesecond-stage model. Results lend further support to ourthree hypotheses (see Table 6, model 3).

Third, we considered alternative operationalizationsof two key variables in our analysis. Specifically, wemeasured alliance-level innovative performance byincluding self-citations, and geographic distancebetween partner as a dummy variable taking value one ifthe partners were located in different countries, zerootherwise. In both cases, results remained consistentwith our predictions.

Discussion

Strategic alliances can be valuable sources of knowledgeand innovation, and in so doing, of competitive advan-tage. However, their ability to do so is subject to anumber of contingencies that have been only partlyexamined so far. In order to contribute in such a direc-tion, we have focused on the role of partner geographicand organizational proximity, and specifically on geo-graphic distance between alliance partners and theiraffiliation with the same business group, to investigatetheir impact on the innovative performance of a specificcategory of knowledge-intensive alliances, namely,

Table 6 Robustness analyses

Model 1 Model 2 Model 3(zero-inflated negative binomial) (Heckman first-stage model) (Heckman second-stage model)

Independent variablesGeoDistance∼ −0.26 (0.13)** −0.39 (0.22)*SameGroup −0.30 (0.15)** −0.44 (0.24)*GeoDistance∼ x SameGroup 0.11 (0.06)* 0.15 (0.08)*

Control variablesFocCompSize∼ −1.13 (0.26)*** −1.12 (0.62)* −2.25 (1.12)**PartnerSize∼ −0.98 (0.94) −0.72 (0.71) −1.07 (1.02)FocCompTechCapital∼ 0.11 (0.05)** 0.19 (0.10)* 0.17 (0.08)**PartTechCapital∼ −0.01 (0.01) −0.04 (0.04) −0.07 (0.07)TeamSize −0.01 (0.01) −04 (0.04) −0.06 (0.05)InnovRelatedness 0.51 (0.28)* 0.60 (0.30)** 0.91 (0.50)*TechProximity 0.06 (0.11) 0.12 (0.30) 0.12 (0.28)PriorAlliances 0.13 (0.08)* 0.11 (0.07) 0.25 (0.14)*Japan −0.01 (0.10) −0.02 (0.10) −0.07 (0.72)SouthKorea −0.03 (0.17) −0.04 (0.16) −0.9 (0.84)PartnerIndustry 0.15 (0.09)* 0.13 (0.07)* 0.27 (0.15)*Year dummies (5) 4 years*** 4 years** 4 years***FocComp dummies (9) 8 companies** 8 companies** 8 companies**

Claims – 0.08 (0.05)* –Correction for self-selection (λ) – – −1.08 (0.81)No. of Obs. 1,515 1,515 1,515

*p < .05; **p < .01; ***p < .001.∼Logged variables.



knowledge-creating alliances. In doing so, we haveadopted a knowledge-based view of interorganizationalcollaboration, and in particular a ‘learning and knowl-edge approach’ to analyzing the relationships betweenalliances and innovation (Di Guardo and Harrigan,2012).

Our econometric analysis shows that, when individ-ually considered, the two examined partner characteris-tics negatively influence innovation performance at thealliance level. On the one hand, effective combination ofgeographically distant knowledge is quite hard toachieve in practice, especially at the interorganizationallevel. Geographic distance undermines the developmentof trust-based interorganizational relationships, limitsthe partners’ ability to develop and adjust over time theidiosyncratic languages needed for the sharing of tacitand holistic knowledge, and impedes the developmentof interorganizational routines for effective knowledge-intensive collaboration. On the other hand, the lowerdegree of knowledge diversity that typically character-izes alliances between firms affiliated with the samebusiness group hampers innovation. When partnersbring to their alliances knowledge resources shapedby similar assumptions and cultural values and embed-ded into similar practices and organizational routines,the alliance’s innovative potential significantly de-creases. In addition, alliances between firms belongingto the same group entails for the participating organiza-tions the danger of remaining locked-in into the allianceand locked-out of external knowledge flows, withdetrimental effects on their ability to jointly developinnovation.

Nevertheless, analyzing the interactions of geo-graphic distance between partners and their affiliation tothe same business group, we have found that they influ-ence one another’s effect on the innovative performanceof knowledge-creating alliances. On the one hand,organizational proximity and the richer networks ofinterpersonal relationships that characterize firmsbelonging to the same business group lessen the obsta-cles to knowledge sharing and combination, and reducethe threat of opportunism, that afflict alliances betweenspatially distant firms. On the other hand, collaboratingwith geographically distant partners can offer access to amore heterogeneous set of resources, so increasing theopportunities to develop new knowledge combinationsand reducing the risk of relational inertia and cognitivelock-in associated with the existence of group tiesbetween partners.

Boundary conditions

The specific features of the innovative task that charac-terizes knowledge-creating alliances (i.e., joint knowl-edge creation) set the boundary conditions for ourtheoretical contribution. Knowledge creation in alli-

ances involves high reciprocal interdependence and sorequires on-going mutual adjustment between the par-ticipating organizations (Krishnan et al., 2006). Thisentails continuous exchange of complex and tacitknowledge (Sampson, 2007), which in turn asks for thedevelopment of a rich social fabric of trust-based rela-tionships across the partnered organizations’ boundaries(Capaldo, 2014). All this can be difficult at long spatialdistance, however (Audretsch, 1998; Maskell andMalmberg, 1999). Thus, knowledge-creating alliancesbetween distant partners and the ensuing innovative out-comes tend to benefit from collaboration with membercompanies of the same business group, which allows fora higher relative absorptive capacity and for lower risksof opportunism by the participating organizations, andin the end for easier knowledge integration at theinterorganizational level. Moreover, since new knowl-edge creation requires partners to explore new combina-tions of heterogeneous knowledge, collaborationbetween firms affiliated with the same group may sufferfrom a lower innovative potential. In such circum-stances, high geographic distance between partners canprovide the alliance with the heterogeneous knowledgeresources needed for successful innovation.

We conjecture that the two partner characteristicsexamined here are likely to have a different impact onperformance in alliances other than knowledge-creating,and knowledge-intensive more generally, such as alli-ances set up to share production facilities or market-access alliances, that are characterized by lower levels ofinterdependence, of a pooled or sequential nature(Borys and Jemison, 1989; Gulati and Singh, 1998). Thecase of geographic distance between partners is illustra-tive. Since these alliances require partners to share tan-gible assets and exchange large amounts of codifiedknowledge (i.e., information), rather than continuousinterorganizational interaction rich in tacit knowledge,standardization and standard communication systemsmay well substitute for spatial proximity in ensuringcoordination (Presutti et al., 2013), so limiting the lia-bilities of geographic distance. More generally, thesearguments also remind us that the impact of partnercharacteristics on alliance performance is contingentupon the specific tasks to be performed within the alli-ance (Li et al., 2008; Shah and Swaminathan, 2008).

Theoretical and managerial implications

Our study has intriguing implications both for theknowledge-based view of strategic alliances and forproximity research. First, we have adhered to thedistinction between three different categories ofknowledge-intensive alliances, namely, knowledge-accessing, knowledge transfer, and knowledge-creatingalliances based on the alliance’s underlying primarylogic of value creation (Capaldo, 2010). Focusing on



knowledge-creating alliances, we have argued that thedegree of knowledge diversity and the ability to integratesuch knowledge at the interorganizational level are twomajor drivers of their innovative performance. However,our study suggests that these two drivers are subject toimportant trade-offs, in that searching for more hetero-geneous knowledge may reduce the ability of alliedorganizations to effectively integrate such knowledgewithin the alliance, while focusing on knowledge inte-gration may be at the expense of knowledge diversity. Ina first attempt to examine such trade-offs and how toface them, we advance that geographic distance betweenpartners and their belonging to the same business groupmoderate each other’s negative effect on knowledgeintegration and diversity respectively.

Second, our research should also be seen in the lightof the growing literature on different dimensions ofproximity and the linkages between them (Boschma,2005). Our findings suggest that, on the one hand, theimpact of geographic distance between partners onalliance-level innovation is contingent upon proximityin organizational terms, and specifically becomes lessnegative when partners are at low organizational dis-tance, while, on the other hand, the impact oforganizational proximity between partners is contingentupon geographic proximity, and specifically becomesless negative when partners are at high spatial distance.Therefore, our study contributes to the idea that distance(proximity) in one dimension can be bridged (over-come) by proximity (distance) in another dimension,and in particular corroborates the conclusion that,in knowledge-intensive relationships, a compensationmechanism is at work between different types of prox-imity (Huber, 2012).

The present study makes a number of further contri-butions to both research and practice. To those interestedin alliance partner selection (Geringer and Killing, 1988;Li et al., 2008) our study reminds that, while severalpartner characteristics may account for alliance success(e.g., Harrigan, 1988; Saxton, 1997; Sarkar et al., 2001),the possible existence of complementary or substitutiveeffects between them should be taken into account. Wehave shown that, when it comes to knowledge-creatingalliances and their innovative performance, geographicdistance between partners and their belonging to thesame business group function as complements, in thatincreasing geographic distance (the existence of groupties) weakens the negative effect of the existence ofgroup ties (increasing geographic distance). Thus,looking individually at the partner characteristics exam-ined in the present study may be misleading. Based onthe main effect of the existence of group ties betweenpartners on the alliance innovative performance, onemay be tempted to conclude that firms should alwaysally with partners outside their group. However, ourinteraction analysis suggests that, while the above con-

clusion may hold at very low geographic distancebetween partners (see Figure 1), as distance increases,the alliance is better off if the participating organizationsare affiliated with the same group. This has relevantmanagerial implications for alliance partner selection.When evaluating whether allying with partners inside oroutside their firms’ business groups, practicing alliancemanagers are advised to be aware of their potential part-ners’ geographic distance, in that collaborating with spa-tially distant others may allow for more knowledgediversity, thus reducing the dangers of collaboratingwith organizationally proximate firms. Moreover, whencreating alliances with other firms at long spatial dis-tance, alliance managers would do better to search forpartners inside their groups, thus capitalizing on theability of organizational proximity to reduce the diffi-culties of integrating knowledge across geographicallydistant organizations.

For those interested in the relationships between busi-ness groups and innovation, our study is unique in that itoffers an account of the ability of same business groupaffiliation to influence innovative performance at thealliance level. Based on our findings, we concur withLincoln and Guillot (2009) that, when innovation is thegoal, smooth coordination and easy of knowledge inte-gration take a back seat to knowledge diversity, soleading to the negative influence of same-group affilia-tion on the alliance innovative performance. Neverthe-less, we add that, when allied firms are geographicallydistant, being part of a same group turns out to be posi-tive for innovation by lessening the liabilities of longspatial distance between partners.

Limitations

This study has some limitations that should be consid-ered when interpreting its results. First, although wehave discussed the impact of geographic distance byfocusing on physical or spatial distance between part-nered firms, benefits and liabilities associated withallying with geographically distant organizations mayalso be dependent upon cultural differences betweendistant geographic countries/regions (Hofstede, 1980).In some contexts, cultural distance between partnersmight even be more important than mere spatial distancefor interorganizational knowledge sharing and combina-tion, as well as for remedying the limitations of collabo-rating with partners affiliated with the same businessgroup. Hence, further research is needed to separate, andcompare, the impact of spatial and cultural distance onalliance innovative performance.

Second, the use of patent data has some obviousweaknesses. Patenting is a coarse measure of firms’innovative activity and output, whose limitations havebeen pointed out by previous scholars (e.g., Gittelman,2008). In particular, not all innovations are patentable,



and not all patents represent innovations. However,being systematically compiled, offering detailed infor-mation, and being continuously available across time(Rosenkopf and Almeida, 2003), patents are still themost commonly used proxy for innovations (e.g.,Rosenkopf and Nerkar, 2001; Miller et al., 2007).

Finally, we included in our sample only alliances thathad generated positive innovation output. This intro-duced a success bias in our analysis. Moreover, the useof joint patents to identify the sample alliances furtherbiased sample selection by excluding from our studyknowledge-creating R&D alliances that, although suc-cessful, did not yield joint patents. However, based onHagedoorn et al. (2003), we consider that filing a jointpatent may be seen as an important landmark signify-ing the successful completion of interorganizationalcooperative R&D aimed at the joint development of newknowledge. We therefore believe that the extent of thisbias is not severe, yet it limits the generalizability of ourresults to knowledge-creating alliances operating inindustries wherein firms are less inclined to (joint)patent.

Acknowledgements

An earlier version of this paper was presented at the2009 Strategic Management Society Annual Interna-tional Conference – October 11–14, Washington DC. Wealso thank participants to a ‘Second Tuesday’ seminar atthe Catholic University of the Sacred Heart in Rome foruseful suggestions.

ReferencesAhuja, G., 2000, “The duality of collaboration: inducements

and opportunities in the formation of interfirm linkages”.Strategic Management Journal, 21: 317–343.

Ahuja, G. and C. M. Lampert, 2001, “Entrepreneurship in thelarge corporation: A longitudinal study of how establishedfirms create breakthrough inventions”. Strategic ManagementJournal, 22: 521–543.

Aiken, L. S. and S. G. West, 1991, Multiple regression: Testingand interpreting interactions. Newbury Park, CA: Sage.

Amin, A. and F. Wilkinson, 1999, “Learning, proximity andindustrial performance: An introduction”. Cambridge Journalof Economics, 23: 121–125.

Arino, A., J. de la Torre and P. Smith Ring, 2005, “Relationalquality and inter-personal trust in strategic alliances”. Euro-pean Management Review, 2: 15–27.

Arora, A. and A. Gambardella, 1994, “The changing technol-ogy of technological change: general and abstract knowledgeand the division of innovative labour”. Research Policy, 23:523–532.

Audretsch, D. B., 1998, “Agglomeration and the location ofinnovative activity”. Oxford Review of Economic Policy, 14:18–29.

Badaracco, J. L., 1991, The Knowledge Link: How FirmsCompete through Strategic Alliances. Cambridge, MA:Harvard Business School Press.

Baptista, R. and P. Swann, 1998, “Do firms in clusters innovatemore?” Research Policy, 27: 525–540.

Barney, J., 1991, “Firm resources and sustained competitiveadvantage”. Journal of Management, 17: 99–120.

Baudry, M. and B. Dumont, 2006, “Comparing firms’ triadicpatent applications across countries: Is there a gap in terms ofR&D effort or a gap in terms of performances?”. ResearchPolicy, 35: 324–342.

Baum, J. A. C., T. Calabrese and B. S. Silverman, 2000,“Don’t go it alone. Alliance network composition and start-up’s performance in Canadian biotechnology”. StrategicManagement Journal, 21: 267–294.

Belenzon, S. and T. Berkovitz, 2010, “Innovation in businessgroups”. Management Science, 56: 519–535.

Bell, G. G., 2005, “Clusters, networks, and firminnovativeness”. Strategic Management Journal, 26: 287–295.

Bell, G. G. and A. Zaheer, 2007, “Geography, networks, andknowledge flow”. Organization Science, 18: 955–972.

Besley, S. and E. F. Brigham, 2008, Essentials of managerialfinance (14th edn.). Gainesville, FL: South-Western.

Bonaccorsi, A. and G. Thoma, 2007, “Institutional comple-mentarity and inventive performance in nano science andtechnology”. Research Policy, 36: 813–831.

Borys, B. and D. B. Jemison, 1989, “Hybrid arrangementsas strategic alliances: Theoretical issues in organizationalcombinations”. Academy of Management Review, 14: 234–249.

Boschma, R. A., 2005, “Proximity and innovation: A criticalassessment”. Regional Studies, 39: 61–74.

Cameron, A. C. and P. K. Trivedi, 1986, “Econometric modelsbased on count data: Comparisons and applications of someestimators and tests”. Journal of Applied Econometrics, 1:29–53.

Capaldo, A., 2007, “Network structure and innovation: Theleveraging of a dual network as a distinctive relational capa-bility”. Strategic Management Journal, 28: 585–608.

Capaldo, A., 2010, Knowledge access, transfer, and coproduc-tion. A knowledge-based view of interorganizational collabo-rative relationships. Working Paper, Catholic University ofthe Sacred Heart, Milan and Rome.

Capaldo, A., 2014, “Network governance. A cross-level studyof social mechanisms, knowledge benefits, and strategic out-comes in joint-design alliances”. Industrial Marketing Man-agement, forthcoming.

Capaldo, A. and A. Messeni Petruzzelli, 2011, “In search ofalliance-level relational capabilities: Balancing innovationvalue creation and appropriability in R&D alliances”. Scan-dinavian Journal of Management, 27: 273–286.

Capello, R., 2009, “Spatial spillovers and regional growth: Acognitive approach”. European Planning Studies, 17: 639–658.

Cattani, G., 2005, “Preadaptation, firm heterogeneity, and tech-nological performance: a study on the evolution of fiberoptics, 1970–1995”. Organization Science, 16: 563–580.

Chang, S.-J. and U. Choi, 1988, “Strategy, structure and perfor-mance of Korean business groups: A transactions costapproach”. The Journal of Industrial Economics, 37: 141–158.



Chang, S.-J., C.-N. Chung and Is. P. Mahmood, 2006, “Whenand how does business group affiliation promote firm innova-tion? A tale of two emerging economies”. OrganizationScience, 17: 637–656.

Chang, S.-J. and J. Hong, 2000, “Economic performance ofgroup-affiliated companies in Korea: Intragroup resourcesharing and internal business transactions”. Academy of Man-agement Journal, 43: 429–448.

Chesbrough, H., 2003, Open innovation: The new imperativefor creating and profiting from technology. Cambridge, MA:Harvard Business School Press.

Coff, R., 2003, “Bidding wars over R&D-intensive firms:Knowledge, opportunism, and the market for corporatecontrol”. Academy of Management Journal, 46: 74–85.

Cohen, P., J. Cohen, S. G. West and L. S. Aiken, 2003,Applied multiple regression/correlation analysis for thebehavioral sciences (3rd edn.). Hillsdale, NJ: Erlbaum.

Cohen, W. M. and R. C. Levin, 1989, “Empirical studies ofinnovation and market structure”. In R. Schmalensee and R.Willig (eds.) Handbook of industrial organization. Amster-dam: Elsevier, pp. 1059–1107.

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

Colombo, M., 2003, “Alliance form: a test of the contractualand competence perspectives”. Strategic ManagementJournal, 24: 1209–1229.

Dacin, M. T., M. A. Hitt and E. Levitas, 1997, “Selectingpartners for successful international alliances: Examinationof US and Korean firms”. Journal of World Business, 32:3–16.

Deeds, D. L and C. W. Hill, 1999, “An examination of oppor-tunistic action within research alliances: Evidence from thebiotechnology industry”. Journal of Business Venturing, 14:141–163.

de Faria, P., F. Lima and R. Santos, 2010, “Cooperation ininnovation activities: The importance of partners”. ResearchPolicy, 39: 1082–1092.

Di Guardo, M. C. and K. R. Harrigan, 2012, “Mappingresearch on strategic alliances and innovation: A co-citation analysis”. Journal of Technology Transfer, 37: 789–811.

Duysters, G. and J. Hagedoorn, 1996, “Internationalization ofcorporate technology through strategic partnering: An empiri-cal investigation”. Research Policy, 25: 1–12.

Dyer, J. H. and N. W. Hatch, 2006, “Relation-specific capa-bilities and barriers to knowledge transfers: Creating advan-tage through network relationships”. Strategic ManagementJournal, 27: 701–719.

Dyer, J. H. and H. Singh, 1998, “The relational view: Coopera-tive strategy and sources of interorganizational competitiveadvantage”. Academy of Management Review, 23: 660–679.

Eisenhardt, K. M. and F. M. Santos, 2002, “Knowledge-basedview: A new theory of strategy?” In A. Pettigrew, H. Thomasand R. Whittington (eds.) Handbook of strategy and manage-ment. London: Sage, pp. 139–164.

Feldman, M. P., 1994, The geography of innovation. Dordrecht:Kluwer Academic Publishers.

Fleming, L., 2002, “Finding the organizational sources of tech-nological breakthroughs. The story of Hewlett-Packard

thermal ink-jet”. Industrial and Corporate Change, 11: 1059–1084.

Folta, T. B., A. C. Cooper and Y.-S. Baik, 2006, “Geographiccluster size and firm performance”. Journal of Business Ven-turing, 21: 217–242.

Freel, M. S., 2003, “Sectoral patterns of small firm innovation,networking and proximity”. Research Policy, 32: 751–770.

Frost, T. S. and C. Zhou, 2005, “R&D co-practice and ‘reverse’knowledge integration in multinational firms”. Journal ofInternational Business Studies, 36: 676–687.

Ganesan, S., A. J. Malter and A. Rindfleisch, 2005, “Doesdistance still matter? Geographic proximity and new productdevelopment”. Journal of Marketing, 69: 44–60.

Gargiulo, M. and M. Benassi, 2000, “Trapped in your own net?Network cohesion, structural holes, and the adaptation ofsocial capital”. Organization Science, 11: 183–196.

Geringer, J. M. and P. J. Killing, 1988, Joint venture partnerselection: Strategies for developed countries. New York:Quorum Books.

Gertler, M. S., 1995, “Being there: Proximity, organization, andculture in the development and adoption of advanced manu-facturing technologies”. Economic Geography, 71: 1–26.

Ghemawat, P. and T. Khanna, 1998, “The nature of diversifiedbusiness groups: A research design and two case studies”.Journal of Industrial Economics, 46: 35–61.

Gittelman, M., 2008, “A note on the value of patents as indi-cators of innovation: Implications for management research”.Academy of Management Perspective, 22: 21–27.

Gomes-Casseres, B., J. Hagedoorn and A. B. Jaffe, 2006, “Doalliances promote knowledge flows?” Journal of FinancialEconomics, 80: 5–33.

Granovetter, M., 2005, “Business groups and social organiza-tion”. In N. Smelser and R. Swedberg (eds.) Handbook ofeconomic sociology. Princeton, NJ: Princeton UniversityPress, pp. 429–450.

Grant, R., 1996, “Toward a knowledge-based theory of thefirm”. Strategic Management Journal, 17: 109–122.

Grant, R. and C. Baden-Fuller, 2004, “A knowledge accessingtheory of strategic alliances”. Journal of ManagementStudies, 41: 61–84.

Griliches, Z., 1979, “Issues in assessing the contribution ofresearch and development to productivity growth”. The BellJournal of Economics, 10: 92–116.

Grunwald, R. and A. Kieser, 2007, “Learning to reduceinterorganizational learning: An analysis of architecturalproduct innovation in strategic alliances”. Journal of ProductInnovation Management, 24: 369–391.

Gulati, R., 1995a, “Does familiarity breed trust? The implica-tions of repeated ties for contractual choice in alliances”.Academy of Management Journal, 38: 85–112.

Gulati, R., 1995b, “Social structure and alliance formation pat-terns: A longitudinal analysis”. Administrative Science Quar-terly, 40: 619–652.

Gulati, R. and H. Singh, 1998, “The architecture of coopera-tion: Managing coordination uncertainty and interdependencein strategic alliances”. Administrative Science Quarterly, 43:781–814.

Hagedoorn, J., 2002, “Inter-firm R&D partnerships: An over-view of major trends and patterns since 1960”. ResearchPolicy, 31: 477–492.



Hagedoorn, J., 2003, “Sharing intellectual property rights – anexploratory study of joint patenting amongst companies”.Industrial and Corporate Change, 5: 1035–1050.

Hagedoorn, J. and G. Duysters, 2002, “External sources ofinnovative capabilities: The preference for strategic alliancesor mergers and acquisitions”. Journal of ManagementStudies, 39: 167–188.

Hagedoorn, J., H. van Kranenburg and R. N. Osborn, 2003,“Joint patenting amongst companies – exploring the effects ofinter-firm R&D partnering and experience”. Managerial andDecision Economics, 24: 71–84.

Hall, B. H., A. B. Jaffe and M. Trajtenberg, 2005, “Marketvalue and patent citations”. RAND Journal of Economics, 36:16–38.

Hamel, G., 1991, “Competition for competence and interpartnerlearning within international strategic alliances”. StrategicManagement Journal, 12: 83–103.

Hamel, G., Y. L. Doz and C. K. Prahalad, 1989, “Collaboratewith your competitors – and win”. Harvard Business Review,67: 133–139.

Hansen, M. T., 1999, “The search-transfer problem: The role ofweak ties in sharing knowledge across organization subunits”.Administrative Science Quarterly, 44: 82–111.

Harhoff, D., F. M. Scherer and K. Vopel, 2003, “Citations,family size, opposition and the value of patent rights”.Research Policy, 32: 1343–1363.

Harrigan, K. R., 1988, “Strategic alliances and partner asym-metries”. Management International Review, 28: 53–72.

Hausman, J., B. B. Hall and Z. Griliches, 1984, “Econometricmodels for count data with an application to the patents-R&Drelationship”. Econometrica, 52: 909–938.

Heckman, J. J., 1979, “Sample selection bias as a specificationerror”. Econometrica, 47: 153–161.

Henderson, R. and K. B. Clark, 1990, “Architectural innova-tion: The reconfiguration of existing product technologies andfailure of established firms”. Administration Science Quar-terly, 35: 9–30.

Hicks, D. and F. Narin, 2001, “Strategic research alliances and360 degree bibliometric indicators”. In J. E. Jankowski, A. N.Link and N. S. Vonortas (eds.) Strategic research partner-ships. Proceedings from an NSF Workshop. Arlington, VA:National Science Foundation, pp. 133–145.

Hoang, H. and F. T. Rothaermel, 2005, “The effect of generaland partner-specific alliance experience on joint R&D projectperformance”. Academy of Management Journal, 48: 332–345.

Hofstede, G., 1980, Culture’s consequences: International dif-ferences in work-related values. Beverly Hills, CA: Sage.

Hsieh, T.-J., R.-S. Yeh and Y.-J. Chen, 2010, “Business groupcharacteristics and affiliated firm innovation: The case ofTaiwan”. Industrial Marketing Management, 39: 560–570.

Huber, F., 2012, “On the role and interrelationship of spatial,social and cognitive proximity: Personal knowledge relation-ships of R&D workers in the Cambridge IT Cluster”.Regional Studies, 49: 1169–1182.

Ingram, P. and P. Roberts, 2000, “Friendship among competi-tors in the Sidney hotel industry”. American Journal of Soci-ology, 106: 387–423.

Inkpen, A. C., 1996, “Creating knowledge through collabora-tion”. California Management Review, 39: 123–140.

Inkpen, A. C., 1998, “Learning, knowledge acquisition, andstrategic alliances”. European Management Journal, 16: 223–229.

Inkpen, A. C. and M. M. Crossan, 1995, “Believing is seeing:joint ventures and organization learning”. Journal of Manage-ment Studies, 32: 595–618.

Inkpen, A. C. and E. W. K. Tsang, 2005, “Social capital,networks, and knowledge transfer”. Academy of ManagementJournal, 30: 146–155.

Jaffe, A. B., M. Trajtenberg and R. Henderson, 1993, “Geo-graphic localization of knowledge spillovers as evidence bypatent citations”. The Quarterly Journal of Economics, 108:577–598.

Kale, P., H. Singh and H. Perlmutter, 2000, “Learning asprotection of proprietary assets in strategic alliances: Buildingrelational capital”. Strategic Management Journal, 21: 217–237.

Khanna, T., R. Gulati and N. Nohria, 1998, “The dynamics oflearning alliances: Competition, cooperation, and relativescope”, Strategic Management Journal, 19: 193–210.

Khanna, Tarun and Krishna Palepu, 2000, “The futureof business groups in emerging markets: Long-run evidencefrom Chile”. Academy of Management Journal, 43: 268−285.

Khanna, T. and Y. Yafeh, 2007, “Business groups in emergingmarkets: Paragons or parasites?” Journal of Economic Litera-ture, 45: 331–372.

Kim, C. and J. Song, 2007, “Creating new technology throughalliances: An empirical investigation of joint patents”.Technovation, 27: 461–470.

Kirat, T. and Y. Lung, 1999, “Innovation and proximity. Ter-ritories as loci of collective learning processes”. EuropeanUrban and Regional Studies, 6: 27–38.

Knoben, J. and L. A. G. Oerlemans, 2006, “Proximity andinter-organizational collaboration: A literature review”. Inter-national Journal of Management Reviews, 8: 71–89.

Kogut, B., 1989, “The stability of joint ventures: reciprocity andcompetitive rivalry”. Journal of Industrial Economics, 38:183–198.

Kogut, B. and U. Zander, 1992, “Knowledge of the firm,combinative capabilities, and the replication of technology”.Organization Science, 3: 383–397.

Kotabe, M. and S. K. Swan, 1995, “The role of strategicalliances in high-technology new product development”.Strategic Management Journal, 16: 621–636.

Krishnan, R., X. Martin and N. G. Noorderhaven, 2006,“When does trust matter to alliance performance?” Academyof Management Journal, 49: 894–917.

Kumar, R. and K. O. Nti, 1998, “Differential learningand interaction in alliance dynamics: A process and outcomediscrepancy model”. Organization Science, 9: 356–367.

Lane, P. J. and Michael Lubatkin, 1998, “Relative absorptivecapacity and interorganizational learning”. Strategic Manage-ment Journal, 19: 461–477.

Lanjouw, J. O., A. Pakes and J. Putnam, 1998, “How to countpatents and value intellectual property. The uses of patentrenewal and application data”. Journal of Industrial Econom-ics, 46: 405–432.

Lanjouw, Jean O. and Mark Schankerman, 2004, “Patentquality and research productivity: Measuring innovation withmultiple indicators”. The Economic Journal, 114: 441–465.



Larsson, R., L. Bengtsson, K. Henriksson and J. Sparks,1998, “The interorganizational learning dilemma: Collectiveknowledge development in strategic alliances”. OrganizationScience, 9: 285–305.

Levin, R. C., A. K. Klevorick, R. Nelson and S. Winter, 1987,“Appropriating the returns from industrial research and devel-opment”. Brookings Papers on Economic Activity, 3: 783–820.

Levitt, B. and J. C. March, 1988, “Organizational learning”.Annual Review of Sociology, 14: 319–340.

Li, D., L. Eden, M. A. Hitt and D. R. Ireland, 2008, “Friends,acquaintances, or strangers? Partner selection in R&D alli-ances”. Academy of Management Journal, 51: 315–334.

Lincoln, J. R, C. Ahmadjian and E. Mason, 1998,“Organizational learning and purchase-supply relations inJapan: Hitachi, Matsushita, and Toyota compared”. Califor-nia Management Review, 40: 241–264.

Lincoln, J. R. and D. Guillot, 2009, “Innovation and change inthe process of alliance formation in the Japanese electronicsindustry”. Center for Economic Institutions Working PaperSeries No, 2008-18, Hitotsubashi University, Tokyo.

Mahmood, I. P. and W. Mitchell, 2004, “Two faces: Effects ofbusiness groups on innovation in emerging economies”. Man-agement Science, 50: 1348–1365.

Masiello, B., F. Izzo and C. Canoro, 2013, “The structural,relational and cognitive configuration of innovationnetworks between SMEs and public research organisa-tions”. International Small Business Journal, doi: 10.1177/0266242613485610.

Maskell, P. and A. Malmberg, 1999, “Localised learning andindustrial competitiveness”. Cambridge Journal of Econom-ics, 23: 167–185.

McEvily, B. and A. Marcus, 2005, “Embedded ties and theacquisition of competitive capabilities”. Strategic Manage-ment Journal, 26: 1033–1055.

Messeni Petruzzelli, A., 2011, “The impact of technologicalrelatedness, prior ties, and geographical distance onuniversity-industry collaborations: A joint patent analysis”.Technovation, 31: 309–319.

Meyer-Krahmer, F. and G. Reger, 1999, “New perspectives onthe innovation strategies of multinational enterprises: lessonsfor technology policy in Europe”. Research Policy, 28: 751–766.

Miller, D. J., M. J. Fern and L. B. Cardinal, 2007, “The useof knowledge for technological innovation within diversifiedfirms”. Academy of Management Journal, 50: 308–326.

Mowery, D. C., J. E. Oxley and B. S. Silverman, 1996, “Stra-tegic alliances and interfirm knowledge transfer”. StrategicManagement Journal, 17: 77–91.

Narula, R., 2004, “R&D collaboration by SMEs: New oppor-tunities and limitations in the face of globalization”.Technovation, 24: 153–161.

Narula, R. and J. Hagedoorn, 1999, “Innovating through stra-tegic alliances: moving towards international partnerships andcontractual agreements”. Technovation, 19: 283–294.

Nelson, R. and S. Winter, 1982, An evolutionary theory ofeconomic change. Cambridge, MA: Harvard UniversityPress.

Nooteboom, B., W. Van Haverbeke, G. Duysters, V. Gilsingand A. van den Oord, 2007, “Optimal cognitive distance andabsorptive capacity”. Research Policy, 36: 1016–1034.

Oerlemans, L. and M. Meeus, 2005, “Do organizational andspatial proximity impact on firm performance?” RegionalStudies, 39: 89–104.

Oerlemans, L., M. Meeus and F. M. W. Boekema, 2000,“Innovation and proximity: Theoretical perspectives”. In M.B. Greenand and R. B. McNaughton (eds.) Industrial net-works and proximity. Aldershot: Ashgate, pp. 16–47.

Osborn, R. N. and J. Hagedoorn, 1997, “The institutionaliza-tion and evolutionary dynamics of interorganizational alli-ances and networks”. Academy of Management Journal, 40:261–278.

Owen-Smith, J. and W. W. Powell, 2004, “Knowledge net-works as channels and conduits: The effects of spilloversin the Boston biotechnology community”. OrganizationScience, 15: 5–21.

Oxley, J. E., 1997, “Appropriability hazards and governance instrategic alliances: A transaction cost approach”. Journal ofLaw, Economics, and Organization, 13: 387–409.

Oxley, J. E. and R. C. Sampson, 2004, “The scope and thegovernance of international R&D alliances”. Strategic Man-agement Journal, 25: 723–749.

Park, S. H. and G. R. Ungson, 1997, “The effect of nationalculture, organizational complementarity, and economic moti-vation on joint venture dissolution”. Academy of ManagementJournal, 40: 279–307.

Peteraf, M. A., 1993, “The cornerstones of competitiveadvantage: A resource-based view”. Strategic ManagementJournal, 14: 179–191.

Phene, A. and P. Almeida, 2008, “Innovation in multinationalsubsidiaries: The role of knowledge assimilation and subsidi-ary capabilities”. Journal of International Business Studies,39: 901–919.

Phene, A., K. Fladmoe-Lindquist and L. Marsh, 2006,“Breakthrough innovations in the US biotechnology industry:The effects of technological space and geographic origin”.Strategic Management Journal, 27: 369–388.

Powell, W. W., K. W. Koput and L. Smith-Doerr, 1996,“Interorganizational collaboration and the locus of innova-tion: Networks of learning in biotechnology”. AdministrativeScience Quarterly, 41: 116–145.

Presutti, M., C. Boari and A. Majocchi, 2013, “Inter-organizational geographical proximity and local start-ups’knowledge acquisition: a contingency approach”. Entrepre-neurship & Regional Development, 25: 446–467.

Reitzig, M., 2004, “Improving patent valuations for manage-ment purposes – validating new indicators by analyzing appli-cation rationales”. Research Policy, 33: 939–957.

Rindfleisch, A. and C. Moorman, 2001, “The acquisitionand utilization of information in new product alliances:a strength-of-ties perspective”. Journal of Marketing, 65:1–18.

Rocha, F., 1999, “Interfirm technological cooperation: Effectsof absorptive capacity, firm-size and specialization”. Econom-ics of Innovation & New Technology, 8: 253–271.

Romo, F. P. and M. Schwartz, 1995, “The structuralembeddedness of business decisions: The migration of manu-facturing plants in New York state, 1960 to 1985”. AmericanSociological Review, 60: 874–907.

Rosenkopf, Lori and Paul Almeida, 2003, “Overcoming localsearch through alliances and mobility”. Management Science,49: 751–766.



Rosenkopf, L. and A. Nerkar, 2001, “Beyond local search:Boundary-spanning, exploration, and impact in the opticaldisk industry”. Strategic Management Journal, 22: 287–306.

Rothaermel, F. T. and D. L. Deeds, 2004, “Exploration andexploitation alliances in biotechnology: A system of newproduct development”. Strategic Management Journal, 25:201–221.

Rothaermel, F. T. and A. M. Hess, 2007, “Building dynamiccapabilities: Innovation driven by individual-, firm-, andnetwork-level effects”. Organization Science, 18: 898–921.

Sakakibara, M., 1997, “Heterogeneity of firm capabilities andcooperative research and development: An empirical examina-tion of motives”. Strategic Management Journal, 18: 143–164.

Sampson, R. C., 2004, “The cost of misaligned governance inR&D alliances”. Journal of Law, Economics, and Organiza-tion, 20: 484–526.

Sampson, R. C., 2007, “R&D alliances and firm performance:The impact of technological diversity and alliance organiza-tion on innovation”. Academy of Management Journal, 50:364–386.

Sarkar, M. B., R. Echambadi, T. S. Cavusgil and P. S.Aulakh, 2001, “The influence of complementarity, compat-ibility, and relationship capital on alliance performance”.Journal of the Academy of Marketing Science, 29: 358–373.

Saxenian, A. L., 1994, Regional advantage: Culture and com-petition in Silicon Valley and Route 128. Cambridge, MA:Harvard University Press.

Saxton, T., 1997, “The effects of partner and relationship char-acteristics on alliance outcomes”. Academy of ManagementJournal, 40: 443–461.

Schilling, M. A., 2009, “Understanding the alliance data”. Stra-tegic Management Journal, 30: 233–260.

Schumpeter, J., 1934, Theory of economic development. Cam-bridge, MA: Harvard University Press.

Shah, R. H. and V. Swaminathan, 2008, “Factors influencingpartner selection in strategic alliances: The moderating role ofalliance context”. Strategic Management Journal, 29: 471–494.

Schulze, A. and G. J. C. Brojerdi, 2012, “The effect of thedistance between partners’ knowledge components on col-laborative innovation”. European Management Review, 9:85–98

Sidhu, J. S., H. R. Commandeur and H. W. Volberda, 2007,“The multifaceted nature of exploration and exploitation:

Value of supply, demand, and spatial search for innovation”.Organization Science, 18: 20–38.

Simonin, B. L., 1999, “Ambiguity and the process of knowl-edge transfer in strategic alliances”. Strategic ManagementJournal, 20: 595–623.

Singh, J., 2005, “Collaborative networks as determinants ofknowledge diffusion patterns”. Management Science, 51:756–770.

Singh, J., 2008, “Distributed R&D, cross-regional knowledgeintegration and quality of innovative output”. ResearchPolicy, 37: 77–96.

Stuart, T. E. and J. M. Podolny, 1996, “Local search and theevolution of technological capabilities”. Strategic Manage-ment Journal, 17: 21–38.

Teece, D. J., 1986, “Profiting from technological innovation:Implications for integration, collaboration, licensing andpublic policy”. Research Policy: 15: 285–305.

Thompson, J. D., 1967, Organizations in action: Social sciencebases of administrative theory. New Brunswick, NJ: Transac-tion Publishers.

Torre, A. and J.-P. Gilly, 2000, “On the analytical dimension ofproximity dynamics”. Regional Studies, 34: 169–180.

Torre, A. and A. Rallet, 2005, “Proximity and localization”.Regional Studies, 39: 47–59.

Trajtenberg, M., 1990, “A penny for your quotes: Patent cita-tions and the value of innovations”. RAND Journal of Eco-nomics, 21: 172–187.

Uzzi, B., 1997, “Social structure and competition in interfirmnetworks: The paradox of embeddedness”. AdministrativeScience Quarterly, 42: 35–67.

Van de Vrande, V., 2013, “Balancing your technology-sourcingportfolio: How sourcing mode diversity enhances innovativeperformance”. Strategic Management Journal, 24: 610–621.

Von Hippel, E., 2005, Democratizing innovation. Cambridge,MA: MIT Press.

White, H., 1980, “A heteroskedasicity-consistent covariancematrix estimator and a direct test for heteroskedacity”.Econometrica, 48: 817–855.

Whittington, K., J. Owen-Smith and W. W. Powell, 2009,“Networks, propinquity, and innovation in knowledge-intensive industries”. Organization Science, 54: 90–122.

Williamson, O. E., 1985, The economic institutions of capital-ism. New York: The Free Press.



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Partner Geographic and Organizational Proximity and the Innovative Performance of Knowledge-Creating Alliances