Reach out or reach within? Performance implications of alliances and location in biotechnology

Copyright # 2004 John Wiley & Sons, Ltd.

MANAGERIAL AND DECISION ECONOMICS

Manage. Decis. Econ. 25: 437–452 (2004)

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/mde.1200

Reach Out or Reach Within? PerformanceImplications of Alliances and Location

in Biotechnology

Akbar Zaheera,y,# and Varghese P. Georgeb,*,#

aStrategic Management and Organization Department, Carlson School of Management, University of Minnesota,321 19th Ave South, Minneapolis, MN 55455, USA

bOrganization Management Department, Rutgers Business School, Rutgers, The State University of New Jersey,111 Washington Street, MEC-324, Newark, NJ 07102-3027, USA

The extensive literature on geographical clusters has argued that firms stand to gain from the

knowledge spillovers and the easy availability of skills in regional agglomerations. At the same

time, the research on strategic alliances, particularly in technology-intensive industries, viewsalliances as vehicles for the transfer of technology and knowledge. Does membership in a

geographical cluster or a network of alliances}an alliance cluster}benefit firms equally?

More importantly, does it matter whether firms form alliances with firms within their

geographical cluster, or do they do better by reaching out beyond their cluster? In this paper,we investigate these and other related questions in the context of technology-intensive

industries such as biotechnology and argue that the high-level knowledge sensing and acquiring

needs of firms in such industries may require combinations of both kinds of knowledge-accessmechanisms. We use the BioScan database to obtain 648 alliances for the period 1985–1998

among 248 public companies, and complement it with COMPUSTAT data to examine the

effect of clustering and alliances on market value. Our results suggest that merely being part

of geographical clusters is not enough, and nor is it adequate for firms to join alliance clusters.Rather, firms gain by forming alliances both within and beyond their geographical clusters,

which highlights the complexities of acquiring technology-intensive knowledge and suggests

that the acquisition of knowledge in technology-intensive settings is best achieved through

mechanisms both formal and informal, both proximate and distant. Copyright # 2004 JohnWiley & Sons, Ltd.

INTRODUCTION

Research and practitioner interest alike hasrecently focused on the value of firm location.Scholars like Porter (1990) in strategy, Krugmanin economics (1994) and others in related fieldshave empirically and theoretically made the case

for the benefits of geographical clusters, or the co-location of firms in regional agglomerations(Storper, 1995). Clustering is said to enhance theperformance of the firms through knowledgespillovers, informal information exchange, andthe easier availability of inputs, skills, and othershared resources (Scott, 1992; Harrison et al.,1996; Rosenkopf et al., 2001). At the same time,the trend toward formalized cooperation}in theshape of interfirm alliances, joint ventures, andR&D and other agreements}has been intensify-ing as well (Powell and Brantley, 1992). Both thesephenomena reflect the accelerating pace of innova-tion, and the related requirements of staying

*Correspondence to: Organization Management Department,Rutgers Business School, Rutgers, the State University of NewJersey, 111 Washington Street, MEC-324, Newark, NJ 07102-3027, USA. E-mail: [email protected]@csom.umn.edu#The authors are listed randomly. Both authors contributedequally to the paper.

abreast of technological and market trends,integrating relevant knowledge, and developingnew products and processes (Lundvall, 1992).

Scholars have suggested that firms in technol-ogy-intensive industries require access toknowledge that is often tacit. In order for suchtacit knowledge to be effectively sourced, trans-ferred, and utilized, intense communication isnecessary (Storper, 1995). Physical presencein geographical clusters, or co-location, is nor-mally a precondition for such communication(Saxenian, 1991). However, given that firmsare also aggressively partnering with otherfirms through formal alliances (Mowery et al.,1996), it would appear that they are seekingadvantages that are unavailable from mere proxi-mity. Furthermore, in technologically fast-movingenvironments that require reaching out to a broadset of technologies, being geographically con-strained might limit firms’ technology choices.The juxtaposition of these trends raises severalquestions: Are firms located in geographicalclusters able to adequately meet their knowledgeneeds through spillovers and other informalprocesses that characterize regional agglomera-tions? Or do they need to create formal allianceseven within their clusters? Moreover, do firms intechnology-intensive industries benefit further byforming alliances with firms outside their geogra-phical clusters?

In this paper we propose that while a presencein the geographical cluster implies beingexposed to the innovation that is ‘in the air’, thecomplexity of high technology sensing, accessand transfer requires firms to create formal tieswithin their geographical cluster. Thus, firmsneed to take purposive action, beyond merephysical presence in a geographical cluster,to facilitate the flow of technology by creatingformal interorganizational structures that actas conduits for technology transfer. More-over, we posit that firms in technology-intensiveenvironments stand to gain further byforming alliances with firms beyond their geogra-phical cluster, implying the need to access thediversity of knowledge only available outside theircluster.

In addition to presenting some importantcaveats to the literature on geographical clusters,our research also sheds light on the debatein economic sociology on the nature of socialcapital between the proponents of Coleman (1988)

and Burt(1992). In essence, the Coleman positionis that a network of dense ties is most beneficial,because the interconnections among the actorssanction opportunism and promote trust. Burt,on the other hand, favors a sparse network withnumerous ‘structural holes’ over which focalactors broker information across the network.Recently, the notion of social capital hasbeen extended to the level of the firm. Researchers(e.g. Rowley et al., 2000) have promoted acontingent view of social capital, suggestingthat dense ties promote firm performance instable environments while a network with struc-tural holes benefits firms in volatile industrycontexts. Our research indicates that ratherthan preferring one network form over the other,firms in technology intensive environmentsmight benefit from both maintaining dense tieswithin their clusters and bridging structural holesto firms outside the cluster. At the same time, weargue that the dense but informal interaction thattakes place within the cluster needs to becomplemented and enhanced with formal structur-al arrangements to facilitate the flow of tacitknowledge.

In this paper, we investigate these issuesin the context of the technology-intensive biotech-nology and related industries. Biotechnology is aparticularly appropriate context in which to studythese research questions, because firms in theindustry both cluster geographically as well asexhibit high rates of alliance formation. Dedicatedbiotechnology firms are frequently located ingeographical clusters given the university-basedresearch origins of the industry. Due to resourceconstraints for R&D, long development leadtimes, lack of an infrastructure to commercializeinnovation and other reasons, the rate of allianceformation is high in this industry (Barley et al.,1992).

We use the population of alliances for allpublic biotechnology firms, as reported in theBIOSCAN industry database and complementit with data on market value from COMPUSTAT.We use clustering algorithms to create geo-graphical and alliance clusters and test ourhypotheses using ordinary least squares regression.In the following section, we begin by elabora-ting on the theory and developing the hypotheses,and thereafter describe the methods and results.We conclude with a discussion of the results,limitations, and future research directions.

A. ZAHEER AND V.P. GEORGE438

Copyright # 2004 John Wiley & Sons, Ltd. Manage. Decis. Econ. 25: 437–452 (2004)

THEORY AND HYPOTHESES

Geographic Clusters and Knowledge Spillovers

A long-standing research tradition in economics,going back to Marshall(1920), has arguedthat firms gain from location in geographicalclusters through knowledge spillovers and accessto common pools of skills and inputs. Recently,scholars have also noted the superior innovativepotential of geographic clusters: information andinnovation are ‘in the air’ and firm efficiency andspecialization are encouraged (Porter, 1990).Harrison, Kelley and Grant (1996) identify specificsources agglomeration economies: those that actto lower the cost of production due to scaleeconomies from specialization and those that arisedue to the enhanced prospects for technologicallearning, as information about new technologies isdiffused quicker and more completely in denselypacked and ‘institutionally rich’ locations. Knowl-edge spillovers, common in geographical clusters(Lieberman, 1989; Jaffe et al., 1993), make it easierfor firms to acquire technologies at low (or no)cost. Empirical research has tended to support thetheoretical propositions, finding broad evidencethat firms in geographical clusters are moreinnovative (de Vet and Scott, 1992).

Another perspective on geographic clustersdraws from economic sociology (e.g. Becattini,1990; Lazerson, 1995). This perspective regards thepure economic viewpoint on geographic clusters asundersocialized (Granovetter, 1985) and pointsto the embeddedness of exchange in a networkof social relationships. The role of trust isparamount (Zaheer et al., 2002). Trust is built upthrough repeated interaction, which comes aboutmore easily when actors are geographically prox-imate. Trust promotes, and is promoted by,exchange and other cluster behaviors, such asinformal deal-making, inter-firm assistance, andmutual responses to external contingencies. Thedense network of relationships promotes thediffusion of information about innovation. Ander-son (1994) notes that relationships in geographicalclusters benefit from proximity through moreeasily communicated information about the flowsof products and services. Geographic clusters alsobenefit from regional institutions that help co-ordinate and manage the network and supportinteraction and learning (Human and Provan,2000).

When the technologies around which the clusteris formed are complex, the role of regionalproximity is further enhanced. Interactions aroundcomplex technologies require high ‘bandwidth’arising from the need to convey the intricate detailand multiple levels of explanation inherent in suchtechnologies (Allen, 1984). Saxenian (1994) in herstudy of the technology clusters in Silicon Valleyand Route 128 observes that ‘long-distance com-munication is inadequate for the continuous anddetailed engineering adjustments required in mak-ing technically complex electronic products.’ Face-to-face interactions enable and facilitate superiorinformation exchange between firms. Moreover, inthe case of complex products, regional proximityenhances the necessary frequent interaction andteamwork. In sum, co-location improves colla-boration that is further promoted by the culturethat evolves around cooperative norms, trust, andshared identity (Saxenian, 1994). Other empiricalresearch, such as that by Almeida and Ko-gut(1999), also makes the connection betweenregional proximity and spillovers in technologi-cally sophisticated industries by showing thatscientists tend to cite the patents of firms that aremore geographically proximate.

Overall, these streams of literature and empiricalevidence together suggest that geographic co-location in technology-intensive industry clustersenhances the innovative potential and perfor-mance of the firms that form part of the clusteras a result of agglomeration economies, spillovers,increased coordination and interaction, and thebuild-up of trust. The argument is formalized inthe following hypothesis:

Hypothesis 1a:

Firms located in geographical clusters performbetter than firms not located in geographicalclusters.

While all geographical clusters are posited toconfer benefits to firms in the clusters, thearguments made above would seem to apply to agreater degree if the number of firms in the clusteris larger. This reasoning draws directly from thebenefits of agglomeration economies arising froma greater supply of resources and inputs, greaterspecialization and hence more scale economies, alllikely to be an increasing function of the overallvolume of activity in the geographical cluster. Alarger number of firms in a cluster should be,

REACH OUT OR REACH WITHIN ? 439


ceteris paribus, a proxy for a higher level ofeconomic activity and result in superior perfor-mance for the firms in it. Hence,

Hypothesis 1b:

Firms located in geographical clusters witha greater number of firms perform better thanfirms located in geographical clusters with fewerfirms.

Alliances, Alliance Clusters, and

Knowledge Transfer

Barley et al. (1992) observe that the focus of agreat deal of research on geographical clusterslimits the attention of firms and researchers tosmall sets of nearby organizations and ‘obscuresimportant relationships with organizations locatedelsewhere’. In effect, in order to get a completepicture of a firm’s network of relationships, it isnecessary to look at the entire gamut of a firm’salliance relationships, whether within the geogra-phical cluster or outside it. Consequently, it isimportant to examine the membership of firms in‘alliance clusters’ which are groups of firms definedby proximity based on formal alliance relation-ships. (In contrast, geographical clusters areformed by the physical proximity of the members.)

Research suggests that firms in high-technologyindustries are active in creating formal alliancesand becoming part of alliance clusters for anumber of reasons}among them informationaccess, technology transfer, learning, and obtain-ing complementary ‘market-driven’ resources(Barley et al., 1992; Powell et al., 1996; Georgeand Farris, 1999). In contrast to geographicalclusters, a significant benefit to creating formalalliances in technology intensive settings is themitigation of the severity of the so-called Arrow’sinformation revelation problem (Harrison, 1994).By committing to jointly share the payoffs frominformation and knowledge creation firms aremore forthcoming with the sharing of technologi-cal know-how, and thereby more likely tocreate new knowledge, when they are protectedagainst knowledge appropriation through contrac-tual safeguards. Membership in alliance clustersmakes available to firms the diverse complemen-tary and pooling capabilities of the other membersof the alliance network making them more

competitive in the marketplace (Nohria andGarcia-Pont, 1991). Moreover, being part of analliance clusters gives firms access to reliableinformation about the trustworthiness of the otheractors in the network through their tie patterns(Gulati, 1995; Stuart, 1998) In particular, ties tocommon third parties in an alliance clusterenhance the levels of trust in the network (Gulatiand Gargiulo, 1999; Ahuja, 2000), which tends toimprove cooperation and information sharingfurther.

The potential for creating new technologicalknowledge through freely shared information, andfor gaining access to the capabilities of other firmsin the network, coupled with higher levels of trust,are all likely to improve the performance of thefirms that are members of alliance clusters.Accordingly, we suggest that such firms willperform better than firms that are not part ofalliance clusters.

Hypothesis 2a:

Firms belonging to an alliance cluster performbetter than firms not belonging to an alliancecluster.

A larger number of firms in the alliance clusterimplies a greater variety of firms with more directand indirect links between them, more choices offirms to ally with, and access to more diverseinformation about new technologies, processes,and capabilities. Firms that are part of largeralliance clusters are likely to have increasedchances for the cross-fertilization of ideas andthe acquisition new ones. Firms in these largerclusters may display a greater propensity to buildon the more numerous innovations of the firms inthe alliance cluster (Porter, 1990). Thus, wehave:

Hypothesis 2b:

Firms belonging to alliance clusters with agreater number of firms perform better than firmsbelonging to alliance clusters with fewer firms.

Alliances Within and Reaching Out

Beyond Clusters

The transfer of technology in knowledge-intensiveindustries presents numerous difficulties due to itstacit and complex nature (Patel and Pavitt, 1995).



Formal, technology-sharing alliances are a well-known mechanism to effect the transfer of com-plex technology (Mowery et al., 1996). However,while formal alliances are a popular vehicle forknowledge transfer, alliances with firms that aregeographically proximate to one another are likelyto be even more beneficial in technology-intensiveindustry settings. This assertion draws on researchin the innovation literature, which has long shownthat increasing physical distance limits the magni-tude and range of technical communication (Allen,1984). Coordination, information transfer, syner-gistic interaction and other knowledge transferactivities become both harder and costlier withincreasing distance. At the same time, we suggestthat while regional proximity contributes to spil-lovers and information sharing, structures offormal cooperation are required as well to over-come natural firm boundaries which act as barriersto the communication of knowledge, particularlywhen the nature of knowledge is tacit, complex, orproprietary (Spender and Grant, 1996). In suchcases, we suggest that firms will do better by notrelying only on happenstance meetings and ran-dom spillovers from within the region, but byformalizing the pathways to knowledge transferwith other firms in the cluster.

As mentioned above, alliances serve well asinterorganizational structures that formally facil-itate the flow of knowledge across organizationalboundaries. However, while many firms may bepart of a geographical cluster, not all firms in thecluster are connected to others to the same extentand in the same manner (McEvily and Zaheer,1999). Firms in the cluster may be heterogeneousin the extent of their alliances for reasons such asdiffering resource profiles, skills, and product orprocess technologies (Shaver and Flyer, 2000).Given the complexity, tacitness, and difficulty ofknowledge access and transfer, we argue that firmsthat combine formal alliances with geographicalproximity are likely to benefit relative to firms thateither do not have alliances within the geographi-cal cluster or have fewer within-cluster alliances.Therefore,

Hypothesis 3:

Firms with a greater number of alliances withintheir geographical cluster outperform firms withfewer alliances in their geographical cluster.

Geographical clusters are usually formedaround sets of common technologies and densenetworks of exchange and other relationshipsbetween firms in the cluster. Regional proximitypromotes the creation of multiple and overlappingnetworks of relationships between firms andindividuals through common suppliers and custo-mers, shared membership in regional institutions,career paths that cross, other personal and socialties, and frequent face-to-face meetings (Saxenian,1994).

However, we argue that the information flowsand knowledge spillovers available to all themembers of a geographical cluster could limit theexposure of firms in geographical clusters toinformation diversity. Since the basis for theformation of a regional cluster is often a set ofinterdependent synergies, the knowledge, innova-tions, or ideas that are unrelated to the technol-ogies prevalent in the cluster are unlikely toflourish in that environment. As firms andindividuals, embedded in multiple networks withinthe cluster, have frequent contact with oneanother, they might find themselves limited intheir exposure to new knowledge. In other words,by occupying a tightly bounded, self-containedgeographical area, firms might find themselvescaught in a cycle of accessing the same redundantinformation as a result of interacting with the sameset of actors over and over (Llobrera et al., 2000).No geographic region is likely to have a monopolyon ideas or innovations, particularly in technol-ogy-intensive settings. Therefore, firms that mightbe constrained in their technology access in thismanner will tend to seek relationships with firmsbeyond their geographical cluster in order to reachsources of innovation unavailable within theircluster.

Reaching out to create alliances outside thecluster is akin to forming ‘bridging’ ties (McEvilyand Zaheer, 1999) across ‘structural holes’ (Burt,1992) to access information sources that existoutside of an environment of close-knit, dense andoverlapping ties, as represented by a geographicalcluster. Firms that bridge structural holes havebeen empirically demonstrated to acquire superiorcompetitive capabilities due to the richer, morenovel and diverse information resources that theyreach (McEvily and Zaheer, 1999). Consequently,firms from within geographical clusters thatform alliances with firms outside their geographi-cal clusters, while benefiting from within-cluster



spillovers, are more likely to also gain access tonew and diverse information, knowledge, andtechnology, resulting in superior innovativeness.Thus, we propose that firms that reach out to agreater extent by forming a larger number ofalliances outside their cluster should performbetter than firms with fewer alliances outside theirgeographical cluster, suggesting the followinghypothesis:

Hypothesis 4:

Firms with a greater number of alliances outsidetheir geographical cluster outperform firms withfewer alliances outside their geographical cluster.

As we have argued above, the formal allianceswithin the geographical clusters likely complementthe spillovers from within the cluster while servingas conduits for the seamless flow of complextechnology and knowledge from proximately lo-cated alliance partners. However, firms that main-tain formal alliance ties to other firms within theirgeographical clusters, while better off than firmsthat do not have such ties, are still likely to belimited in their exposure to novel and diverseinformation. The knowledge, technology, and skillsbeing generated within the geographical cluster maybecome a ‘blind spot’ (Pouder and St. John, 1996),given the rapid pace of technological developmentsand the need to integrate diverse streams oftechnological knowledge in technology-intensiveindustries (Patel and Pavitt, 1995). In this situation,firms that reach out to create formal alliancesbeyond their geographical clusters are likely toavoid succumbing to the blind spots and redundan-cies, leading to higher performance than for firmswith within-cluster alliances. In essence, we arguethat while building formal ties within the cluster arelikely to be beneficial, formal ties outside the clusterare likely to be even more so. Thus, we posit that analliance beyond the cluster will add more to firmperformance than an alliance within the geographi-cal cluster. Consequently, we hypothesize:

Hypothesis 5:

Alliances outside geographical clusters enhancefirm performance more than alliances withingeographical clusters.

Some researchers (Barley et al., 1992; Powellet al., 1996) have suggested that firms in thebiotechnology industry have often lacked theresources and marketing clout to commercialize

their new drugs and inventions. It has been arguedthat when biotechnology firms ally with larger andbetter-established pharmaceutical companies, theyare able to access the important complementaryresources necessary for them to succeed in themarketplace. We test this assertion directly andhypothesize:

Hypothesis 6:

Inter-industry alliances enhance firm performancemore than intra-industry alliances.

DATA AND METHODS

Sources of Data

The data for this study come from two sources.The first source is the BioScan Directory, which ispart of an electronic Biotechnology IndustryReporting Service from Thomson AmericanHealth Consultants. Researchers have used BioS-can extensively in earlier studies (e.g. Barley et al.,1992; Walker et al., 1997). We bound the study toUS public firms available in COMPUSTAT (seebelow), and their alliances, mainly because reliablefirm level variables are publicly available only forthem. We parsed the agreements field in theBioScan database among the US public companiesto obtain 648 alliances for the period 1985–1998.

Our second data source is Standard and Poor’sCOMPUSTAT (North America) database (S&P,2000). We collected data on firm-level attributessuch as size, industry classification, locationof firms, and financial performance from COM-PUSTAT. Although BioScan contains consider-able amount of data related to a firm’s variousattributes, it is not easy to use for analyticalpurposes due to the predominantly free-formnature of its fields. COMPUSTAT also hasadditional fine-grained industry classifications.Our initial dataset consisted of 291 publiccompanies.

Independent Variables

Firm level attributes as control variables. Humancapital is the most important resource in ourindustries, as it represents the firm’s productivecapacity. Therefore, we use Employees, the numberof company workers reported to shareholders, as



the control variable representing a firm’s size.Given the different classes of industries repre-sented in our dataset, each with varying needs fortypes and quantities of resources, Employees is abetter candidate to represent size than, say, Assets.The former also more directly incorporates thecumulative growth of a firm.

To control for industry effects, we recoded theGlobal Industry Classification Standard (GICS)code. Standard & Poor’s and Morgan StanleyCapital International collaborated to create GICS.It provides a classification of economic sectors,which is further subdivided into a hierarchy ofindustry groups, industries and sub-industries. Thecommonly used classic Standard Industrial Classi-fication (SIC) is not sufficiently fine-grained forour purpose. The SIC cannot distinguish wellbetween pharmaceutical and biotechnologicalcompanies, while the newer GICS does thatwell. We recoded GICS sub-industries to fourslightly broader classes of ‘Biotechnology’,‘Pharmaceutical’, ‘Health’ and ‘Other’ for thepurposes of this paper.

Location and geographical clusters. We used thezip codes of their headquarters to identify thegeographical clusters to which the firms primarilybelong. The zip codes were matched with latitudesand longitudes downloaded from the US CensusBureau. We present the primary location of firmsin the US map in Figure 1, where each firm isidentified by its ticker symbol. Although there isconsiderable overlap of the ticker labels, we cansee clear clustering of firms in the map. The large,dense cluster in the northeast is especially striking.

Clusters in north and south of California, andOregon are also noteworthy.

We computed a matrix of geographical dis-tances among the firms using their latitudes andlongitudes. We then used the geographical distancematrix in a hierarchical clustering program, withthe minimum distance option1 to identify thevarious clusters. Two sample sub-trees from thefull hierarchical clustering tree are shown inFigures 2 and 3. The general patterns seen in therespective regions in the map in Figure 1 are againvisible in the sub-trees. The full tree and its sub-trees allow us to precisely define clusters byspecifying the minimum distance between anytwo clusters.

We were keen to preserve the natural clusteringseen on the map as well as to examine the densenortheast pattern more closely. The United Statesis about 2600 miles across, and the Northeastregion is about 500 miles along the East Coast. Ifwe had specified a large inter-cluster distance, say100 miles, for the entire tree, we can see fromFigure 2 that the Northeast would join up into onemammoth cluster. If, on the other hand, we hadspecified a smaller distance, say 50 miles, several ofthe natural clusters in the rest of the country wouldget badly fragmented. Further, the meaning of‘distance’ varies across the country; people in theSouth, West and the Midwest US are accustomedto driving longer distances than in the Northeast.From the trees (Figures 2 and 3) we can clearly seenatural breakpoints that allow us to specifyclusters that closely follow the pattern seen inFigure 1. Accordingly, we specified an inter-clusterdistance of 50 miles for the Northeast, and 100

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labeled with the first three digits of the firm’s zip code and state. Distance is in miles.

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miles for the rest of the country. We also specifiedthat a minimum of three members is required fordefining cluster; if not, the firms were considered‘isolates’. We generated several variables based onthe geographical clusters so obtained.

Variables based on geographical clusters. Wefirst created a binary variable Geographical ClusterMembership indicating whether a firm belonged toany geographical cluster (1 if it does, 0 otherwise).We also assigned to each firm a variable represent-ing Geographical Cluster Size. We finally createdtwo other alliance network based variables for thisgenre, Alliance Count In Geographical Cluster andAlliance Count Outside Geographical Cluster, byapportioning the degree centrality of a firm towithin and without its cluster.2

Alliance clusters. We also identified alliance clus-ters based on the shortest path distances among thefirms in our sample. The technique used to identifyalliance clusters is similar to the one we used toidentify geographical clusters. We used hierarchi-cal clustering with the minimum distance option forboth. For alliance clusters we used as the input theshortest path distances from the alliance network;for geographical clusters we used shortest physicaldistances.

We wanted to obtain a set of alliance clustersthat had a distribution approximately similar tothat of the geographical clusters to make themcommensurable since one of our purposes is tocompare alliance and geographical clusters. Weconducted a sensitivity analysis with a small rangeof inter-cluster path distances and selected thevalue of 4.25 to meet the above criterion. Theclusters so identified were used to generate relatedvariables.

Variables based on alliance clusters. The alli-ance clusters became the basis for developing thevariables: Alliance Cluster Membership and Alli-ance Cluster Size. There are conceptually identicalto the parallel variables based on geographicalclusters.3

Alliances within and outside industry. Although weuse industry as a control variable, alliances withinones own industry and across to others can haveadditional different implication for performance.We computed Alliance Count Outside Own In-

dustry and Alliance Count Within Own Industry toexplore this difference.

Construction of the Dependent Variable

We consistently selected independent variables torepresent cumulation over time of some desiredconstruct. For example, variables that countalliances represent the cumulation of alliancesduring the period of the study. We thereforesought a performance (dependent) variable thatcaptured the present value of the future stream ofearnings based on the cumulation of a firm’sactivities over time. Consequently, we choseMarket Value At Fiscal Year End (MV). Thechoice of this variable is consistent with a longtradition of research in strategy, and morespecifically, research on joint ventures, that relieson market value or related measures (such aschanges in stock prices) as a proxy for perfor-mance (Koh and Venkatraman, 1991; McConnelland Nantell, 1985).

In Table 1 we list and summarize all thevariables. Based on the hypotheses we presentedearlier, the equation below summarizes the overallmodel we propose to fit to our data.

MV ¼ b0 þ b1EMPþ b2INDUSTRY

þ b3GEO:CLUS:MEMBER

þ b4AL:CLUS:MEMBER

þ b5GEO:CLUS:SIZE

þ b6AL:CLUS:SIZE

þ b9AL:COUNT :IN:GEO:CLUSTER

þ b10AL:COUNT :OUT :GEO:CLUSTER

þ b11AL:COUNT :INTRA:INDUSTRY

þ b12AL:COUNT :INTER:INDUSTRY

þ E: ð1Þ

Analytical Strategy

Our analytic strategy is to fit a hierarchy ofmultiple regression models to the data. A basemodel with the control variables EMP andINDUSTRY is fitted to the data first. We expectthese control variables to be related to theoutcome variable, but we are not directly inter-ested in them. They are kept in the model tocontrol for their influence, and to partition thevariance we can explain with other independentvariables. The rest of the predictor variables are



added individually and the explanatory power ofeach is tested using the increment to multiple R2

from the base (or, ‘previous’ level) model. Allpredictor variables with adequate individual ex-planatory power are retained in subsequentmodels fitted to the data. We use a40.05 for theincrement to multiple R2 test, and for presentingsignificant coefficients we use a40.1.

RESULTS

Summary Statistics

Table 2 presents the summary statistics of theunivariate distributions for the non-factor vari-ables in the model and the zero-order correlationsamong them. A preliminary examination of thecorrelation table suggests that the control variableEMP is highly correlated with MV as we expect,since the inclusion of EMP is designed to minimizethe size effects inherent in MV. The correlationof AL.COUNT.IN.GEO.CLUSTER and AL.-COUNT.INTRA.INDUSTRY, among our predic-tor variables, suggests that we should be cautiousabout multicollinearity among them. Later in this

section we report the results of the test formulticollinearity.

Fitting the Model

We carried out a few variable transformations,recoding, and outlier removal for fitting the model.As can be seen from the summary statistics inTable 1, MV and EMP have highly skeweddistributions. We transformed them with naturallogs to ensure that the model residuals are wellbehaved. We identified 17 geographical clustersand 12 alliance clusters in our analyses. We alsoobserved that some firms in our sample withextraordinarily high MVs have either no alliances,or have a small number of them. They turned outto be firms in the ‘Other’ category of INDUSTRY.Examples in this category are General Electric,Philip Morris, Procter & Gamble, DuPont andDow Chemical. Since these and other firms in thecategory are at best only side players in thebiotechnology sector, and since their presencewould considerably distort the models, we re-moved them from the sample. This reduced ourfirm sample size to 248.

Table 1. Description of Variables

Variable name Description Variable code

Employees Actual number of company workersas reported to shareholders}in thousands

EMP

Industry Industry Classification of the firm recodedfrom Global Industry Classification Stan-dard Codes}‘Biotechnology’,‘Pharmaceutical’, ‘Health’ & ‘Other’

INDUSTRY

Geographical Cluster Membership Binary variable indicating whether the firmbelongs to any geographical cluster

GEO.CLUS.MEMBER

Alliance Cluster Membership Binary variable indicating whether the firmbelongs to any alliance cluster

AL.CLUS.MEMBER

Geographical Cluster Size Number of other members in the firm’sgeographical cluster

GEO.CLUS.SIZE

Alliance Cluster Size Number of other members in the firm’salliance cluster

AL.CLUS.SIZE

Alliance Count In GeographicalCluster

Number of alliances a firm has with firmswithin its geographical cluster

AL.COUNT.IN.GEO.CLUSTER

Alliance Count OutsideGeographical Cluster

Number of alliances a firm has with firmsoutside its geographical cluster

AL.COUNT.OUT.GEO.CLUSTER

Alliance Count Outside OwnIndustry

Number of alliances a firm has with firmsoutside its recoded industry

AL.COUNT.INTER.INDUSTRY

Alliance Count Within OwnIndustry

Number of alliances a firm has with firmswithin its recoded industry

AL.COUNT.INTRA.INDUSTRY

Market Value At Fiscal Year End Closing Price at the end of fiscal yearmultiplied by the company’s CommonShares Outstanding}in millions of dollars

MV



We summarize the results from a hierarchy ofregression models in Table 3. The control variablesEMP and INDUSTRY in the base model aresignificant. We start with the base model andprogressively test the explanatory power of each ofthe predictors by adding them individually.Models 1 and 2 tell us that neither sheer locationin a geographical cluster nor membership in analliance cluster helps in explaining its marketvalue. This means there is no support forHypotheses 1a and 2a. The size of these clusters,both geographic and alliance, also do not reallymatter as can be seen in models 3 and 4. There ismodest significance to the coefficient of GEO.-CLUS.SIZE, but the increment to R2 test does notpass our hurdle of a40.05. Thus, Hypotheses 1band 2b are also not supported.

We find support for Hypotheses 3 and 4.AL.COUNT.IN.GEO.CLUSTER and AL.COUN-T.OUT.GEO.CLUSTER (models 5 and 6) displaynot only significant parameter estimates, but alsosufficient explanatory power to be separatelyadded to our cumulative model. However, sincethere is no significant difference between theirparameter estimates (means of distributions), thereis no support for Hypothesis 5 regarding strongereffect of alliances that are external to one’sgeographical cluster vis "aa vis ones that are internal.Models 7 and 8 show that, ceteris paribus,alliances within and outside a firm’s industry donot significantly add to its performance. Wetherefore cannot make any claim about the relativeeffectiveness of inter-industry of alliances ashypothesized earlier (Hypothesis 6).

Compared with the hypothesized Equation (1),we now have a truly parsimonious model. FromTable 3, we can restate the final model # 6 using

the following equation:

logðMVÞ ¼ 5:79�� þ 0:76��logðEMPÞ

þ 0:92��INDUSTRYPharma

þ 0:60��INDUSTRYBiotech

þ 0:09��AL:COUNT :IN:GEO:CLUSTER

þ 0:07��AL:COUNT :OUT :GEO:CLUSTERþ E:ð2Þ

We examined the distribution of residualsgraphically and verified them to be normal andhomoscedastic. We also inspected influence statis-tics to determine how sensitive the model fit was toremaining outlying cases. The largest Cook’sDistances is less than 0.065, much less than theacceptable 0.5. The diagonal elements of the Hatmatrix are all less than 0.5, maximum being 0.41,indicating the absence of high leverage points. Thelargest variance inflation factor (VIF) is 1.94,much lower than the acceptable 10. This suggeststhat multicollinearity does not adversely affect themodel.

DISCUSSION

Research and conventional wisdom have bothextolled the economic advantages of firm locationin geographical clusters such as those alongRoute 128 in Massachusetts and in Silicon Valley(Porter, 1990; Saxenian, 1991). However, littleresearch has systematically compared the benefitsto firms from sheer presence in a geographiccluster with the advantages of maintaining formalalliance relationships, whether within or beyondthe cluster. Since both regional and allianceclustering are phenomena that characterize

Table 2. Summary Statistics and Correlations among Continuous Variables in the Models

Variable

No Variable name Minimum Median Mean Maximum 1 2 3 4 5 6 7

1 MV 4.840 135.000 3530.000 132000.00

2 EMP 0.001 0.120 2.650 91.000 0.90

3 GEO.CLUS.SIZE 1.000 39.000 33.900 64.00 0.16 0.12

4 AL.CLUS.SIZE 1.000 10.000 14.700 49.00 0.19 0.13 0.12

5 AL.COUNT.IN.GEO.CLUSTER 0.000 0.000 0.823 26.00 0.77 0.73 0.35 0.20

6 AL.COUNT.OUT.GEO.CLUSTER 0.000 1.000 1.610 31.00 0.22 0.22 �0.13 0.24 �0.13

7 AL.COUNT.INTER.INDUSTRY 0.000 0.000 1.190 15.00 �0.07 �0.10 0.20 0.30 0.05 0.41

8 AL.COUNT.INTRA.INDUSTRY 0.000 0.000 1.240 31.00 0.83 0.81 0.08 0.19 0.69 0.45 �0.17



knowledge-intensive industries, the issue becomesone of examining whether firms can successfullymeet their knowledge access needs through apresence in geographical clusters or by being partof alliance clusters, or in fact by creating allianceswithin their geographical clusters. Moreover,given that alliances need not be constrained bygeography, a further question arises as to whetherfirms gain more by allying within their clustersrelative to creating formal alliances outside theirclusters. These questions are important becauseaccessing information and expertise is critical inknowledge-intensive industries.

Our most significant finding relates to the effectsof alliances within and outside geographicalclusters. Our results reveal that merely being partof a geographical cluster does not help firmperformance (although most firms in our samplebeing members of clusters limited our variance onthis measure). Instead, firms need to go beyondmerely being in regional agglomerations to purpo-sefully creating formal alliances within theirclusters; formal ties which serve as structuredconduits for the transfer of knowledge and skillsfrom within their cluster. Our findings suggest thatthe complexity and tacitness of sophisticatedtechnologies are best handled through formal,though geographically limited, interfirm pathways.It is not adequate for firms in knowledge-intensiveindustries to simply be present in a regional clusterand expect to freely appropriate spillovers and theknowledge available ‘in the air’. Instead, it is whenfirms establish formal alliances within the clusterthat they benefit, highlighting the considerabledifficulties that exist in the transmission of tacitand complex knowledge and technology (Patel andPavitt, 1995). This finding is particularly note-worthy in light of the vast research on regionalclusters which has long touted the benefits ofgeographic clusters, not only for the economicdevelopment of the region but also for the firmswithin them (Pouder and St. John, 1996).

Firms benefit also when they reach out to formformal alliances beyond their regional agglomera-tions. This finding is consistent with the logic ofstructural holes and suggests that all the requisiteknowledge that firms need is not necessarilyavailable within their geographical clusters(McEvily and Zaheer, 1999). Firms in knowledge-intensive industries need to access the frontiers oftechnology no matter where they are being pushedout. Additionally, we had also hypothesized (inT

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Hypothesis 5) that firms that reach out beyondtheir clusters would stand to gain more than thefirms that create and maintain formal allianceswithin their clusters. We do not find support thishypothesis, suggesting that complexity of technol-ogy access requires formal alliances within thegeographic cluster at least as much as alliancesoutside the cluster.

Together the finding that firms need formalties both within regional clusters as well as beyondthem speaks to the debate on social capitalbetween Coleman (1988) and Burt(1992). Colemanargues that dense ties, such as those that existin geographic clusters, are most beneficial whileBurt (1992) contends that the bridging of structur-al holes, such as to firms beyond the cluster,is a superior structural pattern. Our resultsindicate that both maintaining dense ties andbridging structural holes are important in theseindustries. These findings can be interpreted assuggesting that firms equally need the benefits thatthe two opposing structural patterns provide, theformer (dense ties) perhaps allowing firms to stayabreast with the current technologies of theircompetitors and suppliers and the latter (structuralholes) providing access to diverse and noveltechnologies. It is worth pointing out that ourresults add a further caveat to the social capitaldebate, by showing that in addition to being partof the dense ties in the geographical cluster,creating strong ties within clusters (i.e. formalalliances) are also important for the tacit and ‘fine-grained’ information facilitated by such ties (Uzzi,1996).

Like our finding for geographic clusters, we findthat mere membership in alliance clusters doeslittle to help firm performance. On investigatingthis result further, we find that 95 firms are isolatesand exist outside alliance clusters, indicating thatthis particular effect is statistically valid. Thisfinding goes against the conventional wisdom thatalliances, particularly in knowledge-intensive in-dustries are generally a ‘good thing’. We explain itby noting that little research actually exists thatexplicitly examines the performance implicationsof presence in alliance clusters. It could also be thecase that dyadic alliances are more valuable andless costly than alliance clusters. Explicating thecontingencies that are associated with superiorperformance as part of membership in allianceclusters therefore becomes a fertile area for futureresearch.

From another perspective, research on strategicalliances has often taken for granted that formalalliance relationships serve well as conduits for thetransmission of knowledge, even in technology-intensive industries (Mowery et al., 1996). Ourresearch suggests that it may be necessary to bepart of a regional cluster first while creatingalliances both within and selectively outside theregional cluster in order to thrive in technology-intensive industries. We also did not find thatbeing part of larger alliance clusters benefited thefirms in the clusters, although being part of largergeographical clusters was marginally significant inthe initial models.

Contrary to previous research and our hypoth-esis, we did not find that alliances across industriesin these technology intensive sectors of biotech-nology, pharmaceuticals, and health benefitedfirms more than did alliances within their indus-tries. In particular, research suggests that firmsreach across industries to access complementaryassets, as for example biotechnology firms allyingwith pharmaceuticals companies to gain access tocomplementary assets in the form of distributionand marketing networks (Powell et al., 1996). Ourlack of support for this proposition could be theresult of our inclusion of other factors that captureinterfirm alliances, such as our construct ofalliances beyond geographic clusters. Our modelcould in fact be capturing a more fine-grainedform of complementary asset access, in the form ofresources possessed by firms both within andselectively beyond the regional clusters.

Overall, our results speak to the research onregional clusters, alliances and firm-level socialcapital. To the research on regional clusters ourstudy suggests that the informal processes ofknowledge acquisition from spillovers may notsuffice in technology-intensive industries. Rather,given the considerable difficulties in accessing tacit,complex and technology-laden knowledge, infor-mal processes of knowledge acquisition need to becomplemented with formal ones in the shape ofalliance relationships. To the literature on strategicalliances, our results provide some importantcaveats and contingencies. Alliances in technol-ogy-intensive industries are most beneficial whenthey are proximately located, with the proviso thatit is also important to reach out to form alliancesoutside the geographical cluster to keep up withtechnologies developing outside the cluster. To thedebate on social capital, our findings indicate that



a combination of dense ties, bridging of structuralholes and strong ties may be beneficial to firms insuch industries. The acquisition of knowledge intechnology-intensive settings is best achievedthrough mechanisms both formal and informal,both proximate and distant.

LIMITATIONS AND DIRECTIONS FOR

FUTURE RESEARCH

One area in which our data were limited is in itsexclusion of privately held companies, necessitatedby our requirement of including a firm perfor-mance variable, in this case market value. Clearly,firms access knowledge and technology throughties to non-public firms as well. While our resultsneed to be interpreted with caution in this regard,we note that the geographical clusters are likely toinclude many non-public firms too, and to thatextent our findings are still valid. In terms of howfuture research might address this issue, there areno easy answers either}although alliances withnon-public firms could be included, the perfor-mance measurement issue still remains.

Further, we use a cumulative count approach toalliances, over a 14-year period. It could be thatsome alliances may have terminated, and othersmay have passed their peak value-adding phase,resulting in the market value at the end of theperiod not being completely reflective of the valueof some of the alliances. We could have addressedthis limitation by conducting a longitudinalanalysis, but the complexities of creating twokinds of clusters annually for each of the 14 yearsmade this approach untenable. On the other hand,if technology alliances deliver a lasting benefit, weshould still be able to capture their effects in thefirm’s overall market value.

The performance issues that we unearthedaround the firms forming alliances within clustersalso are a fertile ground for future research.Specifically, research should examine in greaterdetail the conditions under which knowledge orcomplementary assets are accessed within thecluster and lead to superior performance. It wouldalso be interesting to investigate whether firms thatreach out beyond their clusters are doing sobecause they ran out of partners or because ofthe unavailability locally of particular types ofknowledge bases they need. A good place to start

would be to get more fine-grained detail about thepurposes and the types of alliances that firms formboth within and outside their clusters.

CONCLUDING REMARKS

The extensive literature on geographical clustershas implied that knowledge spillovers and the easyavailability of skills within regional clustersbenefits firms from merely being present in thecluster. Our results suggest that merely being partof geographical clusters is not enough, and nor is itadequate to join alliance clusters. Informal pro-cesses of knowledge transfer do not suffice intechnology-intensive industries. Instead, firms thatcreate formal alliances both within and beyondtheir geographical clusters perform best, whichhighlights the significant difficulties of stayingabreast with and accessing technology-intensiveknowledge.

Acknowledgements

We are grateful to Geoff Bell, Deborah Dougherty, GeorgeFarris, and Bill McEvily for useful comments on earlierversions of this paper and to Anna Curridori for admirableresearch assistance. The work was supported in part by a grantto the second author from the National Science Foundation(DMI-9700797) and from the Center for Innovation Manage-ment Studies (CIMS) of Lehigh and North Carolina StateUniversities. All errors are ours.

NOTES

1. The distance between two clusters is the minimumdistance between a point in the first cluster and apoint in the second cluster.

2. In the case of an isolate firm, it can have a positivenumeric value only for Alliance Count OutsideGeographical Cluster.

3. Given the conceptualization of alliance clusters, andthe specification of intra-cluster path distance of 4.25there cannot be similar parallel formulations forvariables that apportion degree centrality within andoutside alliance clusters. A firm is most unlikely tohave links outside its alliance cluster.

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Reach out or reach within? Performance implications of alliances and location in biotechnology

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