Don't go it alone: alliance network composition and startups' performance in Canadian biotechnology

Strategic Management JournalStrat. Mgmt. J.,21: 267–294 (2000)

DON’T GO IT ALONE: ALLIANCE NETWORKCOMPOSITION AND STARTUPS’ PERFORMANCE INCANADIAN BIOTECHNOLOGY

JOEL A. C. BAUM1*, TONY CALABRESE1 and BRIAN S. SILVERMAN2

1Rotman School of Management, University of Toronto, Toronto, Ontario, Canada2Graduate School of Business Administration, Harvard University, Boston, Massa-chusetts, U.S.A.

We combine theory and research on alliance networks and on new firms to investigate theimpact of variation in startups’ alliance network composition on their early performance. Wehypothesize that startups can enhance their early performance by 1) establishing alliances, 2)configuring them into an efficient network that provides access to diverse information andcapabilities with minimum costs of redundancy, conflict, and complexity, and 3) judiciouslyallying with potential rivals that provide more opportunity for learning and less risk of intra-alliance rivalry. An analysis of Canadian biotech startups’ performance provides broad supportfor our hypotheses, especially as they relate to innovative performance. Overall, our findingsshow how variation in the alliance networks startups configure at the time of their foundingproduces significant differences in their early performance, contributing directly to an expla-nation of how and why firm age and size affect firm performance. We discuss some clear, butchallenging, implications for managers of startups.Copyright 2000 John Wiley & Sons, Ltd.

INTRODUCTION

Strategy and organizations scholars have longnoted that young firms have higher failure ratesthan established firms. In his seminal paper,Stinchcombe (1965) proposed that this propensityto fail exists because young firms have not estab-lished effective work roles and relationships andbecause they lack a track record with outsidebuyers and suppliers. While there has been muchdebate (for a review see Baum, 1996) concerningthe underlying source of the hazards facing newfirms—whether a liability of newness or a lia-bility of smallness—most of the research in thisdebate implicitly assumes that new entrants aretypified by a lack of stable relationships andsufficient resources.

Key words: strategic alliances; alliance networks;startup performance; Canadian biotechnology*Correspondence to: Professor J. Baum, Rotman School ofManagement, University of Toronto, 105 St. George Street,Toronto, ON M5S 3E6, Canada

CCC 0143–2095/2000/030267–28 $17.50Copyright 2000 John Wiley & Sons, Ltd.

However, startups vary considerably in theiraccess to resources and stable relationships, andthese variations may lead to differences in theirearly fates (Baum, 1996; Fichman and Levinthal,1991). The burgeoning literature on alliance net-works contends that alliances enable firms to gainaccess to resources, particularly when time is ofthe essence (Gulati, 1998; Teece, 1992). If so,then alliances are likely to be particularly ben-eficial to young, resource-constrained firms. Inshort, development of an appropriate alliance net-work at founding may enable a young firm toenjoy relationships and resources typical of amore established firm, hence overcoming liabili-ties of newness and/or smallness.

Our paper focuses attention on this possibilityby linking theory on alliance networks and onnew firms to investigate the early performanceconsequences of variation in the alliance networksthat startups configure at the time of their found-ing. We predict that startups can enhance theirearly performance by, at the time of their found-ing, 1) establishing an alliance network, 2) con-

268 J. A. C. Baum, T. Calabrese and B. S. Silverman

figuring the network to provide efficient accessto diverse information and capabilities with mini-mum costs of redundancy, conflict and com-plexity, and 3) allying with potential rivals thatprovide more opportunity for learning and lessrisk of intra-alliance rivalry. No prior researchhas explicitly linked startups’ early performanceto their founding-network composition. Past stud-ies examine consequences of startups’ current orcumulative alliance patterns (e.g., Shan, Walkerand Kogut, 1994; Stuart, Hoang and Hybels, 1999),often focusing on performance implications of parti-cular alliances (e.g., R&D alliances or commercialties) rather than overall network composition.

We test our predictions through a study of all142 biotechnology firms founded in Canada dur-ing the six-year period from January 1991 toDecember 1996. We include information on thefounding-network conditions of each startup,defined as the characteristics of a startup’s initialconfiguration of alliances, and on performance—up to the end of the fifth year—for each startup’slife. We measure alliance networks compre-hensively, consideringhorizontal alliances withother biotechnology firms,vertical-downstreamalliances with pharmaceutical, chemical and mar-keting firms, andvertical-upstreamalliances withuniversities, research institutes, government labs,hospitals and industry associations.

Theory and research on the hazards facing newfirms has traditionally focused on failure as theoutcome of interest (e.g., Baum and Oliver, 1991;Levinthal, 1991; Singh, House and Tucker, 1986).However, it is common to observe large perform-ance differences among surviving startups; somenew ventures flourish while others languish—surviving—but only just (Eisenhardt and Schoon-hoven, 1990; Stuartet al., 1999). Consequently,we examine multiple measures of performance,including revenue growth, employment growth,R&D spending growth, and patenting success.These multiple measures permit us to comparethe effects of startups’ alliance networks acrossa range of critical early performance dimensions:economic resource acquisition, human capitalrecruitment, investment in innovation, and intel-lectual property development. Non-failure per-formance measures also allow us to address acommon criticism of studies that have shownbeneficial effects of alliances. Such studies facea fundamental identification problem: do alliancesenhance performance or are alliances spuriously

Copyright 2000 John Wiley & Sons, Ltd. Strat. Mgmt. J.,21: 267–294 (2000)

correlated with performance because superiorfirms are better able to secure alliances? Ourstudy design and empirical models attempt tocontrol for much of the unobserved heterogeneityunderlying this criticism (e.g., lagged perform-ance and founding conditions), enabling us tointerpret our results with greater confidence(Jacobson, 1990).

Strategic alliance networks and startupperformance

The high failure rate among young firms, relativeto their older counterparts, has frequently beennoted in the strategy and organizations literature.In a seminal paper, Stinchcombe (1965) arguedthat a new firm’s subsequent performance is sig-nificantly affected by conditions surrounding itsfounding. His theoretical argument emphasizedtwo sets of founding conditions. One is organi-zational: startups’ key members are typically inunfamiliar roles and new work relationships at atime when organizational resources are stretchedto the limit. A second set is environmental: newfirms are assumed to lack broad bases of influenceand endorsement, stable exchange relationshipswith important external constituents, and percep-tions of quality, reliability and legitimacy thatyears of experience in providing particularproducts or services confers on more establishedsuppliers. Numerous early studies supportedStinchcombe’s conjecture of a liability of newness(e.g., Carroll, 1983; Freeman, Carroll and Han-nan, 1983). More recent research challenges thisinterpretation, concluding that since new firmstend to be small, the liability of newness isactually a liability of smallness (for a review seeBaum, 1996).

Both liability of newness and smallness argu-ments assume that incipient organizational rou-tines (Eisenhardt and Schoonhoven, 1990; Larson,1992), uncertainty about the quality of the organi-zation’s products or services (Hannan and Free-man, 1984), and a lack of social approval, sta-bility and sufficient resources (Boeker, 1989)typify recent entrants and that these shortcomingsraise their risk of failure. To date, only a fewstudies have explored the potential for variationin liability of newness and smallness effects.1

1 In their research on the ‘liability of adolescence’, Bru¨derland Schu¨ssler (1990) and Fichman and Levinthal (1991) have

Don’t Go It Alone 269

Singh et al. (1986) found that externallegitimacy—measured as inclusion in communitydirectories or charitable registration—decreasedthe liability of newness among voluntary socialservice organizations. Reinforcing and extendingthese results, Baum and Oliver (1991) showedthat institutional linkages with municipal govern-ment and community agencies moderated bothliabilities of newness and smallness for day carecenters and nursery schools.

Only recently, however, have researchers begunto explore either sources of startups’ alliance-based performance variation resulting from tiesother than those to government and communitylegitimating bodies, or performance outcomesother than organizational failure. Eisenhardt andSchoonhoven (1990) found that semiconductorstartups whose founding TMTs had greater priorjoint work experience (i.e., more well-establishedworking relationships) exhibited higher initialsales growth, which provides support for Stinch-combe’s (1965) internal coordination argument.2

Shanet al. (1994) showed that cumulative coop-erative ties established by 85 U.S. biopharmaceu-tical startups with commercial firms between theirfounding and 1989 positively influenced theircumulative innovative output (patents issued)over the same period. And, in a companion study,Walker, Kogut and Shan (1997) showed thatthese startups’ cumulative commercial alliancepatterns between their founding and 1984 were astrong predictor of their future commercialalliance patterns. In two recent studies of startupperformance, Stuart and his colleagues (Stuart,1998a, Stuartet al., 1999) found technology start-ups with prominent alliance or exchange partnersto perform better than comparable ventures with-out endorsements. They found that endorsementby well-regarded affiliates increased sales growth

studied the effects of variation in organizational size at found-ing on the pattern of age dependence in organizational mor-tality. Liability of adolescence arguments, which propose thatan organization’s initial stock of assets can buffer it frominitial selection pressures, are consistent with the idea thatthe liability of newness is really a liability of smallness.2 Notably, while Eisenhardt and Schoonhoven (1990) interprettheir findings as support for Stinchcombe’s (1965) internalcoordination argument, because their empirical models do notcontrol for founders’ cumulative prior work experience, it ispossible that their findings capture, spuriously, the resource-access advantages of founders with more years of industryexperience. Penningset al. (1998), for example, found thatthe Dutch accounting firms with more industry-experiencedfounders exhibited lower failure rates.


rates among U.S. semiconductor startups, andresulted in faster initial public offerings—athigher valuations—among U.S. biotechnologystartups.

These recent studies indicate the influence thata startup’s alliances may have on its subsequentperformance, and also show the relevance ofliability of newness/smallness arguments for out-comes other than failure. To date, however, onlyEisenhardt and Schoonhoven (1990) haveexplicitly linked startups’ early performance totheir founding conditions. All past studies ofstartups’ alliances examine contemporaneous orcumulative counts of alliances. As Stinchcombe(1965) suggests, however, conditions and eventssurrounding the creation and infancy of neworganizations affect their exposure to liabilitiesof newness and smallness, and, moreover, canhave long-lasting effects on their future develop-ment. Firms may be imprinted or otherwise starton developmental trajectories based on circum-stances at the time of their founding (Boeker,1989). Given these potentially powerful initialand historical effects, an important predictor of astartup’s initial performance trajectory may be itsalliance network at founding.

Benefits of strategic alliances

Recent research on alliances and networks hasstressed the value of interorganizational relation-ships for accessing resources and creating com-petitive advantage (Dyer and Singh, 1998). Anextensive literature discusses the characteristicsof alliances and networks that facilitate the flowof knowledge among partners (e.g., Kogut, 1988;Mowery, Oxley and Silverman, 1996). Allianceshave also been postulated to provide access tocomplementary assets (Pisano, 1990) as well asaccess to external legitimacy and status similarto that provided by legitimating institutions(Baum and Oliver, 1991; Miner, Amburgey andStearns, 1990; Stuartet al., 1999). Thus, a firm’sstrategic alliances may influence its capabilitiesas well as others’perceptionsof its capabilities.If a new firm lacks resources and suffers in themarketplace from uncertainty about its wares, andif alliances provide both access to resources itlacks and favorable signals about the firm whenits true qualities are least well known, then anew firm’s alliances should provide a significantbuffer against the hazards typically faced by start-


ups. By forming strategic alliances, startups canthus potentially access social, technical, and com-mercial competitive resources that normallyrequire years of operating experience to acquire(Ahuja, 2000; Nohria and Garcia-Pont, 1991).

We expect that establishment of an alliancenetwork at the time of founding will significantlyreduce the hazards faced by a startup, resultingin differential initial performance and growth.When startups are able to secure relationshipswith key actors at the time of their foundingthey mitigate the risks of newness because theknowledge, resources, stability and associativelegitimacy that partners confer on the startupwill tend to compensate for the disadvantages oforganizational inexperience (see also Hite andHesterly, 1999). Interorganizational alliances thusaccord advantages to startups that are usuallyassociated with the privilege of advanced age,including access to strategic and operationalknowhow (Teece, 1992), possession of stableexchange relationships (Stinchcombe, 1965) andinnovative capabilities (Shanet al., 1994), exter-nal endorsement of its operations (Baum andOliver, 1991), and the perceived quality andreliability of its products and services amongpotential customers, suppliers, employees, collab-orators and investors (Hannan and Freeman,1984; Stuartet al., 1999).

Hypothesis 1: A startup’s initial performanceincreases with the size of its alliance networkat founding.

In addition to performance implications ofparticular types of alliances (e.g., R&D alliancesor commercial ties), which have been the primaryfocus of past research, the overall composition ofa startup’s alliances may contribute significantlyto the startup’s performance. Several factors,including redundancy, internal conflict, and com-plexity are especially likely to influence the effec-tiveness of a startup’s alliance configuration.Cumulatively, growth in number of a firm’salliances increases potential partner redundancy:alliances are redundant to the extent that theyprovide access to the same information (Burt,1992) or complementary capabilities (Gomes-Casseres, 1994). Consequently, increasing thenumber of alliances without considering partnerdiversity can create inefficient configurations thatreturn less diverse information and capabilities


for greater cost than a smaller nonredundant set.A highly redundant configuration may even pre-vent a firm from obtaining new or novel infor-mation critical to its adaptation by limiting thenumber of links to firms in touch with emerginginnovations (Uzzi, 1996, 1997).

Entering alliances without attention to composi-tion can also lead to conflict among a firm’spartners as duplication creates rivalry among afirm’s alliance partners (Gomes-Casseres, 1994).As Dyer and Nobeoka (2000) point out, forexample, Toyota will not place competing sup-pliers together when composing itsjishuken, orvoluntary supplier learning teams. The potentialfor conflict depends on how many network mem-bers perform similar functions or take on dupli-cate roles. Internal conflict can have two opposingeffects. To a point, it can increase flexibility,foster innovation and ensure security of access tocritical complementary assets. But it can alsofragment the network as partners’ competinginterests pull in different directions, members failto reach sufficient scale or returns to invest inthe alliance, and appropriation concerns derailcooperative efforts. Such conflict, which may bedifficult to remedy through ex-post renegotiationgiven alliances’ hybrid structures, staffing,accounting and payouts (Kogut, 1988; William-son, 1991), can undermine the alliances’ value.

Finally, difficulty in quantifying the value ofpartnerships has led some industry analysts tocriticize firms with numerous alliances as diffuseand unfocused and to steer investors toward com-petitors with simpler networks. For example, WallStreet analysts have criticized Chiron Corporation,which has emerged as a diversified bio-conglomerate in the U.S. by establishing a com-plex web of equal-partner alliances (Fisher,1996). Thus, in the same way that markets lookunfavorably on conglomerates, firms with exten-sive, inefficient webs of alliances comprised ofmultiple, duplicate partners risk criticism fromanalysts, investors and capital markets.

These ideas suggest that more ‘efficient’alliance configurations—that is, configurationsthat provide access to more diverse informationand capabilities per alliance, and thus producedesired benefits with minimum costs of redun-dancy, conflict, and complexity—will prove mostbeneficial to startups. Consistent with this expec-tation, Powell, Koput and Smith-Doerr (1996)showed that U.S. biotechnology firms in human


therapeutics with ties to a more diverse set ofactivities were better able to locate themselves inresource and information-rich positions, and grewmore rapidly. Baum and Silverman (1998) alsofound that more efficient alliance configurationslowered failure rates for Canadian biotechnologyfirms—even after controlling for the main effectsof alliance formation. Therefore, we hypothesize:

Hypothesis 2: A startup’s initial performanceincreases with the efficiency of its alliancenetwork at founding.

Risks of strategic alliances

An alternate argument in the alliance literaturepoints out the potentially harmful effects ofalliance formation. Strategic alliances areinherently incomplete contracts in which the prop-erty rights associated with alliance output andprofits may not be well defined. As a result,collaborators risk opportunistic exploitation bytheir partners, including leaking proprietaryknowledge to partners or otherwise losing controlof important assets (Hamel, 1991; Williamson,1991). Although appropriate use of governancestructures might ameliorate these concerns(Larson, 1992; Oxley, 1997), intra-alliance rivalryretains the potential to severely disrupt an allianceand to harm a participating firm. This is partic-ularly true when alliances are at risk of deteriorat-ing into learning races (Khanna, Gulati andNohria, 1998) in which a firm attempts to extractas much knowledge as possible from its partnerwhile divulging as little as possible.

Although there has been little empiricalresearch exploring learning races, it is likely thatsuch rivalries are fiercest and most damaging incollaborations among potential rivals.3 Firmsappear to view their partnerships more obviously aszero-sum games when the potential for competitionbetween them is high, impeding the success of thepartnership. For example, Moweryet al. (1996)

3 We view the potential for competition between firms as apositive function of overlap in firms’ ‘market domains.’ Ourdefinition of ‘market’ follows Abell (1980: 17) who definesproduct markets as ‘a set of goods and services that servesimilar functions, are created with the use of similar tech-nology, and are used by similar users.’ ‘Market domain’ refersto the set of markets in which a firm operates. Dependingon the market domains they target, firms encounter differentpotential rivals and face different competitive conditions(Baum and Singh, 1994).


found that alliances involving partners who com-peted in the same primary SIC exhibited lowerlevels of knowledge transfer, measured by changesin patent cross-citation rates, than did alliancesinvolving non-competing partners. Such partnershipsare also unlikely to permit what Nakamura, Shaverand Yeung (1996) term ‘complementary specializa-tion,’ according to which each partner focuses ona subset of activities and then combines the resultswith those of the other partner. Relatedly, Grindley,Mowery and Silverman (1994) suggested that the14 U.S. semiconductor manufacturers involved inthe research consortium SEMATECH were unableto undertake their initial joint research agenda pre-cisely because of fears concerning information leak-age and learning races and, consequently, changedthe research agenda to focus on vertical infrastruc-ture issues.

Hypothesis 3: A startup’s initial performanceis weakened by alliances withpotential rivalsat founding.

Mitigating the risks of strategic alliances withpotential rivals

The foregoing risks of allying with potential rivalsnotwithstanding, startups may be particularlylikely to establish alliances with their potentialrivals. Startups are driven to forge such alliancesbecause established rivals are repositories ofknowledge needed by organization builders.Stinchcombe (1965: 152), observing that ‘organi-zations requiring similar talents and training tendto have interlocking directorates,’ argued thatsuch linkages to similar (and therefore potentiallyrival) organizations enable a new organization totap experience relevant to its formation and sur-vival. Alliances with rivals offer similar benefits.By allying with potential rivals, startups can pre-sumably gain access to uncodified, tacit knowl-edge about strategy, technology and operationscritical to their success (Liebeskindet al., 1996).

How can a startup balance the risks of collabo-rating with potential rivals against its need toaccess such knowhow? Hypothesis 3 assumes thata new startup’s alliances with any potential rivalsare equally hazardous. However, some potentialrivals may either pose greater risk or offer greateropportunity than others, and, as a result, the effectof a startup’s alliances with its potential rivalsmay vary from partner to partner. In particular,


the above-described hazards of allying withpotential rivals hinge on the likelihood that suchpartners will initiate learning races. To the extentthat potential rivals vary in their propensity tolaunch learning races different potential rivalspresent different levels of risk.

Asymmetries in partners’ incentives to allocateresources to learning can affect the likelihoodthat an alliance will experience a learning race.Khannaet al. (1998) decompose the benefits ofalliances into common benefits, which result fromthe application of joint learning to activitieswithin the sphere of the alliance, and privatebenefits, which a partner gains by applying whatit learns (unilaterally) to its operations outsidethe scope of the alliance. Asymmetric learningincentives arise when one partner stands to gaina larger private benefit than the other partner.Thus, the relative scope of allies’ market domainscaptures the incentives each partner has to investin learning and act competitively (versuscooperatively) within the alliance.4 A startup witha relatively narrower market domain than a givenpotential rival partner should generally face moresevere intra-alliance competition in that alliance.5

Therefore, we hypothesize:

Hypothesis 4: A startup’s initial performanceincreases with its scope advantage relative topotential rivals with which it establishesalliances at founding.

Alliance benefits may also vary by partnerquality—defined by skill level critical for com-petitive success in a particular market (Podolny,1994; Stuart, 1998a, b). High quality partnerspossess leading-edge technology and productioncapabilities.6 To the extent that access to knowl-edge is a primary motive for alliances, as widely

4 Note that this is somewhat different from the definition ofrelative scope in Khannaet al. (1998), which is a ratio ofthe scope of the alliance to the set of markets in which afirm participates. Nevertheless, these are similar in spirit, asevident in Khannaet al. (1998: 195–196).5 An alternative interpretation of the learning race argumentis that as the likelihood of a learning race increases,bothpartners will become less forthcoming in their provision ofknowledge, so that even if a learning racedoes not occurthe alliance will be of little value. This interpretation impliesthat the appropriate measure for H4 is theabsolute differencein partner scopes, without concern for whether a given partneris the broader or narrower partner. We examined this possi-bility empirically, but found no evidence to support it.


contended in the alliance literature, then the bene-fit of alliances will depend on the technologicalcompetencies of alliance partners. Partners’ tech-nological competence will moderate the effect ofalliances even if learning is simply a concomitantof relationships formed for other reasons (Stuart,1998a, b). Alliances with the most skilled inno-vators are the most promising opportunities tolearn new routines and acquire advanced techno-logical knowhow. In addition to such learningbenefits, the implicit transfer of status and explicittransfer of resources that comes with an allianceto an innovative rival may reduce uncertaintyabout startup quality, raising other external actors’assessments of the startup’s prospects and value(Stuartet al., 1999). This possibility of spilloversfrom prominent partners is stressed both in thesignaling literature in economics (Spence, 1974)and research on status in sociology (Podolny,1994). Supporting these ideas, Stuart (1998a)showed that U.S. semiconductor startups withmore innovative partners experienced higher salesgrowth rates. And, Stuartet al. (1999) showedthat U.S. biotechnology startups with more promi-nent partners were faster to initial public offering(IPO)—at higher valuations—than startups with-out lesser connections. Therefore we hypothesize:

Hypothesis 5: A startup’s initial performanceincreases with the innovative capabilities ofpotential rivals with which it establishesalliances at founding.

We recognize that partners’ relative scope andtechnological innovativeness may have mutuallyreinforcing effects. For example, a partner whosescope is greater than that of a focal startup isparticularly likely to initiate a learning race whenit is also weak technologically. We therefore includethe interaction between these characteristics of astartup’s partners in our empirical models below.

Strategic alliance networks in thebiotechnology industry

We test the above hypotheses in a study ofstartups in the Canadian biotechnology industry.In biotechnology, the significant resource and

6 Although we focus here on partners’ ‘technical capital,’Ahuja’s (2000) analysis indicates that social and commercialcapital may also influence partner attractiveness.


speed demands of patent races and commerciali-zation motivate biotechnology firms (BFs) to seekout partnerships with other organizations (Powellet al., 1996). Industry associations play a centralrole in the ‘biopartnering process,’ offeringnumerous international forums in which firms canpursue strategic alliances and partnerships aroundthe world–several such forums operate on theinternet [e.g., Allele’s (www.recap.com)].

Alliances with downstream partners can pro-vide access to complementary assets critical tosuccessful development and commercialization:market access, marketing and distribution infra-structure, technology and production facilities,and/or expertise in managing clinical trials(Pisano, 1990). This is particularly true of collab-oration with pharmaceutical and chemical firms.While established pharmaceutical and chemicalfirms often lack capabilities in the new researchtechnologies—BFs have successfully recruited thebrightest scientists for nearly two decades (Fisher,1996)—these firms excel in product commer-cialization. They also have the capital to supportstartup BFs’ high R&D ‘burn rates’ and therelevant experience to evaluate, and wait for,payoffs far in the future (Arora and Gambar-della, 1990).7

Alliances between BFs and upstream partnerscan also be a source of up-to-date information orknowledge critical to success in patent races buttoo tacit to be effectively transferred throughlicensing or purchase (Liebeskindet al., 1996).Such alliances provide interaction opportunitiesthat generate new concepts and ideas and incen-tives for the extensive sharing of experiencenecessary for interorganizational learning (Powellet al., 1996). This is particularly true of collabo-ration with universities, research institutes, andgovernment labs.8 Such links can provide timely

7 Pre-commercial payments (including R&D and milestonepayments, loans and credit lines and equity investments) toBFs by large pharmaceutical firms associated with drug dis-covery or development alliances averaged more than US$40million during the 1991–96 period (Edwards, 1997). Thereis also a financial incentive for these investments in externalR&D: by moving currently funded internal R&D to a BF,pharmaceutical and chemical firms can reduce operating bud-gets and raise reported earnings. If the resources such tiesconfer are substituted for venture capital and weaker startupstend to make such a substitution, the expected beneficialeffect of these alliances will be moderated.8 Although this is also true of alliances with hospitals, Cana-dian health care reform has driven Canadian research hospitalsincreasingly to form alliances with industry geared toward


access to new ideas and concepts (e.g.,approaches to disease intervention), emergingknowledge of biological systems, and technologi-cal knowhow that BFs attempt to translate intonew product ideas and applications.

In biotechnology, the incentives for and likelyharm of intra-alliance rivalry appear greatest inalliances with other, potentially rival, BFs thatoperate in common market sectors. Alliances withother BFs can also provide many of the abovebenefits, as well as access to experience on howto operate and grow a firm in the biotechnologyindustry. Yet learning races among BFs are fueledby competition for patentable compounds (i.e.,patent races), thus creating incentives for rivalBFs to appropriate scientific knowledge that isnot already protected by patent laws, while, atthe same time, being especially vigilant againstexactly such appropriation from themselves.

Finally, in biotechnology, a firm’s success iswidely held to depend on the capabilities of allits partners (e.g., Powellet al., 1996), and itsaccess to capable partners is highly dependent onits prior set of alliances (Ahuja, 2000; Gulati,1995; Walkeret al., 1997). Given that networksin the biotechnology industry may serve as aselection mechanism, culling out some firms onthe basis of their partners’ weaknesses (Walkeret al., 1997: 110), a reputation for successfulcooperation can be an asset in obtaining financingand furthering cooperative ties (Gulati, 1995;Uzzi, 1996, 1997).9

RESEARCH METHODS

Data description

We tested our hypotheses using data describingthe alliances, organizational characteristics, andperformance growth of BFs that began operationsin Canada during the six-year period betweenJanuary 1, 1991 and December 31, 1996. Wecompiled life histories on the 142 startup BFs

funding hospital-based research and spinning off revenue-generating companies (KPMG, 1997).9 In Rowley et al.’s (2000) terms, biotechnology firms’ hori-zontal alliances are thus constituted primarily to achieveexploratory aims. Notably, however, their vertical alliancesappear to have mixed aims, with upstream alliances similarlyexploratory in orientation, but downstream alliances generallyexploitative. Overall then, firms’ strategic alliance networksmay be multifaceted and not of unitary character.


that were founded during this period, as well as471 incumbent BFs founded prior to 1991, fromCanadian Biotechnology, an annual directory ofcompanies active in the biotechnology field inCanada published since 1991.Canadian Biotech-nology is the most comprehensive historical list-ing in existence of BFs, their products, growth,performance, and alliances.Canadian Biotechnol-ogy tracks BFs operating in sixteen industry sec-tors: 1) agriculture, 2) aquaculture, 3) engi-neering, 4) environmental, 5) food, beverage andfermentation, 6) forestry, 7) human diagnostics,8) human therapeutics, 9) human vaccines, 10)horticulture, 11) contract research organization,12) veterinary, 13) energy, 14) biomaterials, 15)cosmetics, and 16) mining. Thirteen of the 16sectors experienced at least one startup (theexceptions were biomaterials, cosmetics, andmining).10 We cross-checked this informationwith The Canadian Biotechnology Handbook(1993, 1995, 1996), which lists information fora more restrictive set of core Canadian BFs–firms entirely dedicated to biotechnology, andfound no significant discrepancies in informationfor those firms represented in both sources.

For each startup BF in our sample, we gener-ated separate observations for each year of itsexistence up to five years. A BF founded in 1991and still in existence at the end of 1996 wouldbe represented by five separate observations. BFsfounded in later years and the 19 (13%) BFsthat ceased operations by the end of 1996 arerepresented by fewer observations. Our data baseincludes 511 observations. The number of BFsfounded in each year, the number of BFs surviv-ing to the end of each year, and the number ofobservations by BF age are given in Table 1.

Dependent variables and analysis

We measure five dimensions of BF startups’ per-formance. The first two measures define perform-ance as year-over-yearrevenueand R&D spend-ing growth (both in 1991 constant dollars). Thenext two performance measures reflect year-over-year employment growth in 1) the number ofnon-R&D employeesand 2) the number of dedi-

10 We used these industry categories rather than categoriesbased on patent classifications since most startups did nothave a patent granted at the time they were founded, andmany were not issued a patent by the end of 1996.


cated R&D employees. We use absolute growthrather than percentage growth since the lattercannot be computed from founding since manyfirms’ values on these variables are zero initiallyand in subsequent years as well. Together, thesefour measures, along with the fifth—startups’patenting rate (discussed separately below)—gauge startup BFs’ performance across a range ofdimensions that liability of newness and smallnessarguments indicate are critical to early success:revenue generation, investment in innovation andinnovative capabilities, success in recruitinghuman capital, and development of intellectualproperty.

To test our hypotheses we estimated changesin these variables using the following standardlog-linear growth model, which is suitable forestimation with linear methods:

ln(Pit ) = aln(Pit−1) + bxit−1 + eit

where P is a time-varying measure of perform-ance,a is an adjustment parameter that indicateshow current performance depends on prior per-formance, andb is a vector of parameters forthe effects of independent and control variables.Inclusion of the past year’s performance (Pit−1)to predict the current year’s dependent variablehelps account for the possibility that our empiricalmodels of startup performance suffer from speci-fication bias due to unobserved heterogeneity(Jacobson, 1990), which enables us to infer causalrelationships between alliances and performancewith greater confidence. That is, if startups’alliance networks are themselves a result of unob-served factors related to performance, controllingfor lagged performance should eliminate spuriouseffects resulting from such endogeneity. Inclusionof the lagged dependent variable results in a lossof 142 observations, reducing the sample from511 to 369 observations.

We estimated this model, which links foundingand contemporaneous conditions with startups’initial performance, on the pooled dataset witheach startup BF contributing a time-series panel.We entered an observation for each startup BFfor every year which we have data. For example,if a startup BF has four years of data, then itwould contribute four observations (one for eachyear of its existence) to the analysis. The lengthof each startup’s time series may differ becauseof when it was founded or failed during the


Table 1. Sample BF foundings/survivors by year and observations by BF age

BF age ObservationsFounding year: 1991 1992 1993 1994 1995 1996 by BF age

Surviving end 1991 31 0 142Surviving end 1992 30 28 1 124Surviving end 1993 29 26 29 2 99Surviving end 1994 26 25 29 19 3 77Surviving end 1995 25 23 27 18 21 4 46Surviving end 1996 23 21 26 18 21 14 5 23

Note: Initial entry for each cohort is the number of foundings for the year. The total number of observations is 511; thetotal number of observations for the analysis is 369 (i.e., 511 minus 142—the first observations for each startup).

observation period. Pooling the data in this wayimproves estimation efficiency and allows us tocorrect for the bias arising from the inclusion ofa lagged dependent variable (Hannan andYoung, 1977).

Pooling repeated observations on the sameorganizations is likely to violate the assumptionof independence from observation to observationand result in the model’s residuals being autocor-related. First-order autocorrelation occurs whenthe disturbances in one time period are correlatedwith those in the previous time period, resultingin incorrect variance estimates. This renders OLSestimates inefficient, and for the model of interest(with lagged dependent variable included) auto-correlation generates biased estimates (Judgeetal., 1985). Therefore, we estimated random-effects GLS models, which correct for autocorre-lation of distrubances due to constant firm-specificeffects (Kennedy, 1992).11

A final estimation issue concerns possible sam-ple selection bias due to attrition: if a startup failsit leaves the sample without its final performancechanges represented in the data. Therefore, in asupplementary analysis, we estimated models thatcorrected for possible sample selection bias dueto attrition using Lee’s (1983) generalization of

11 A second potential estimation problem is heteroskedasticity,which violates the assumption of constant error variance, andcan bias coefficient standard errors. Although the error vari-ance may become proportionately adjusted after includingrandom effects, and so heteroskedasticity is not usually cor-rected after estimating random effects, some heteroskedasticitymay still nevertheless exist. The only truly accurate solutionwould be to use the multi-step procedure outlined by Greene(1990: 472–475). However, the short panels comprising ourdata set (most including fewer than 6 observations) limits thevalue of this approach (personal communication, Bill Greene).


Heckman’s (1979) two-stage-least-squares (2SLS)procedure. The estimates do not differ substan-tively from those reported below, perhaps as aresult of the small number of failures among oursample of startups (19 of 142).

Our fifth performance measure is the yearlynumber of patents granted to a startup. BFs com-pete in patent races against rivals. Since patentsare granted to the first to invent the idea, runningsecond provides little if any benefit. Intellectualproperty protection for newly developed productsand processes offers significant benefits for thewinner of a patent race: a 20-year monopoly inthe U.S. and Canada. Armed with intellectualproperty protection, a BF is more likely to obtainfurther financing and find willing partners to sup-port commercialization activities (Lerner, 1994).Thus, the ability to stake technological claimsthat will give a BF a share of the expandingmarket is a critically important element of per-formance (Powell and Brantley, 1992: 388).

We identified the yearly number of patentsissued to each startup BF in the United Statesusing the Micropatent data base. We used U.S.patent data because most Canadian BFs file patentapplications in the U.S. first to obtain a one-year protection during which they file in Canada,Europe, Japan and elsewhere (Canadian Biotech’89; Canadian Biotech ’92). For startups thatwere subsidiaries, we included only those patentsassigned to the subsidiary. Following priorresearch, we assign a patent to a BF at the dateof application rather than the date of granting.Although our observation period ends December31, 1996, we include information on patentsgranted up to May 31, 1999. We do this to limittruncation of the count of patents granted due to


the time lag between application and grantingdates. Of all the granted patents that were appliedfor by firms in our data base between 1985 and1992, more than 75% were granted within 29months of application. More than 92% weregranted within 41 months of application. Thus,in our sample, a minority of 1996 inventiveactivity is omitted. To check our results, we re-estimated our models using observations throughDecember 1995 rather than December 1996. Inthese models we should capture over 90% ofinventive activity, since we include informationon patents granted 41 months after the obser-vation endpoint. This re-estimation produces sub-stantially the same findings as those reportedbelow.12

Because this final dependent variable is a countmeasure (i.e., the yearly number of patentsgranted to a startup), we used the pooled cross-section data to estimate the number of patentsexpected to occur within an interval of time(Hausman, Hall and Griliches, 1984). A Poissonprocess provides a natural baseline model forsuch processes and is appropriate for relativelyrare events (Coleman, 1981). The basic Poissonmodel for event count data is:

Pr(Yt = y) = expl(xt)[l(xt)y/y!]

where both the probability of a given number ofevents in a unit interval,Pr(Yt = y), and thevariance of the number of events in each intervalequal the rate,l(xt). Thus, the basic Poissonmodel makes the strong assumption that there isno heterogeneity in the sample. However, forcount data, the variance may often exceed themean. Such overdispersion is especially likely inthe case of unobserved heterogeneity. The pres-ence of overdispersion causes standard errors ofparameters to be underestimated, resulting inoverstatement of levels of statistical significance.In order to correct for overdispersion, a negativebinomial regression model can be used. A com-mon formulation, implemented inLIMDEP 6.0,which allows the Poisson process to include het-erogeneity by relaxing the assumption of equalmean and variance, is:

12 Estimates for a modified patent variable that includes onlypatents that have been applied for and are granted within a29-month window are also substantially the same.


lt = exp(p9xt)et

where the error term,et, follows a gamma distri-bution. The presence ofet produces overdisper-sion. The specification of overdispersion we usetakes the form:

Var(Yt) = E(Yt)[1+aE(Yt)]

In a supplementary analysis comparing fits ofnegative binomial and Poisson regression models,we found no evidence of overdispersion in anyof the models we present in the paper (i.e., theoverdispersion parameters were not significantlydifferent from zero atp , 0.05), indicating thatnegative binomial models did not improve sig-nificantly over Poisson models. Therefore, wereport estimates from Poisson regression modelsbelow.13

Independent variables

Alliance network at founding. To test Hypotheses1 and 3 we constructed a set of firm-specificvariables that counted, separately, the number ofalliances a BF had at the time of its foundingwith each of several types of partner: 1) non-rival BFs, 2) potential rival BFs, 3) pharmaceu-tical cos., 4) chemical cos., 5) universities, 6)research institutes, 7) government labs, 8) indus-try associations and 9) marketing cos.14 We definepotential rival BFs as those operating in one ormore of the same biotechnology sectors as thestartup BF. Although somewhat crude, this disag-gregation should permit us to distinguish amongBFs more or less likely to be potential competi-tors. Consistent with Stinchcombe’s (1965) ideathat startups will be likely to establish allianceswith direct rivals, we found no alliances betweenstartups and other BFs in nonoverlapping sectors.Consequently, we cannot estimate nonrival BFalliance effects. Hypothesis 1 proposes that alarger alliance network—or put differently, morealliances—should improve a startup’s early per-

13 Although, as Barron (1992) notes, a quasi-likelihoodapproach may be preferred when lagged counts to control forautocorrelation are not justified, our inclusion of lagged pa-tents is grounded in well-known growth models. As with theOLS models, however, inclusion of the lagged dependentvariable reduces the sample size to 369 observations.14 Although all startup BFs in our sample areCanadian, ourdataset includes these firms’ alliances worldwide.


formance. Consequently, we expect these alliancecounts to have a positive impact on performance,with the exception of potential rival BF alliances.Hypothesis 3 proposes, in contrast, that allianceswith potential rival BFs should, on average,adversely affect a startup’s early performance.Consequently, we expect the coefficient for poten-tial rival BF alliances to be negative.

Network efficiency at founding. Hypothesis 2proposes that a startup’s performance shouldincrease with the efficiency of its alliance net-work, where efficiency is defined as diversity ofinformation and capabilities per alliance. Follow-ing Burt’s (1992) conception of structuralequivalence—in which firms participating in thesame line of business are considered equivalentin the set of skills, relationships, and assets theyembody—we assume that alliance partners of agiven type are roughly structurally equivalent.We therefore construct a measure of allianceconfiguration efficiency that captures the diversityof a startup’s alliance partner types at founding.This measure is based on the Hirschman-Herfindahl index, and computes diversity as oneminus the sum of the squared proportions of aBF’s alliances with each of the nine partner typesdivided by the startup’s total number of alliancesat founding. Scaling by total network size permitsthe variable to capture the extent to which thealliances comprising each startup BF’s networkare, on average, redundant. Specifically,

Network Efficiencyi = [1 − Sij (PAij )2]/NAi

where PAij is the proportion of all startupi’salliances that are with partner typej, and NAi isstartup i’s total number of alliances. A startupwith six alliances, two with pharmaceutical firms,two with hospitals, and two with marketing firmswould score [1−(2/6)2+(2/6)2+(2/6)2]/6 = 0.111.Another, with a less ‘efficient’ network comprisedof five alliances with pharmaceutical firms andone marketing alliance, would score[1−(5/6)2+(1/6)2]/6 = 0.046. Network efficiencywas coded zero for 13 startups with no alliancesat founding (i.e.,NAi = 0).15

Potential rival partner BFs’ relative scope and

15 Coefficients for network efficiency were not affected byexcluding these firms from the sample.


innovativeness at founding.16 To test Hypothesis4, we computed a measure of the relationshipbetween the scope of a focal BF and the scopeof those BFs with which it allies. This measurecomputes relative scope as the number of biotech-nology sectors in which a focal BF participatesdivided by the average number of biotechnologysectors in which its BF partners participate.Specifically

Relative Scopei = Si /(SkSk/NABFi)

where k includes all potential rival BFs alliedwith the focal BF i at the time of its founding,Si andSk are the number of biotechnology sectorsin which BFsi andk were active at the time ofi’sfounding, andNABFi is startupi’s total number ofalliances with potential rival BFs.

To test Hypothesis 5 we measured potentialrival partner BFs’ innovativeness using infor-mation on the patenting success of all BFs alliedwith a startup BF at the time of its founding.17

We used the Micropatent data base to determinethe number of patents granted to BFs allied witheach startup BF in the five years prior to thestartup’s founding using the same measurementprocedure as described for the dependent patentvariable.

Control variables

Many other factors may influence the perform-ance of startup BFs. Accordingly, the analysiscontrols for a variety of additional BF character-istics and industry-specific environmental factors.

Organizational factors. An additional foundingcondition that may affect a startup’s performanceis its initial endowment of, for example, financialassets, intellectual property, or product perform-ance. High values on such factors should bothlower the early risk of failure (Fichman and

16 Because we lack firm-level data for foreign BFs, partners’relative scope and innovativeness were computed only forCanadian partner BFs, which accounted for more than 90%of all Canadian startups’ alliances with other BFs.17 This differs from Stuart’s (1998a, b) operationalization ofpartner innovativeness based on patent citations. We do notweight partner’s (or own) patents by their citations becausethe patent approval process creates a three (or more) yearlag between a patent’s granting and its citation in later patents.Reliance on citations is particularly appropriate for Stuartgiven his emphasis of the ‘technological status’ of firms,which differs from our emphasis.


Levinthal, 1991) and step up the pace of growth(Eisenhardt and Schoonhoven, 1990). We controlfor variation in initial endowments by includingthe number of patents granted to each startup atthe time of its founding. Using the Micropatentdata base, we assigned values for the number ofpatents granted to startups at the time of theirfounding in the manner described above for thedependent variable. Firm-level data on financialassets and product performance at founding arenot available.

Performance may also vary with the industrysector that startup BFs emphasize. In particular,compared to nonhuman sectors, commerciali-zation is most taxing for developments in humantherapeutics and vaccines where rigorous clinicaltrials and regulations inhibit speedy implemen-tation, and somewhat less so for human diagnos-tics (about half of which arein vitro and half invivo) (Powell and Brantley, 1992: 369, also note18). We control for possible performance differ-ences between startups focused on human andnonhuman applications with a dummy variablecoded one for startups most active in humanmedical applications (i.e., human diagnostics,therapeutics or vaccines) and zero otherwise. Wealso control for the degree of diversification,defined as the total number of biotechnologysectors in which a startup BF was active at thetime of its founding.

Ownership differences at founding may alsoinfluence initial performance. For example, for-profit startups may be more focused on increasingeconomic performance than nonprofit startups’operators (Hansmann, 1980). We account forsuch possibilities by including a set of dummyvariables in the analysis. Each variable was coded1 if the startup BF was founded as 1) private,2) public, 3) nonprofit, 4) government, university,or hospital, 5) subsidiary, 6) joint venture, and0 otherwise. Private was used as the holdoutcomparison category. Finally, startup performancemay also be affected by whether or not thestartup operates its own research lab and/ormanufacturing facility. Therefore, we includedtwo dummy variables, coded 1 if a BF operatedits own research lab or manufacturing facilityat the time of founding, and 0 otherwise, inthe analysis.

Current performance on one dimension (e.g.,patenting) may also depend on lagged perform-ance on another dimension (e.g., R&D


expenditures). Consequently, in addition to thelagged dependent variable, we include laggedvalues for all other dependent variables in eachperformance equation. Finally, in addition to theforegoing characteristics of startups at the timeof their founding, we control for startup age,defined as the number of years since founding,in our models to ensure that any significanteffects of our theoretical variables were not sim-ply a spurious result of aging-related growth.

Environmental factors. We also controlled forseveral factors influencing the carrying capacityfor and intensity of competition within thebiotechnology field in Canada. First, we obtainedyearly information on aggregate financing of BFsfrom all sources (e.g., venture capital, privateplacement, IPO, public offering, and other) bybiotechnology sector from the National ResearchCouncil of Canada. To control for initial andcontemporaneous effects of financing on startupBF performance, we constructed a variable thatmeasured the total financing (in 1991 constantdollars) in all sectors in which a startup BF wasactive at the time of its founding and the startof each subsequent year. Second, we obtainedyearly data on M.Sc. and Ph.D. degrees in agri-culture and biology subspecialties granted byCanadian universities. Using this information wecreated labor supply variables computed as thelogged total M.Sc. and Ph.D.-level graduates inthe subspecialties most relevant to the sectors inwhich startup BFs were active at the time offounding and the start of each subsequent year.18

The resource opportunities available to startupsdepend on the intensity of competition at the timethe funding is added. If potential competition isnot measured explicitly, then the effect of addingresources to the environment is assumed to beconstant over time, and estimates for the effectsof financing and labor supply will suffer speci-

18 Sector-specific labor supplies were defined as follows:human (genetics, microbiology, biochemistry, toxicology),agriculture and horticulture (plant science, soil science, ge-netics, botany, food science), aquaculture (animal science,genetics, fish and wildlife, food science, veterinary medicine,veterinary science, zoology), forestry (plant science, soilscience, botany), engineering (microbiology, biochemistry,toxicology), environmental (microbiology, biochemistry,toxicology), food, beverage and fermentation (microbiology,biochemistry, food science, toxicology), veterinary (animalscience, genetics, microbiology, biochemistry, fish and wild-life, veterinary medicine, veterinary science, zoology) andenergy (microbiology, biochemistry, toxicology).


fication bias. Intense competition makes it partic-ularly difficult for new firms to survive and grow.In addition to contemporaneous effects of compe-tition, Carroll and Hannan (1989) argue that nichecrowding and resource scarcity experienced atfounding as a result of competition forces a startupto rely on more peripheral and inferior resources,and reduces its ability to adapt. They have shownthat greater competition for resources at the timeof a firm’s founding is associated with a higherrisk of failure throughout the firm’s life in multiplepopulations. This ‘imprinting’ effect of competitionat founding operates in addition to contempor-aneous effects of competition. Therefore, weinclude measures of a startup BF’s potential for‘mass-dependent’ (Barnett and Amburgey, 1990)and ‘patent-dependent’ (Baum and Silverman,1998) competition.

We measure a startup’s potential for mass-dependent competition as the aggregate size(measured as the sum of R&D and non-R&Demployees) of its potential rival BFs (i.e., allBFs—startups and incumbents—operating in oneor more overlapping market sectors with thestartup). We measure the potential for patent-dependent competition, analogously, as the num-ber of patents granted to all of a startup BF’spotential rivals using the procedure described ear-lier. To account for imprinting and contempo-raneous competitive effects, we compute thesevariables both at the time of a startup’s foundingand at the start of each subsequent year.

In addition, we compute rival BFs’ patent vari-ables for two time frames: recent patents grantedwithin the last five years, and past patents grantedbetween 1975 and five years ago (Stuart andPodolny, 1996). This disaggregation permits us toexamine the possibility that knowledge diffusionrelated to patents granted in the past (.5 yearsago) produces positive externalities (Amburgey,Dacin and Singh, 1996) that stimulate startupgrowth, while competitive effects predominateshortly after patent races are concluded (within 5years). This disaggregation also permits us to gaugeand control for the rate of technological innovation,which plays a crucial role in shaping resource andmarket opportunities for technology-based ventures(Eisenhardt and Schoonhoven, 1990).

Finally, given the proclivity of Canadian BFsto patent in and export to the U.S., we accountfor possible competition from U.S. BFs by includ-ing a count of the number of BFs active in all


sectors of the U.S. biotechnology industry at thestart of each observation year (CanadianBiotech, 1997).

Means, standard deviations and correlations forall variables are given in the Appendix. Althoughthe correlations are generally small in magnitude,correlations between time-of-founding and con-temporaneous specifications are somewhat larger.Such levels of multicollinearity among explana-tory variables can result in less precise parameterestimates (i.e., larger standard errors) for corre-lated variables but will not bias parameter esti-mates (Kennedy, 1992). So, although this doesnot pose a serious estimation problem, it canmake it difficult to draw inferences about theeffects of adding particular variables to the mod-els. Therefore, when estimating results, we fol-lowed a strategy of estimating hierarchically-nested models to check that multicollinearity wasnot causing less precise parameter estimates(Kmenta, 1971).

RESULTS

Table 2 reports baseline random effects GLSestimates for startups’ rates of revenue, employee(R&D and non-R&D) and R&D spending growth,and Poisson regression models for startups’ pa-tenting rate. Models in Table 2 include thecharacteristics of the startup and environmentalconditions at the time of founding and contempo-raneous (i.e., t−1) environmental conditions. Thevariables in each model are identical except forthe dependent variable. Baseline estimates forfounding-condition and contemporaneous controlvariables are generally in expected directions andexhibit robust independent effects; that is, bothfounding conditions and contemporaneous factorssignificantly affect startup growth. Notably, base-line model estimates reinforce the general findingof other studies that extra-organizational factorsare more influential than intra-organizational fac-tors in shaping startups’ fates.

Models in Table 3 present tests of our hypoth-eses by adding variables to estimate the effectsof startups’ alliance networks at founding ontheir initial performance.19 Estimates in Table 3

19 We also ran models including a set of year dummy variablesto account for any time-period effects. Coefficients in thesemodels do not differ substantively from those in Table 3.


Table 2. Baseline models of startup performance: Founding and contemporaneous conditions

Non-R&D R&D R&DVariables Revenues employees employees expenses Patents

Constant −3.584* −1.126 −1.373 6.931* 3.119*(1.032) (1.015) (1.109) (0.613) (1.327)

Startup characteristicsRevenues (t − 1) 0.908* 0.069 0.037 0.020 −0.041

(0.032) (0.062) (0.046) (0.029) (0.148)Non-R&D employees (t − 1) −0.013 0.769* 0.205* −0.024 0.029

(0.036) (0.074) (0.035) (0.028) (0.197)R&D employees (t − 1) 0.122* 0.239* 0.647* 0.123* 0.627*

(0.046) (0.066) (0.039) (0.058) (0.184)R&D expenses (t − 1) −0.061 0.017 0.256* 0.751* 0.010

(0.056) (0.058) (0.054) (0.093) (0.099)Patents (t − 1) −0.020 0.008 0.034 0.159* 0.644*

(0.039) (0.027) (0.029) (0.041) (0.046)Age 0.056* 0.101* −0.022 −0.019 −0.028

(0.021) (0.030) (0.022) (0.018) (0.169)Diversification (tfound) −0.021 −0.026 0.005 −0.010 −0.381*

(0.021) (0.024) (0.023) (0.018) (0.175)Human sector (tfound) −0.046 0.006 0.078 0.149* −0.086

(0.102) (0.067) (0.078) (0.051) (0.522)Public (tfound) 0.039 −0.189* 0.135* −0.024 0.191

(0.082) (0.090) (0.082) (0.084) (0.177)Private (tfound) – – – – –Nonprofit (tfound) −0.192 −0.505* 0.669* −0.174* −0.061

(0.158) (0.183) (0.262) (0.100) (0.640)Govt./university/hospital (tfound) −0.092 −0.116 0.245+ −0.100 −0.018

(0.108) (0.082) (0.152) (0.086) (0.362)Subsidiary (tfound) 0.132 0.005 −0.142* 0.098* −0.066

(0.093) (0.086) (0.075) (0.054) (0.185)Joint venture (tfound) 0.482 −0.118 0.081 0.248* 0.176

(0.537) (0.122) (0.154) (0.110) (0.368)Manufacturing facility (tfound) 0.073 0.084* −0.042 0.059* −1.301

(0.071) (0.045) (0.050) (0.036) (1.069)Laboratory facility (tfound) 0.100 0.036 −0.056 0.135* 1.057*

(0.077) (0.061) (0.069) (0.058) (0.344)Patents granted (tfound) 0.210* −0.030 −0.198 0.057 −0.053

(0.147) (00.175) (0.217) (0.156) (0.091)

(Continued)

provide substantial evidence in support ofHypothesis 1. In particular, startups with:

I pharmaceutical companyalliances at foundingexperienced higher rates of patenting andgrowth in revenue, R&D and non-R&D employment, and R&D spending

I university alliances at founding experiencedhigher rates of patenting and revenue growth

I government laballiances at founding experi-enced higher rates of patenting and growth inR&D employment and spending

I research institutealliances at founding experi-enced a higher rate of R&D spending, andR&D and non-R&D employment growth

I marketing alliances at founding experienced


higher rates of growth in revenue, R&D andnon-R&D employment and R&D spending

In contrast to Hypothesis 1, several types ofalliances at founding slowed initial performancegrowth significantly. Industry association mem-bership lowered revenue and R&D spendinggrowth. Alliances withgovernment labsalso low-ered startups’ rate of revenue growth. One pos-sible explanation for the apparent performance-dampening impact of industry association mem-bership is that the founders of startups that joinindustry associations at the time of founding doso to compensate for their own lack of personalnetwork ties. Thus, early industry associationmembership may reflect the inexperience of start-


Table 2. (Continued)


Environmental characteristicsSector biofinancing ($m) (tfound) 0.001 0.0008* 0.0006 0.0016* 0.0071*

(0.001) (0.0005) (0.0008) (0.0008) (0.0033)Sector biofinancing ($m) (t − 1) 0.00023+ 0.0003* 0.0002* 0.0002* 0.0011*

(0.00015) (0.00014) (0.0001) (0.0001) (0.0005)MSc & PhD degrees (tfound) 0.0007* 0.0015* 0.0003 0.0007+ 0.082+

(0.0004) (0.0006) (0.0005) (0.00045) (0.060)MSc & PhD degrees (t − 1) 0.0002* −0.0004* 0.0001 −0.0002* 0.005

(0.0001) (0.0001) (0.0001) (0.0001) (0.033)Rivals’ mass (tfound) 0.0009* 0.00007* 0.00002 0.00014* −0.0009

(0.0005) (0.00004) (0.00004) (0.00007) (0.0038)Rivals’ mass (t − 1) −0.0009* −0.00010* −0.00008* −0.00010* 0.0069*

(0.0004) (0.00004) (0.00003) (0.00003) (0.0032)Rivals’ patents issued within 5 years −0.005 −0.002 −0.004 −0.005* 0.047*(tfound) (0.004) (0.003) (0.003) (0.003) (0.024)Rivals’ patents issued within 5 years −0.007* −0.005* 0.005* 0.003+ 0.037*(t − 1) (0.003) (0.003) (0.003) (0.002) (0.015)Rivals’ patents issued.5 years ago 0.002 0.004 −0.006* −0.006* −0.153*(tfound) (0.002) (0.007) (0.002) (0.003) (0.090)Rivals’ patents issued.5 years ago 0.021* −0.010 0.008 −0.004 −0.093*(t − 1) (0.008) (0.007) (0.009) (0.006) (0.054)U.S. biotechnology firms (t − 1) 0.021* −0.0011* 0.0017* −0.005* −0.013*

(0.006) (0.0005) (0.0008) (0.001) (0.005)Log-likelihood −59.246* −76.862* −66.644* −36.206* −101.719*

*p , 0.05; +p , 0.10; standard errors are in parentheses;N = 369. Models are random-effects GLS except for patents,which is Poisson. All dependent variables, with the exception of patents, are logged.

ups’ founders, which can hamper startup perform-ance (Eisenhardt and Schoonhoven, 1990). Takentogether, the negative effect of alliances withgovernment labs on startups’ revenue growth andthe positive effects of these alliances on ratesof patenting and growth in R&D spending andemployment may be indicative of the commercialuncertainty of research projects in which govern-ment labs become involved (Powell and Brantley,1992: 380).

Somewhat surprisingly, although not harmful,alliances with chemical companies did not benefitstartups’ performance as we had expected. Thisnon-result appears, primarily, to reflect the growthpotential and patenting characteristics of theindustry subsectors in which startup BFs alliedwith chemical firms typically operate. Our dataindicate that startup BFs in horticulture, energy,engineering and environmental industry subsec-tors were most likely to form a chemical firmalliance at founding. In contrast, startups in thehuman industry subsectors (therapeutics, vaccinesand diagnostics) were least likely to establishsuch an alliance. A contributing factor may bethat the appropriability regime in the industry


subsectors in which alliances with chemical firmsare more common allows them to more fullyexploit the startups who partner with them(Powell and Brantley, 1992: 391).

The panels in Figure 1 present graphically theoverall performance implications of the modelsin Table 3. Figure 1 shows how establishing analliance of the indicated type at founding influ-ences startup performance over time across alldimensions. In the figure, a multiplier of greater(less) than 1 indicates that the performancegrowth rate is increased (decreased) relative tothe baseline rate by a factor equal to the multi-plier. Thus, a multiplier of 1.6 (0.4) represents a60% increase (decrease) relative to a startup with-out an alliance of the indicated type at founding.The figures estimate startup performance over theobserved range of ages (1–6 years), and simulateit for four additional years. Performance on eachdimension att1 was set equal to the mean per-formance of sample BFs in their startup year;performance in yearst2 − t10 was then estimatediteratively using model coefficients from Table 3.As the figures show, establishing different typesof alliances at founding produce quantitatively


Table 3. Effects of alliance networks at founding on startup performance


Constant −3.693* −1.056 −1.627+ 6.577* 2.589*(0.915) (1.044) (1.128) (1.604) (1.355)

Startup characteristicsRevenues (t − 1) 0.879* 0.084 0.047 0.047+ −0.049

(0.050) (0.062) (0.053) (0.032) (0.149)Non-R&D employees (t − 1) 0.007 0.754* 0.213* −0.050* −0.016

(0.036) (0.075) (0.037) (0.029) (0.198)R&D employees (t − 1) 0.125+ 0.224* 0.635* 0.109* 0.617*

(0.073) (0.066) (0.042) (0.056) (0.186)R&D expenses (t −1 ) −0.020 −0.059 0.278* 0.700* 0.019

(0.063) (0.061) (0.063) (0.093) (0.099)Patents (t − 1) −0.026 0.017 0.015 0.156* 0.639*

(0.036) (0.028) (0.029) (0.058) (0.047)Age 0.051* 0.106* −0.022 −0.017 −0.033

(0.022) (0.034) (0.026) (0.019) (0.169)Diversification (tfound) −0.030+ −0.013 0.028 −0.006 −0.392*

(0.022) (0.026) (0.030) (0.017) (0.177)Human sector (tfound) −0.026 0.043 0.063 0.112* −0.091

(0.085) (0.077) (0.078) (0.055) (0.524)Public (tfound) 0.056 −0.183* 0.134+ −0.009 0.129

(0.080) (0.100) (0.093) (0.084) (0.178)Private (tfound) – – – – –Nonprofit (tfound) −0.184 −0.503* 0.486* −0.137 −0.062

(0.169) (0.189) (0.278) (0.115) (0.633)Govt./university/hospital (tfound) −0.120+ −0.143+ 0.203 −0.030 0.027

(0.083) (0.098) (0.161) (0.081) (0.373)Subsidiary (tfound) 0.112 0.012 −0.150* 0.087+ −0.077

(0.091) (0.090) (0.084) (0.058) (0.186)Joint venture (tfound) 0.476 −0.159 −0.064 0.232* 0.169

(0.515) (0.140) (0.161) (0.114) (0.369)Manufacturing facility (tfound) 0.051 0.086* −0.081 0.052+ −1.403

(0.071) (0.048) (0.054) (0.039) (1.077)Laboratory facility (tfound) 0.183* −0.056 −0.079 0.143* 0.993*

(0.095) (0.083) (0.084) (0.065) (0.345)Patents granted (tfound) 0.202+ 0.031 −0.196 0.095 −0.054

(0.151) (0.166) (0.217) (0.151) (0.092)

Environmental characteristicsSector biofinancing ($m) (tfound) 0.001 0.0022* 0.0008 0.0014* 0.0069*

(0.001) (0.0008) (0.0008) (0.0008) (0.0033)Sector biofinancing ($m) (t − 1) 0.00025* 0.0003* 0.0002* 0.0002* 0.0010*

(0.00015) (0.00015) (0.0001) (0.0001) (0.0005)MSc & PhD degrees (tfound) 0.0007 0.0020* 0.0006 −0.0002 0.092+

(0.0005) (0.0008) (0.0007) (0.0005) (0.064)MSc & PhD degrees (t − 1) 0.0002* −0.0005* −0.0001 −0.0002* 0.003

(0.0001) (0.0001) (0.0001) (0.0001) (0.036)Rivals’ mass (tfound) 0.0008* 0.00007* 0.00001 0.00013* −0.0013

(0.0004) (0.00004) (0.00004) (0.00007) (0.0038)Rivals’ mass (t − 1) −0.0009* −0.00010* −0.00008* −0.00009* 0.0066*

(0.0004) (0.00004) (0.00004) (0.00003) (0.0033)Rivals’ patents issued within 5 years −0.006 −0.001 −0.002 −0.003 0.046*(tfound) (0.004) (0.003) (0.004) (0.003) (0.022)Rivals’ patents issued within 5 years −0.007* −0.005* 0.005* −0.002 0.035*(t − 1) (0.003) (0.003) (0.003) (0.002) (0.016)Rivals’ patents issued.5 years ago 0.002 0.003 −0.006* −0.005* −0.156*(tfound) (0.002) (0.008) (0.002) (0.003) (0.090)Rivals’ patents issued.5 years ago 0.022* −0.010 0.006 −0.005 −0.096*(t − 1) (0.008) (0.009) (0.009) (0.006) (0.055)U.S. biotechnology firms (t − 1) 0.024* −0.0017* 0.0020* −0.005* −0.011*

(0.006) (0.0007) (0.0010) (0.001) (0.005)

(Continued Overleaf)



Table 3. (Continued)


Alliance network at foundingPharmaceutial co. H1(+) [5/5]a 0.203* 0.168* 0.142* 0.099* 0.431*(tfound) (0.086) (0.078) (0.078) (0.051) (0.155)Chemical co. H1(+) [0/5] −0.061 −0.054 −0.067 0.012 −0.172(tfound) (0.111) (0.087) (0.102) (0.077) (0.167)University (tfound) H1(+) [2/5] 0.191* 0.020 0.042 −0.051 0.153*

(0.062) (0.057) (0.060) (0.043) (0.081)Research institute H1(+) [3/5] 0.014 0.143* 0.117* 0.147* 0.062(tfound) (0.074) (0.059) (0.064) (0.052) (0.137)Government lab H1(+) [3/5] −0.268* −0.034 0.175* 0.298* 0.444*(tfound) (0.134) (0.060) (0.063) (0.065) (0.272)Industry assn. H1(+) [0/5] −0.194* 0.101 −0.055 −0.181* −0.114(tfound) (0.099) (0.082) (0.081) (0.108) (0.303)Marketing co. H1(+) [4/5] 0.147* 0.223* 0.106* 0.186* −0.077(tfound) (0.069) (0.109) (0.059) (0.062) (0.159)Network H2(+) [3/5] 0.407* −0.131 −0.024 0.420* 2.803*efficiency (0.223) (0.300) (0.318) (0.246) (0.322)(tfound)Rival partner BF H3(−) [3/5] −0.439* −0.070 0.063 −0.082* −0.311*(tfound) (0.166) (0.052) (0.060) (0.036) (0.118)Partner BFs’ H4(+) [2/5] 0.060 −0.003 −0.060 0.120* 0.188*relative (0.091) (0.074) (0.062) (0.064) (0.107)scope (tfound)Partner BFs’ H5(+) [2/5] 0.002 −0.010 0.006 0.028* 0.063*patents (0.019) (0.008) (0.007) (0.013) (0.036)w/in 5 years(tfound)Partner BFs’ H4× H5(+) 0.136* 0.103 −0.010 0.180* 0.052*relative [3/5] (0.063) (0.087) (0.040) (0.043) (0.029)scope× patentsw/in5 years (tfound)

Log-likelihood −44.493* −69.590* −57.706* −20.772* −54.500*Likelihood ratio vs. Table 2 models 29.506* 14.544 17.876 30.868* 94.438*(12 d.f.)

*p , 0.05; +p , 0.10; standard errors are in parentheses;N = 369. Models are random-effects GLS except for patents,which is Poisson. All dependent variables, with the exception of patents, are logged.aIndicates prediction and number of estimated coefficients significant in predicted direction.

and qualitatively different trajectories of startupperformance that quickly create substantial across-the-board performance differences.

Coefficients for network configurationefficiency at the time of founding supportHypothesis 2 for three of five performance vari-ables. The significant, positive coefficients fornetwork efficiency at founding indicate thathigher founding network efficiency (i.e., networksthat provide access to more diverse informationand capabilities per alliance) raised rates of rev-enue and R&D spending growth and patenting.Network configuration has a particularly powerfuleffect on startups’ rate of patenting.

Hypothesis 3, which predicted weaker initial


performance for startups founded with allianceswith potential rival BFs, is supported for thesame three performance indicators: startups thathad alliances with potential rival BFs at the timethey were founded exhibited slower rates of pa-tenting and revenue and R&D spending growth.

For patenting and R&D spending (but notrevenue) growth, however, performance-weakening effects of alliances with rival BFswere moderated by the potential rival partners’relative scope and innovativeness, as predictedby Hypothesis 4 and 5. The significant positivecoefficients for partner BFs’ relative scope sup-port the idea underlying Hypothesis 4 that startupswith broader market domains than potential rival


Figure 1. Estimated effects of founding alliances on Startup Performance



Figure 2. Estimated effects of rival partner’s relative scope and innovativeness

BFs with which they are allied are less likely toexperience harmful effects of strong partner learn-ing incentives. Hypothesis 5 is supported by thesignificant positive coefficient for partner BFs’patents granted in the last 5 years. Moreover, thesignificant positive coefficients for the interactionof partners’ relative scope and technological inno-vativeness support our speculation that the effectsof these partner characteristics are mutually rein-forcing.20 Thus, opportunities to learn new rou-tines and acquire advanced technologicalknowhow from potential rivals who are skilledinnovators moderates the risk to early perform-ance growth associated with allying with them.

Figure 2 shows graphically how a startup’spartners’ relative scope and innovativeness attenu-ate and even overwhelm the negative performanceconsequences of allying with them at founding.For purposes of illustration, this figure comparesthe patenting rate of a startup with no partnerBFs to patenting rates of BFs with potential rivalBF partners of various combinations of relativescope (between 0.1 and 1.7) and innovativeness(between 0 and 7 patents) in the observed range.A multiplier of greater (less) than 1 indicates thepatenting rate is increased (decreased) relative toa startup with no potential rival partner BFs bya factor equal to the multiplier. As the figureshows, startups with highly innovative potential

20 The pattern of significance of coefficients for partners’relative scope and innovativeness (i.e., patents granted withinthe last 5 years) is not altered by the exclusion of theinteraction term. As reported in Table 3, neither coefficientis significant in revenue and employment growth models; bothare significant (p , 0.05) and positive in R&D expensegrowth and the patent rate models.


rival partner BFs always have higher patentingrates than startups without partner BFs—evenwhen they face a severe scope disadvantage (i.e.,relative scope= 0.1). By comparison, startupswith non-innovative potential rival partner BFsalways exhibit lower patenting rates than BFswithout such partners, although the performancegap becomes quite small for startups’ with asizable scope advantage vis a` vis their potentialrival BF partners. Thus, specialist startup BFsmay be able to overcome the patent-rate dampen-ing effects of learning races with more estab-lished, generalist potential rival BFs by tradingtheir specialized knowledge for access to gen-eralists’ broader technological knowhow from therecent past; in other words, by balancing asym-metric learning incentives for asymmetricknowhow.

Figure 3 depicts the overall performance impli-cations of startups’ alliances with rivals at found-ing. The panels in Figure 3 show how partneringwith a potential rival at founding influencesstartup performance over time across all dimen-sions relative to a startup without a rival partner.The figure plots performance multipliers for start-ups with a rival partner scoring at the mean onrelative scope and recent patenting, as well as onestandard deviation above and below the mean. Asbefore, the figures estimate startup performanceover the observed range of ages (1–6 years), andsimulate it for an additional four years. Perform-ance values were initialized and estimated in thesame way as Figure 1. As the middle panel inthe figure shows, the average partnership with apotential rival was detrimental to startup perform-ance, driving it downward across all dimensions


Figure 3. Estimated effects of founding alliances on Startup Performance



relative to a startup without a rival partner. Thetop panel shows the devastating effect of part-nering with a rival with a scope advantage andpoor patenting record. The bottom panel shows,in contrast, the generally performance-enhancingeffect of partnering with a rival with a scopedisadvantage and strong patenting record.

DISCUSSION AND CONCLUSION

This paper, which investigated how variation inthe alliance networks that biotechnology startupsconfigure at founding shapes their initial perform-ance, was motivated by our desire to test thetaken-for-granted assumption that new entrantstypically lack stable relationships and sufficientresources, creating a liability of newness and/orsmallness. A secondary motivation was to expandthe focus of research on the hazards facing start-ups beyond failure as the outcome of interest. Byextending research on new ventures, this paperprovides insight into how observed variationamong startups in their access to resources andstrategic partnerships shapes performance differ-ences among startups.

We predicted that startups could enhance theirinitial performance by 1) establishing alliances,2) configuring them into an efficient networkthat provides access to diverse information andcapabilities with minimum costs of redundancy,conflict, and complexity, and 3) allying withestablished rivals that provide more opportunityfor learning and less risk of intra-alliance rivalry.Our analysis provides some support for each ofour hypotheses. Startup BFs that, at the time oftheir founding, formed upstream and downstreamalliances (H1) and configured them to provideaccess to more diverse information and capabili-ties per alliance (H2) generally exhibited strongerinitial performance. In contrast, startups thatinitially formed alliances with established poten-tial rivals tended to experience weaker perform-ance, on average (H3). However, alliances withpotential rivals were not universally harmful,varying systematically with the partner BFs’ rela-tive scope (H4) and innovativeness (H5). Takentogether, our findings show how variation in start-ups’ alliance network composition rapidly pro-duces significant differences in their performance,supporting the idea that liabilities of newness andsmallness result, to a large extent, from a lack


of access to resources and stable exchangerelationships. These results also contributedirectly to an explanation of how and why firmage and size affect firm performance.

Although each of the five performance meas-ures we examined was affected by startups’alliance networks, innovative performance,reflected in startups’ patenting and R&D spendinggrowth, was most clearly and most stronglyinfluenced in line with our predictions. Thestronger impact on innovation-related perform-ance is consistent with the widely held belief thatalliance networks form a ‘locus of innovation’ inhigh-technology fields (e.g., Powellet al., 1996).It is also consistent with the alliance literature’semphasis on alliances as mechanisms to access ortransfer technological knowledge and to facilitateinnovative efforts (e.g., Mody, 1993; Teece,1992)—a characterization particularly germane tohyper-innovative industries such as biotechnology(McGrath and McGrath, 1999).

The linkage between the composition of start-ups’ alliance networks at founding, innovationand other measures of startups’ performance indi-cates further research questions and opportunities.Given the strong theoretical link betweenalliances and innovation, and given the relativelystronger empirical evidence for alliance effectson innovation than for alliance effects on othermeasures of firm performance, to what degree doalliances positively influence firm performanceother than through enhancing innovation?Although there is by now a substantial literatureon the benefits associated with alliances, it is notclear whether these benefits arise directly fromalliance participation or rather as second-ordereffects of the innovation-enhancing characteristicsof alliances. Although some research has begunto unpack these effects (e.g., Baum and Sil-verman, 1998), further disaggregation of the pro-posed benefits of alliances on startups, and onfirms more generally, is warranted.

Furthermore, although our analysis tracks start-ups’ differential performance for up to five years,Stinchcombe’s (1965) imprinting argumentsimply long-lasting—perhaps permanent—effectsof founding conditions. How immutable are per-formance differences arising from compositionaldifferences in startups’ alliance networks atfounding? Put differently, how sensitive to initialconditions, i.e., path-dependent, are startups’ per-formance growth trajectories? We think highly


so. By configuring effective alliance networks atfounding, startups access social, technical, andcommercial competitive resources that normallyrequire years of operating experience to accumu-late, buffering themselves from hazards typicallyfaced by new firms and sowing seeds of futureopportunities to develop their alliance networks.Startups that fail to configure effective alliancenetworks at founding, in contrast, are likely tosuffer conditions of resource scarcity, forced torely on more peripheral resources, and relegatedto the periphery of the industry. As a result,efforts to shift from organizing to operations arehampered, employees have few chances or incen-tives to invest in learning and refining organi-zation-specific routines, and recovery from suchinitial deprivations is taxing. Consequently, it ispossible that firms that fail to configure effectivealliance networks at the time they are foundedwill be inferior competitors atevery age.

Our study also provides some clear impli-cations for practicing managers of startups. Themost important prescription is embedded in ourtitle: ‘Don’t go it alone.’ While startups are fre-quently characterized by a lack of resources andexchange relationships with other actors in theirenvironment, they can potentially overcome manyof the attendant hazards through judicious estab-lishment of inter-firm alliances, particularly verti-cal alliances. In high-technology businesses suchas biotechnology, these alliances may be partic-ularly effective for enhancing innovation; in allbusinesses they may facilitate access to comple-mentary assets necessary for successful growth.

Regarding the judicious establishment ofalliances, two prescriptions follow. First, startups’founders should attend carefully to the configur-ation as well as the number of partners theycultivate. Multiple alliances with similar partnersmay yield fewer benefits than alliances with dif-ferentiated partners, both because same-typealliances offer access to less diverse pools ofinformation and because a startup engaging inmultiple same-type alliances—which often meansallying with firms that are each others’competitors—may spark conflict that reduces theutility of an alliance. Second, managers of start-ups must carefully consider which potential rivalsmake the most beneficial partners. Alliances withpotential rivals are particularly susceptible to thedeleterious effects of intra-alliance competition.Managers of startups may be able to overcome


these problems by managing asymmetric learningincentives and asymmetric technological exper-tise. Allying with an appropriate potential rivalmay enable a manager to trade her startup’sspecialized knowledge for access to the potentialrivals’ broader technological knowhow. Notably,however, the correlation of 0.49 between partners’relative scope and patents granted within the lastfive years for the subset of startups that estab-lished alliances with potential rivals at the timeof founding indicates that their foundersdo nottypically engage in such tradeoffs.

Of course, attempting to follow these rec-ommendations represents a significant entrepre-neurial challenge. As Hite and Hesterly’s (1999)analysis suggests, the alliance networks thatentrepreneurs create at the time of founding arelikely to depend heavily on their prior social andwork-related ties, and this may severely limit therange of possible partners. And, as Ahuja (2000)demonstrates empirically, lacking social, technicaland commercial capital, nascent startups withouttrack records of their own are likely to havelimited opportunity to pursue such prescriptions,and the most valuable potential collaborators havelittle interest in partnering with them. Conse-quently, future research examining the interrelateddynamics and evolution of firms’ social, technicaland commercial capital over organizational lifecycles seems critical to refining our ability tomore effectively counsel entrepreneurial ventures.

ACKNOWLEDGEMENTS

We are grateful to Fred Haynes (ContactInternational) and Denys Cooper (NRC) for per-mitting us access to their data. Silvermanacknowledges financial support from the Divisionof Research at Harvard Business School. Forhelpful comments and suggestions we thankSMJ’s Special Issue editors and anonymousreviewers, Bharat Anand, Ken Corts, John deFigueiredo, Mike Ryall, Myles Shaver, and sem-inar and conference participants at theSMJ Spe-cial Issue Conference, Kellogg Graduate Schoolof Management, Northwestern University, theIvey School of Business, University of WesternOntario, and the Strategy Research Forum, BostonMA. Whitney Berta, Jack Crane and Igor Kotlyarprovided expert help with data collection andcoding.


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Uzzi, B. (1997). ‘Social structure and competition ininterfirm networks: The paradox of embeddedness’,Administrative Science Quarterly, 42, pp. 35–67.

APPENDIXMeans and bivariate correlations (n = 369)

Variable Mean S.D. 1 2 3 4 5 6 7

Panel A

1. Log revenues (t − 1) 8.39 6.712. Log non-R&D employees (t − 1) 2.19 0.94 0.053. Log R&D employees (t − 1) 2.00 0.96 −0.06 0.704. Log R&D expenses (t − 1) 0.53 0.63 −0.08 0.46 0.625. Patents granted (t − 1) 0.25 0.96 0.10 0.16 0.18 0.106. Age 3.12 1.52 0.10 0.12 −0.02 −0.02 −0.037. Diversification (tfound) 2.29 1.15 0.18 0.09 0.17 0.02 0.11−0.118. Human sector (tfound) 0.58 0.50 −0.14 0.23 0.28 0.29 −0.02 0.06 −0.229. Public (tfound) 0.15 0.36 0.20 0.25 0.39 0.42 0.29−0.03 0.03

10. Nonprofit (tfound) 0.01 0.09 0.06 −0.11 0.12 0.00 −0.02 −0.04 0.1411. Govt./university/hospital (tfound) 0.03 0.18 −0.18 0.25 0.28 −0.06 −0.04 0.00 0.3812. Subsidiary (tfound) 0.14 0.35 −0.07 0.31 −0.13 0.22 −0.00 0.00 −0.1313. Joint venture (tfound) 0.03 0.16 −0.13 0.02 0.11 0.09 0.03 −0.08 −0.0914. Manufacturing facility (tfound) 0.51 0.50 0.16 0.13 −0.04 0.15 −0.11 0.02 0.0615. Laboratory facility (tfound) 0.11 0.32 0.06 −0.20 −0.24 0.17 −0.12 0.05 0.0516. Patents granted (tfound) 0.02 0.14 0.11 0.05 0.03 0.12 0.25−0.07 −0.1017. Sector biofinancing ($m) (tfound) 78.82 72.13 −0.06 0.25 0.22 0.22 0.07 0.16 −0.3318. Sector biofinancing ($m) (t − 1) 218.8 276.7 −0.09 0.29 0.23 0.18 0.19 0.41 −0.0919. Log (MSc & PhD degrees) (tfound) 5.98 1.22 −0.25 −0.05 0.11 0.15 −0.12 0.04 −0.0620. Log (MSc & PhD degrees) (t − 1) 4.32 2.94 −0.07 −0.14 −0.08 0.02 −0.08 −0.48 −0.0721. Rivals’ mass (tfound) 2670 1422 −0.26 0.15 0.19 0.28 0.09 −0.07 −0.3322. Rivals’ mass (t − 1) 3098 1564 −0.22 0.24 0.24 0.32 0.06 0.08 −0.3223. Rivals’ patents issued within 5 years 29.62 18.83−0.21 0.18 0.29 0.20 0.13 −0.11 −0.17

(tfound)24. Rivals’ patents issued within 5 years 41.06 25.29−0.15 0.29 0.29 0.31 0.14 0.19 −0.25

(t − 1)25. Rivals’ patents issued.5 years ago 16.19 13.95 0.02 0.03−0.06 0.05 0.01 0.09 −0.21

(tfound)26. Rivals’ patents issued.5 years ago 15.79 8.03 0.19 0.30 0.30 0.32−0.25 0.13 −0.33

(t − 1)27. U.S. biotechnology firms 1279.2 85.11 0.10 0.04 0.11 0.06−0.33 −0.03 −0.0428. Network efficiency (tfound) 0.08 0.09 0.17 −0.02 0.03 0.11 0.07 0.00 0.0329. Partner BFs’ patents w/in 5 years 0.20 1.57−0.14 0.08 0.14 0.12 0.18 −0.04 −0.08

(tfound)30. Partner BFs’ relative scope (tfound) 0.70 0.29 −0.03 0.00 −0.03 0.04 0.13 0.09 0.0131. Rival biotech co. (tfound) 0.24 0.43 −0.18 0.02 0.10 0.10 −0.13 −0.01 −0.0532. Pharmaceutical co. (tfound) 0.18 0.38 −0.09 0.17 0.21 0.24 0.14 0.04 0.3233. Chemical co. (tfound) 0.07 0.25 0.20 0.03 −0.09 −0.12 0.04 −0.10 0.1734. University (tfound) 0.46 0.50 0.06 −0.07 −0.01 −0.07 0.17 −0.01 0.2035. Research institute (tfound) 0.28 0.45 −0.07 −0.10 0.00 −0.01 −0.04 −0.09 0.1836. Government lab (tfound) 0.11 0.31 −0.06 −0.15 −0.14 −0.06 0.15 0.05 0.2137. Industry assn. (tfound) 0.03 0.16 −0.20 0.08 0.08 0.12 −0.07 −0.05 −0.0438. Marketing co. (tfound) 0.10 0.31 −0.02 0.10 0.10 0.10 0.04 0.08 −0.2639. Log revenues (t) 8.41 6.68 0.49 0.02 0.19 0.19 0.17 0.18−0.0240. Log non-R&D employees (t) 2.20 0.91 0.01 0.56 0.33 0.17 0.11 0.15 0.1341. Log R&D employees (t) 2.01 0.91 0.19 0.29 0.49 0.31 0.18−0.05 0.2242. Log R&D expenses (t) 0.54 0.62 0.18 0.18 0.31 0.63 0.32−0.06 0.0843. Patents granted (t) 0.25 0.96 −0.02 0.13 0.37 0.18 0.53 0.09 0.27



Walker, G., B. Kogut and W. J. Shan (1997). ‘Socialcapital, structural holes and the formation of an indus-try network’, Organization Science, 8, pp. 109–125.

Williamson, O. E. (1991). ‘Comparative economicorganization: The analysis of discrete structural alter-natives’, Administrative Science Quarterly,36, pp.269–296.


(Continued)

8 9 10 11 12 13 14 15 16 17 18 19 20 21

0.30−0.11 −0.04−0.03 −0.08 −0.02

0.10 −0.17 −0.04 −0.070.03 −0.07 −0.01 −0.03 −0.06

−0.04 0.10 0.09 −0.19 0.05 −0.060.14 −0.04 −0.03 −0.07 −0.03 −0.06 −0.020.11 0.15 −0.01 −0.02 0.09 −0.02 0.02 0.110.65 0.20 −0.06 −0.04 0.12 0.01 −0.07 0.14 0.120.38 0.19 −0.07 −0.01 0.02 0.04 0.00 −0.01 0.09 0.430.38 0.03 −0.01 0.02 0.09 0.03 −0.18 0.03 0.01 0.20 0.120.16 0.01 −0.05 −0.03 0.07 −0.07 −0.08 0.08 0.05 0.06 −0.56 0.270.59 0.22 −0.08 −0.20 0.07 0.13 −0.18 −0.04 0.17 0.64 0.44 0.25 0.040.62 0.25 −0.06 −0.17 0.13 0.05 −0.12 −0.02 0.17 0.65 0.44 0.30 0.12 0.850.48 0.24 −0.08 −0.04 −0.07 0.09 −0.17 0.11 0.28 0.55 0.43 0.24 −0.04 0.840.55 0.19 −0.09 −0.03 0.15 −0.01 −0.12 0.03 0.22 0.64 0.54 0.02 −0.03 0.650.39 0.05 −0.04 −0.06 0.00 −0.03 0.01 0.33 0.08 0.56 0.29 0.19 0.06 0.370.47 0.16 −0.02 −0.04 0.21 −0.03 −0.19 −0.03 0.20 0.63 0.58 0.33 −0.11 0.630.08 0.03 0.00 0.01 0.03 0.00 −0.07 −0.18 0.05 −0.03 −0.05 0.22 0.10 0.040.09 0.06 −0.01 −0.09 −0.20 0.07 −0.27 0.23 −0.10 0.03 0.01 0.18 0.02 0.100.10 0.10 0.05 −0.02 0.09 −0.02 −0.02 −0.05 −0.01 0.08 0.02 0.04 0.05 0.01

−0.12 −0.08 0.03 −0.05 0.05 −0.02 0.16 −0.03 −0.01 −0.01 −0.03 −0.07 −0.03 −0.030.03 0.14 0.16 −0.05 −0.06 0.03 0.03 −0.05 −0.07 0.07 −0.04 0.08 0.04 0.180.03 0.13 −0.04 0.16 −0.19 −0.08 −0.20 0.18 −0.06 −0.05 0.02 0.07 0.02 −0.08

−0.31 −0.11 −0.03 −0.05 0.04 −0.04 −0.04 −0.10 −0.03 −0.24 −0.15 −0.07 −0.07 −0.24−0.04 −0.07 0.10 0.20 −0.11 0.01 −0.15 0.09 −0.12 −0.19 −0.08 0.11 −0.01 −0.05−0.20 −0.09 0.15 −0.12 −0.25 0.26 0.01 0.13 −0.08 −0.19 −0.09 0.10 −0.04 −0.14−0.15 −0.15 −0.03 −0.06 −0.14 −0.05 −0.13 0.14 −0.04 0.01 −0.01 −0.10 −0.04 0.07−0.03 −0.07 −0.01 −0.03 −0.06 −0.03 −0.16 −0.06 −0.02 −0.04 0.09 0.06 0.00 0.17

0.07 0.12 −0.03 −0.06 0.10 0.12 0.14 0.18 −0.04 0.20 0.01 −0.07 0.03 0.080.02 0.25 −0.02 −0.23 0.02 0.01 0.12 0.11 0.07 0.14 0.20−0.11 0.12 0.150.21 0.27 −0.26 0.20 0.22 −0.03 0.17 −0.15 −0.01 0.19 0.21 0.09 −0.16 0.140.16 0.28 0.19 0.19 −0.21 0.02 −0.00 −0.12 −0.02 0.15 0.09 −0.07 0.01 0.050.25 0.32 −0.01 −0.04 0.09 0.13 0.16 0.17 0.05 0.19 0.16 0.02−0.17 0.100.04 0.39 −0.02 −0.04 −0.08 0.08 −0.11 0.21 0.27 0.19 0.22 −0.11 0.06 0.13




(Continued)

Variable 22 23 24 25 26 27 28 29 30

Panel B

22. Rival’s mass (t − 1)23. Rivals’ patents issued within 5 years 0.73

(tfound)24. Rivals’ patents issued within 5 years 0.80 0.67

(t − 1)25. Rivals’ patents issued.5 years ago 0.37 0.41 0.41

(tfound)26. Rivals’ patents issued.5 years ago 0.73 0.53 0.80 0.38

(t − 1)27. U.S. biotechnology firms 0.12 0.11 0.20 0.04 0.0028. Network efficiency (tfound) 0.08 0.06 0.04 −0.05 0.10 0.0629. Partner BFs’ patents w/in 5 years 0.03 −0.02 0.12 0.01 0.11 0.02 0.03

(tfound)30. Partner BFs’ relative scope (tfound) −0.02 −0.07 0.02 −0.04 −0.01 −0.21 0.05 0.1131. Rival biotech co. (tfound) 0.13 0.12 0.02 0.00 0.04 0.03 0.38 0.22 0.3032. Pharmaceutical co. (tfound) −0.02 −0.02 0.03 −0.08 −0.02 0.03 0.30 0.09 0.1433. Chemical co. (tfound) −0.26 −0.17 −0.20 −0.13 −0.17 0.02 0.06 −0.03 −0.0234. University (tfound) −0.01 −0.09 −0.14 −0.15 −0.15 −0.07 0.38 −0.11 0.0735. Research institute (tfound) −0.13 −0.12 −0.16 −0.17 −0.16 −0.11 0.36 −0.07 0.1936. Government lab (tfound) 0.03 0.03 −0.03 −0.01 −0.03 −0.19 0.31 −0.04 0.1837. Industry assn. (tfound) 0.15 0.20 0.17 0.07 0.20 0.02 0.15−0.02 −0.0238. Marketing co. (tfound) 0.07 −0.04 0.04 0.01 0.04 −0.19 0.05 −0.04 0.0339. Log revenues (t) 0.13 −0.12 −0.16 0.04 0.15 0.08 0.21 −0.02 −0.0340. Log non-R&D employees (t) 0.10 0.22 0.24 0.02 0.13 0.02 −0.02 0.14 0.0541. Log R&D employees (t) 0.04 0.17 0.21 −0.11 0.17 −0.01 −0.04 0.07 −0.0042. Log R&D expenses (t) 0.10 0.18 0.23 −0.12 0.19 0.03 0.17 0.17 0.1243. Patents granted (t) 0.15 0.07 0.16 −0.12 −0.28 −0.35 0.12 0.22 0.14




(Continued)

31 32 33 34 35 36 37 38 39 40 41 42

0.040.00 −0.130.06 0.31 −0.120.08 0.31 0.21 0.350.13 0.23 −0.09 0.28 0.240.03 0.13 −0.04 0.17 −0.10 0.210.16 −0.16 −0.09 −0.10 −0.09 −0.03 −0.05

−0.18 0.15 0.02 0.19 −0.06 −0.15 −0.12 0.140.00 0.24 −0.00 −0.01 0.11 −0.13 0.10 0.16 0.080.09 0.12 −0.08 0.04 0.14 0.20 −0.01 0.10 −0.03 0.63

−0.12 0.19 −0.07 0.01 0.17 0.15 −0.13 0.13 −0.06 0.41 0.67−0.18 0.15 −0.03 0.15 −0.02 0.18 −0.14 0.04 0.13 0.21 0.12 0.04


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