Research Policy 34 (2005) 101–115

Termination outcomes of research alliances

Jeffrey J. Reuera,∗, Maurizio Zollob,1

a Kenan-Flagler Business School, University of North Carolina, McColl Building, Chapel Hill,NC 27599-3490, USA

b Strategy and Management Department, INSEAD, Boulevard de Constance, 77305 FontainebleauCedex, France

Accepted 24 November 2004Available online 21 January 2005

Abstract

We draw upon evolutionary economics and transaction cost economics to examine how alliance experience accumulation at theparent firm level and alliance features at the transaction level jointly and interactively shape the favorability of research alliances’termination outcomes. Fifteen percent of the terminated alliances we examined were successful, 34% were failures, and 51%experienced an intermediate outcome in the form of contract expiration or unilateral withdrawal by a partner. We find that theeffect of partner-specific experience on the favorability of termination outcomes is greater for non-equity alliances than for equitystructures affording stronger formal governance mechanisms. Other forms of experience such as general alliance experience orprior alliances in the same technological area as the focal agreement have no such favorable consequences for alliance termination.The findings also indicate that alliance complexity adversely influences firms’ termination outcomes in alliances. We thereforefi rminationo©

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nd evidence in partial support of both evolutionary and transaction cost based arguments for the explanation of teutcomes in research alliances.2004 Elsevier B.V. All rights reserved.

eywords:Strategic alliances; Termination; Evolutionary economics; Transaction cost economics; Biotechnology industry

. Introduction

There is growing recognition that alliance instabilitys a central feature of inter-firm collaboration and can

∗ Corresponding author. Tel.: +1 919 962 4514;ax: +1 919 962 4266.

E-mail addresses:reuer@unc.edu (J.J. Reuer),aurizio.zollo@insead.fr (M. Zollo).1 Tel.: +33 1 60 72 44 74; fax: +33 1 60 74 55 00.

be an important determinant of the net benefits fiobtain, or fail to obtain, from partnering (e.g.,Ari no andde la Torre, 1998; Doz and Hamel, 1998). Many empir-ical studies over the last three decades have proevidence that joint ventures and other forms of colorative agreements tend to be short-lived and arherently unstable organizational forms (e.g.,Barkemaet al., 1997; Beamish, 1985; Dussauge et al., 2Franko, 1971; Gomes-Casseres, 1987; Killing, 1

048-7333/$ – see front matter © 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.respol.2004.11.003

102 J.J. Reuer, M. Zollo / Research Policy 34 (2005) 101–115

Kogut, 1989; Li, 1995; Park and Russo, 1996; Pen-nings et al., 1994).

On a theoretical level, however, recent develop-ments in the alliance literature have suggested that in-stability may be a natural, or even desirable, aspect ofcollaboration rather than indicative of failure as mostempirical studies assume (e.g.,Reuer, 2001). Whilemany alliances no doubt come to an end simply due tofailure, alliance termination can also reflect satisfactionof a partner’s strategic objectives. Further, alliance ter-mination can be a consequence of firms’ sequential in-vestments (e.g.,Reuer and Koza, 2000), exploitation ofemerging opportunities (Kogut, 1991; Reuer and Tong,2005), learning from partners (Hamel, 1991; Khannaet al., 1998), or adaptation choices (Koza andLewin, 1998).

This paper intends to contribute to the literature onalliance instability by analyzing the favorability of re-search alliances’ termination outcomes rather than thelongevity of collaborative agreements. Our objectiveis to consider the extent to which terminated alliancesare successful or failed partnerships, or might simplyreflect more neutral outcomes such as contract expira-tion. More importantly, as the literature review belowindicates, many factors from multiple levels of analy-sis have been identified as drivers of alliance instabil-ity, but these factors have typically been addressed in aparticularistic fashion, so the present study attempts tobridge levels of analysis to distinguish more or less suc-cessful alliances that have terminated. Specifically, wed coste mi-n ncee cter-i Wea titu-t eena ver-n par-e ntlya cha ad-d lightst de-tr ncesiL

2. Background literature on alliancetermination

In his pioneering study,Franko (1971)observed thatcertain corporate strategies are more conducive thanothers to the shared control and decision-making re-quired by joint ventures (JVs). Like most of the re-search on the termination of alliances, different types oftermination were bundled together within the broadernotion of instability. His empirical research confirmedthat shifts in strategy, often reflected in changes in orga-nizational structure, precipitated JV instability. Theseearly findings are consistent with the more recent callto view alliances as being embedded in the evolvingstrategies of parent firms rather than as stand-alone,independent operations with their ownraison d’etre(Koza and Lewin, 1998).

More recent research on alliance termination hasseveral broad characteristics. First, while much ofthe literature has continued to focus on interna-tional collaborations and the roles of host coun-try influences and joint venture characteristics (e.g.,Killing, 1983; Beamish, 1985; Harrigan, 1985; Con-tractor, 1990; Franko, 1989; Reynolds, 1979), moreattention has been given to industry conditions thatinfluence alliance instability. For example,Kogut(1989) showed that shifts in partner rivalry dueto changing industry concentration levels increasedthe likelihood of JV dissolution. Related researchfound that alliances between direct competitors werem( thee ex-p us-t ands

tners’c lity.N rel on-v rmswe eenfi dgea ns,M sep inton ess,

raw upon evolutionary economics and transactionconomics to explain the favorability of alliance teration outcomes based on different firm-level alliaxperience trajectories and transaction-level chara

stics of the collaborative agreement, respectively.lso bring together these two perspectives from ins

ional economics by examining the interaction betwfirm’s alliance experience and the alliance’s go

ance structure. By focusing on how factors fromnt firm and alliance levels of analysis independend interactively shape the favorability of high-telliances’ termination outcomes, this paper alsoresses the recent debate between work that high

he primacy of individual transactions’ features inermining alliance efficiency (e.g.,Oxley, 1997) andesearch emphasizing the embeddedness of allian the adaptation practices of parent firms (Koza andewin, 1998).

ore likely to fail (Park and Russo, 1996). Kogut’s1991) treatment of JV partner buyouts asxercise of call options revealed that firmsand through acquisition when the venture’s ind

ry experiences an unexpectedly favorable demhock.

Second, recent research has focused on parapabilities and learning as drivers of instabiakamura et al. (1996)found that alliances are mo

ikely to terminate when partners’ capabilities cerge over time, while alliances between parent fiith diverging capabilities are more durable.Khannat al. (1998)depict alliances as learning races betwrms competing to acquire each others’ knowlend skills. In the domain of high-tech collaboratioitchell and Singh (1992)suggested that firms ure-entry alliances before making commitmentsew technical sub-fields of an industry. Neverthel

J.J. Reuer, M. Zollo / Research Policy 34 (2005) 101–115 103

Hagedoorn and Sadowski (1999)found that rarely dohigh-tech alliances end in acquisitions.

This recent research challenges prior studies in sev-eral respects. First, alliance research has generally op-erated under the assumption that alliance longevity isin parent firms’ interests, and that instability, whetherdefined as different types of termination or other typesof alliance change, is indicative of failure on the part ofthe alliance or the collaborators. However, alliance in-stability may be a natural part of collaboration as parentfirms alter their commitments in uncertain investmentcontexts (Balakrishnan and Koza, 1993; Kogut, 1991),learn from their partners (Hamel, 1991; Khanna et al.,1998), and adapt over time (Koza and Lewin, 1998).Hence, we seek to differentiate terminated alliancesthat were successful from those that were failures ormore neutral for parent firms rather than focus on thedurations of alliances. Second, the outcomes associ-ated with alliance termination are partner-specific, sowe study the favorability of alliance termination out-comes as reported by a focal firm rather than examiningtermination outcomes at the dyad level and assumingequivalent payoffs to participating firms.

Finally, although the preponderance of studies onalliance instability has been conducted on interna-tional joint ventures, the rise of non-equity collabo-rations in high-tech domains suggests a need to ex-amine alliance instability in this investment context(Hagedoorn, 1993, 1995). Such a focus is likely togenerate a sufficient number of terminated alliancest hisa dger tionsa h col-l icalf tionc

3

tem-m cea ly-s f ther r al-l ef-f

we give particular attention to parent firm and alliancelevels of analysis for a set of alliances within a singleindustry. More specifically, we draw on two differentstreams of the institutional economics literature, evo-lutionary economics and transaction cost economics,to analyze the effects of parent firms’ accumulationof alliance experience and alliance attributes on theirtermination outcomes. Evolutionary economics high-lights the role of the tacit accumulation of alliance ca-pabilities through experience, and therefore focuses onthe firm level of analysis. By contrast, transaction costanalysis gives more attention to the particular charac-teristics of individual alliances that give rise to variousexchange hazards, and our focus is on the alliance’scomplexity as shaped by its operational scope as wellas its division of labor. We then combine evolution-ary economics and transaction cost predictions by con-sidering how the development of inter-organizationalroutines through partner-specific experience is more orless attractive for alliances with different formal gov-ernance structures. Although we address the role ofalliance-level explanatory factors that influence the fa-vorability of alliance termination outcomes, we takea focal firm’s perspective in assessing whether the al-liance was a success, failure, or intermediate category(e.g., contract expiration or unilateral withdrawal) asoutcomes may differ across collaborating firms.

3.1. Parent firm experience effects

on-t in-t ofr intop arn-i ts ofl .g.,D lle,1 e ef-f e ofo ;Ll rmsc repli-c n,1 95T tive,p tion

o study in a single industry given the activity in trea as well as the roles of uncertainty and knowleesources in such collaborations. These consideras well as the exchange hazards that attend suc

aborations also align well with the study’s theoretoundations in evolutionary economics and transacost economics.

. Theory and hypotheses

As the literature review suggests, many factors sing from multiple levels of analysis may influenlliance termination, yet any single empirical anais cannot approach being exhaustive. Because oecent debate on whether parent firm factors and/oiance features contribute to alliance efficiency andectiveness (e.g.,Koza and Lewin, 1998; Oxley, 1997),

The prediction that alliance experience will cribute to improved alliance outcomes follows theuitive proposition developed in several streamsesearch that experience accumulation translateserformance improvements. For instance, the le

ng curve literature has demonstrated the benefiearning-by-doing in the manufacturing context (eutton and Thomas, 1984; Epple et al., 1991; Ye979), and the behavioral school has examined th

ects of experience accumulation for a broader rangrganizational activities (e.g.,Cyert and March, 1963evitt and March, 1988; March and Simon, 1958). Evo-

utionary economics has developed theory on how fihange based on the evolution, adaptation, andation of routinized behavior (Cohen and Bacdaya994; Nelson and Winter, 1982; Winter, 1987, 19).his work emphasizes that firms develop a collecrimarily tacit, understanding regarding the execu

104 J.J. Reuer, M. Zollo / Research Policy 34 (2005) 101–115

of an organizational task. This understanding is typi-cally updated without explicit cognitive efforts as theindividuals exposed to repeated events retain a memoryof prior performance outcomes and of possible causalfactors.

Evolutionary economics and related theoretical tra-ditions therefore suggest a positive relationship be-tween alliance experience and the favorability of al-liance termination outcomes. For example, firms lack-ing alliance experience may choose suboptimal part-ners, design a collaborative agreement that does notmatch well with the firms’ objectives, and persist in al-liances that have outlived their purposes. Experiencedfirms, on the other hand, are more likely to mastercomplex alliance processes (e.g.,Doz, 1996; Doz andHamel, 1998), monitor alliance and environmental de-velopments better to take advantage of opportunitiesfor sequential investment (e.g.,Balakrishnan and Koza,1993; Kogut, 1991), and know when a collaborativeagreement is no longer needed due either to the successof the alliance or to a change in the firms’ strategic pri-orities. Based on this logic, the following hypothesiscan be advanced:

Hypothesis 1. The greater a firm’s experience withalliances in general, themore favorable its terminationoutcome will be in the focal alliance.

While this prediction has intuitive appeal, there arein fact several good reasons why firms may not ben-e thea eser them n inp eta -t ex-p ncesa r ex-a ionalf nsid-e e.g.,Ao rentfi lesss Al-l oreh s for

which learning effects are documented. All of thesefactors are obstacles for simple learning by doing totake place, as reflected byHypothesis 1.

It is also important to note that empirical support forHypothesis 1could also be a consequence of firms go-ing back to existing partners that are performing wellrather than alliance capability building per se. This se-lection issue also suggests that there is value in unpack-ing prior alliance experiences to consider more specificalliance experience trajectories. Thus, we intend to in-vestigate alliance experience specific to a technologicalarea and partner-specific experience and then introducethe role of alliance governance in moderating the ef-fects of partner-specific experience.

3.1.1. Technology-specific experienceAlliance experience specific to a technological area

can lead to favorable alliance termination outcomes fortwo reasons in addition to those discussed in develop-ing Hypothesis 1. First, firms’ experiences specific toa given technological area will be less heterogeneousthan experience culled from alliances on any subject.While positive alliance experience effects may still bemitigated by factors such as low frequency and thelack of clear performance metrics, the likelihood ofmisapplying knowledge across dissimilar alliances islessened. Second,Cohen and Levinthal (1990)haveshown that firms engaged in creative efforts develop anabsorptive capacity that is proportional to the amountof discovery in similar domains. Thus, firms with al-l de-v ncet ncet bleb owh y beb eters( bina-t f ap

H ha cala mew

3es-

t eral

fit from the tacit accumulation of experiences inlliance setting, and we believe that highlighting theasons is useful as they might explain some ofixed findings on alliance experience accumulatiorior research (Anand and Khanna, 1998; Barkemal., 1997; Simonin, 1997). In comparison with rela

ively homogeneous and repetitive tasks for whicherience effects have been well-documented, alliare more complex administrative processes. Fomple, prior research has detailed how organizat

orms such as alliances are characterized by corable performance ambiguity and uncertainty (nderson, 1990; Geringer and Hebert, 1991). The lackf clear performance metrics makes it hard for parms to understand which alliances are more oruccessful and discern what works well and why.iance also tend to occur less frequently and are meterogeneous than other organizational activitie

iance experience in a technological area shouldelop greater absorptive capacity, which will enhahe success of new alliances in similar areas. Alliaermination outcomes are likely to be more favoraecause firms will learn more in their ventures, know such ventures serve the firm’s strategy, and maetter able to decide on key alliance design parame.g., contract length, safeguards, resource comions, division of labor, etc.) to meet the needs oarticular technology.

ypothesis 2. The greater a firm’s experience witlliances in the same technological area as the folliance, the more favorable its termination outcoill be in the focal alliance.

.1.2. Partner-specific experienceAlliance experience specific to the partner in qu

ion may provide benefits as in the case of gen

J.J. Reuer, M. Zollo / Research Policy 34 (2005) 101–115 105

collaborative experience and experience specific to atechnological area. However, partner-specific allianceexperience is unique in that it deepens the relation-ship between two firms and the evolution of inter-organizational routines (Zollo et al., 2002). The factthat the two groups of individuals cooperating acrossfirm boundaries develop an improved understanding ofeach other’s behaviors and beliefs tends to help mitigatecoordination, conflict resolution or information gather-ing problems, which in turn facilitates learning and ad-justments (Doz, 1996). In their first alliance with eachother, for example, scientists may encounter inefficien-cies due to either idle time or rushing activities at thetime of handoffs, simply because each group is not fa-miliar with how the other one operates. These problemscan be mitigated in subsequent alliances, and firms mayalso be able to manage troubleshooting or the resolutionof disputes more efficiently and effectively.Dyer andSingh (1998)argue that relationship-specific knowl-edge that accrues from frequent and intense partnerinteractions translates into a relational capability thatcan improve the performance of firms in alliances. Ifthis is true, then the following hypothesis can be ad-vanced:

Hypothesis 3. The greater a firm’s experience withalliances with the same partner, the more favorable itstermination outcome will be in the focal alliance.

3

arye ex-p es oft ear-i eri-e mayi s ona thec opea arep ngk s upt idget ans-a

3.2.1. Alliance scopeIn comparison with mergers or internal organiza-

tional units, alliances tend to have a much narrowermandate. Collaborative agreements are often used foronly one value chain activity and may not even be de-signed to generate profits (e.g.,Harrigan, 1985). Be-cause alliances are not efficient for close coordinationwhen significant interdependencies among differenttasks exist (e.g.,Chesbrough and Teece, 1996), firmsare often advised to keep their collaborations focusedand flexible. While alliances generally do tend to bemore focused than other organizational arrangements,there is also significant heterogeneity across alliancesin the scope of collaborative effort. On the one hand,some collaborations involve limited downstream co-operation in promotion or upstream collaboration inbasic research, while some ventures perform the fullcomplement of value chain activities as independentcompetitors.

Previous research has shown that firms structurehigh tech alliances based on the scope of collabora-tive efforts, but research has not examined the impli-cations of alliance complexity.Pisano (1989), for in-stance, notes that biotechnology alliances encompass-ing multiple projects are more likely to be equity al-liances than non-equity arrangements.Oxley (1997)shows that transactions encompassing a wider range ofproducts or technologies tend to be either equity-based(e.g., joint ventures) or bilateral contractual agree-ments (e.g., cross-licensing agreements or joint re-search projects) than unilateral alliances such as long-term supply agreements or R&D contracts.

Alliances that are broader in scope expose firms togreater contractual hazards. Not only do they involvegreater uncertainty regarding the performance of in-dividual tasks and the coordination of tasks (Oxley,1997), but they also require adjustments that exposeparent firms to risks after the agreement has been imple-mented. Thus, in broad-based collaborations, partnersface challenges in working out their obligations to thealliance as well as their claims on the alliance over time(Borys and Jemison, 1989). Because alliances are gen-erally not well-suited to coordinated adaptations thatare more likely to be necessary when the scope of thealliance is very broad (e.g.,Williamson, 1991), suchcollaborations will be especially difficult to manage.Alliances with a narrower scope, by contrast, are morelikely to end due to a shift in collaborators’ strategic

.2. Alliance complexity

While the previous hypotheses from evolutionconomics focus on firm-level variables such aserience accumulation, we also expect that featur

he particular collaborative agreement will have a bng on the alliance termination outcome of firm expnces. Although different alliance characteristics

nfluence the performance of an alliance, we focurguments from transaction cost theory relating toomplexity of the alliance as indicated by its scnd partners’ allocation of responsibilities, whicharticularly important for high tech alliances involvinowledge resources. A subsequent section takehe alliance’s governance structure in order to brhe arguments from evolutionary economics and trction cost theory.

106 J.J. Reuer, M. Zollo / Research Policy 34 (2005) 101–115

priorities or because the alliance has satisfied the parentfirm’s initial, more limited objectives.

Hypothesis 4. The broader the scope of the alliance,the less favorable the firm’s termination outcome willbe in the alliance.

3.2.2. Division of laborWhile the complexity of an alliance increases with

the scope of the collaboration, even alliances of a givenscope can vary greatly in complexity. Alliance com-plexity depends not only on the number of activitiesperformed in collaboration, but also on how firms allo-cate responsibilities among themselves. For instance,in many collaborative agreements, one party is fullyresponsible for one value chain activity, and the part-ner is responsible for a contiguous value chain activity.Alliances involving a sharp division of labor based onvalue chain contributions have been referred to as Xcoalitions (Porter and Fuller, 1986), sequential ventures(Park and Russo, 1996), and link alliances (Dussaugeet al., 2000; Hennart, 1988). In such alliances, collab-orative efforts focus on coordination across activitiesrather than within activities. In other alliances—termedY coalitions (Porter and Fuller, 1986), integrative ven-tures (Park and Russo, 1996), and scale alliances(Hennart, 1988)—collaborators share responsibilitiesfor one or more alliance activities. Thus, as the divisionof labor in an alliance decreases, collaborative effortsneed to focus on coordination both across and withina nsaW

H ran

3d

gestt ncel s’t eseh aly-s ent,t and

alliance levels of analysis relevant in their own right,but that they also interactively influence the favorabilityof alliance termination outcomes.

Within our empirical setting, at the alliance levelwe focus on firms’ use of an equity or non-equity ar-rangement as this feature of the alliance has receivedsignificant attention in transaction cost research exam-ining alliance design and its antecedents (e.g.,Garcia-Canal, 1996; Gatignon and Anderson, 1988; Osbornand Baughn, 1990; Shan, 1991). Equity alliances aredistinctive relative to non-equity alliances because theformer offers enhanced incentive alignment and jointcontrol through the establishment of equity sharing anda board over the collaborative agreement. In order toconsider the interaction between levels of analysis, wewish to advance a hypothesis using evolutionary eco-nomics reasoning to suggest how partner-specific al-liance experience effects differ across equity and non-equity alliances.

On a theoretical level,Gulati (1995)interprets thepositive influence of partner-specific alliance experi-ence on a firm’s choice of a non-equity alliance overan equity structure based on the formation of inter-organizational trust. If familiarity does breed trust ashe suggests, the protection from opportunistic behav-ior afforded by an equity arrangement to mitigate op-portunism may become redundant and perhaps evencounter-productive to the establishment and implemen-tation of inter-firm cooperation (Doz and Hamel, 1998;Ari no and de la Torre, 1998). The application of evolu-t ex-p ons,a n ofi ngerc ter-v gersa ando r-a butt s tof

za-t st-m be-hc withe a seto ach

ctivities, and there is less clarity on firms’ obligationd rights in the alliance (Borys and Jemison, 1989).e therefore predict:

ypothesis 5. The greater the division of labomong partners, the more favorable the firm’s termi-ation outcome will be in the alliance.

.3. Partner-specific experience and governanceesign

Taken together, the previous five hypotheses sughat factors from both the parent firm and the alliaevels of analysis influence the favorability of firmermination outcomes in alliances. Support for thypotheses would indicate that both levels of anis matter. The final step in our theoretical argumhough, is that not only are factors from parent firm

ionary economics logic, though, offers a differentlanation for the existence of cross-level interactind this explanation does not require the formatio

nter-organizational trust, which depends upon stroonditions than frequent interaction. In fact, in iniews we conducted for a related project, manat HP noted that in their 18 alliances with Ciscover 30 with Microsoft that the quality of their intections improved with successive collaborations,

hey denied that they would expect their partnerorgo opportunities to hold them up.

According to evolutionary economics, organiions evolve through the formation, marginal adjuent, and decay of primarily tacitly understoodavioral patterns (Nelson and Winter, 1982). As indi-ated above, two organizations that have alliedach other in the past should also have developedf tacit understandings on how to collaborate with e

J.J. Reuer, M. Zollo / Research Policy 34 (2005) 101–115 107

other, how to prevent and remedy misunderstandings,and how to coordinate effectively across organizationalboundaries. Over time, and across collaborative ex-periences, they form and develop inter-organizationalroutines, which can facilitate cooperation and enhancethe effectiveness of their alliances. If this is the case,then the formal monitoring, control, and incentive-alignment features of equity structures may be less nec-essary and may even hamper the informal coordinationroutines the partners have established and refined inprior alliances (Zollo et al., 2002). As such, we ex-pect that accumulated partner-specific experience willbe particularly important for non-equity structures thattend to lack such formal governance mechanisms.

Hypothesis 6. The effect of partner-specific experi-enceon the favorability of a firm’s terminationoutcomewill be greater for focal alliances that are non-equityarrangements versus equity arrangements.

4. Methodology

4.1. Sample

We obtained a sample of research alliances by ad-ministering a survey to biotechnology and pharmaceu-tical firms engaged in inter-firm collaboration. We usedthe University ofNorth Carolina’s (UNC’s) (1993)database to identify a relevant target population of col-l otherl tacti ra-t tivea treat-m NCd

ex-p d orm thata dy’sa re-q rsonw ingt etedq to a3 isfac-t try

and the seniority of respondents. 26.9% of the respon-dents are CEOs, 33.7% are business development staff,and 39.5% are alliance managers.

The sample of collaborative agreements we ob-tained is representative of the biotech alliance popu-lation in covering 32.6% of the total number of ob-servable transactions (i.e., 145/445). Although it is im-possible to completely rule out all non-response bi-ases such as the potential underreporting of failures,we were able to compare our sample with the set of ob-servable transactions based on experience levels, andthis provided no evidence of non-response bias. To fur-ther explore the possibility of non-response bias withadditional data, however, we assessed possible differ-ences in all of the variables across early and late re-spondents under the assumption that late respondentsare more similar to non-respondents than early respon-dents are to non-respondents (Armstrong and Overton,1977). Two-samplet-tests andχ2-tests for all of thecontinuous and categorical variables used in this study,respectively, indicated that early and late respondentsdid not differ from one another, providing no evidenceof non-response bias. Fifty-three alliances with com-plete information terminated, and descriptive statisticsfor the sample appear in Section5.

To examine how this final sample of terminated al-liances potentially differs from the remainder, severaladditional tests were conducted. Using data on the de-gree to which a firm was satisfied with the alliancemeeting its objectives on a 1–5 scale, a two-samplet atis-fit -e het-e sat-i eing3 on-g lesi ri-a ghtb hav-i -l ri-a del.T ncet tingr entsa abil-

aborative agreements. The BioScan database andibrary sources were then consulted to obtain connformation for 262 firms engaged in 445 collaboive agreements out of a total of 753 collaboragreements in the human diagnostic, therapeuticent, and equipment sub-fields identified by the Uatabase.

The survey was pre-tested using five industryerts, and a final two-page questionnaire was faxeailed to the CEOs of the targeted firms. A letterccompanied the questionnaire conveyed the stuims, promised a report on principal findings, anduested that the survey be forwarded to the peho is most knowledgeable on the alliance. Follow

wo rounds of telephone calls, 81 firms had compluestionnaires for 145 alliances, corresponding0.9% response rate, which was considered sat

ory given the heavy surveying activity in this indus

-test indicated that firms were generally more sed with alliances that were ongoing (µ = 3.4) thanhose that had terminated (µ = 2.4) (p< 0.001). Howver, consistent with our arguments, there is greatrogeneity in the latter category, with that average

sfaction level of alliances ending as successes b.6. We also compared the sets of terminated andoing alliances using all of the explanatory variab

n this study and found that 9 out of the 10 vables did not differ for the two samples. As mie expected, terminated alliances were seen as

ng lower relevance to the firm vis-a-vis ongoing aliances (p< 0.01). This result also held in a multivate setting using a Cox proportional hazard mohe same interpretations held for the three allia

ermination outcomes described below in compeisk models, which also suggest that the argumnd results presented below apply to the favor

108 J.J. Reuer, M. Zollo / Research Policy 34 (2005) 101–115

ity of alliance termination outcomes rather than theirdurations.

4.2. Measures

4.2.1. Alliance termination outcomeIn constructing the dependent variable, we sought to

examine whether each alliance terminated as a failure,success, or other, more neutral, category. The depen-dent variable was coded as ‘zero’ if the alliance failed,‘one’ if a firm withdrew unilaterally due to a change instrategic priorities or the alliance ended with the natu-ral expiration of the alliance contract, and ‘two’ if thealliance terminated because firms successfully fulfilledtheir objectives and there was no more need to collab-orate. In each case, the favorability of the alliance ter-mination outcome was determined from a focal firm’sperspective. Although it would have been desirable toexamine the favorability of alliance termination out-comes from both parties’ vantage points, it was not pos-sible to obtain sufficient data on terminated alliancesto make such comparisons. We are aware of only onestudy that was able to obtain survey data on both sidesof alliance dyads (Lane and Lubatkin, 1998), and thisstudy was able to assess only 31 dyads in biotechnol-ogy and did not focus on terminated alliances as in thepresent analysis.

To explore the validity of our coding scheme, weperformed several analyses. First, we obtained data onrespondents’ satisfaction with the degree to which al-l per-f rosst de-p ure,t was1 walo tis-f tos llab-ot gory( uc-c ento

ationo xpi-r ilarc p

with the alliances subject to unilateral withdrawal in-dicated it was appropriate to pool together the twotypes of termination outcomes (i.e.,t= 0.95). However,a similar insignificantt-statistic was obtained when thealliances ending upon contract expiration were com-pared with terminated alliances deemed to be successes(i.e.,t= 0.72). In light of these findings, two additionalanalyses were performance. First, the expired allianceswere deleted from the sample, and the ordered logitmodels were re-estimated. Second, these alliances werereclassified as successes when conducting the multi-variate analyses. For both of these sensitivity analyses,we obtained the same interpretations as those presentedbelow.

4.2.2. Explanatory variablesTo measure firms’ various types of alliance experi-

ence, we asked respondents to indicate the number ofprior strategic alliances they had with any partner onany subject (i.e.,collaborative experience), with anypartner on technological subjects similar to the alliancein question (i.e.,technological experience), and withthe focal partner (i.e.,partner-specific experience). Ex-amination of these three count variables’ distributionsindicated the presence of significant positive skewness,which we addressed by redefining these three variablesusing logarithmic transformations.1

A second set of explanatory variables was usedto characterize the alliance’s design and governance.First, a dummy variable classified collaborative agree-mu delt turea xpe-r ut-c sa nces lev-e oft en-t ;

ul-t ct trans-f theu unde-fi aledt

iances met their objectives on a 1–5 scale. Weormed an analysis of variance for this variable ache three termination codes used to construct theendent variable. For alliances ending due to fail

he mean level of satisfaction with the alliances.83. For alliances ending due to unilateral withdrar expiration of the contract, the mean level of sa

action was 2.44. Finally, for alliances ending dueuccess, the mean level of satisfaction with the coration was 3.63 (F= 4.79,p= 0.013). Two-samplet-

ests comparing failures with the intermediate catei.e., t= 1.65) and the intermediate category with sesses (i.e.,t= 1.96) also support the ordinal treatmf the dependent variable.

Second, in order to assess further the classificf a few alliances that were not renewed upon eation of the alliance contract, we performed simomparisons. A two-samplet-test comparing this grou

ents into equity and non-equity categories (i.e.,eq-ity). This variable was incorporated into the mo

o investigate how the alliance’s governance strucffects the relationship between partner-specific eience and the favorability of alliance termination oomes (i.e.,Hypothesis 6). Inclusion of this variable ilso motivated by the fact that the alliance’s governatructure can reflect the firm’s alliance experiencels (Gulati, 1995) and may influence the efficiency

he alliance by providing control rights as well as incive alignment through residual claimancy (Chi, 1994

1 Skewness can inflate the risk of Type I and Type II errors in mivariate models (Tabachnick and Fidell, 1996), and the logarithmiransformation has been shown to remedy this problem. Theormation ‘new variable’ = log(1 + ‘old variable’) was used sincentransformed measures can equal zero and the log of zero isned. Inspection of the transformed variables’ distributions revehat this transformation corrected for skewness.

J.J. Reuer, M. Zollo / Research Policy 34 (2005) 101–115 109

Hennart, 1988). Second, coordination of collaborativeactivities is also achieved through other governancemechanisms, so we included a dummy variable to in-dicate whether or not parent firms put in place a boardor committee overseeing the coordination of allianceactivities (i.e.,coordination committee).

Two explanatory variables were used to capture thealliance’s complexity based on the scope of its activi-ties and how parent firms allocated responsibilities forthese activities. First,alliance scopewas measured asthe number of project activities encompassed by thecollaborative agreements. Six potential project activ-ities were identified: basic research, new product orprocess development, testing and obtaining regulatoryapproval, manufacturing, sales and marketing, and dis-tribution. Thus, alliance scope takes on integer valuesfrom 1 to 6. Second, a variable for parent firms’di-vision of laborwas constructed to measure the allo-cation of responsibilities for the alliance. Respondentsindicated partners’ responsibilities by allocating 100%points between the collaborators across the alliance’svarious project activities. The division of labor in thecollaboration was then measured as follows:

division of labori = 1

ni

ni∑

j=1

|P1ij − P2ij|, (1)

where ni is the number of project activities under-taken by alliancei, P1ij the percentage representingparent 1’s responsibility for taskj, andP2ij the per-c sk( -b o 1.W itiesf umo fore axi-m atert

heme ourcec ,1 in-c nge-m tod erallb tive

size of available resources committed. The variablecan take on values ranging from 1 to 4, which corre-spond to ‘marginal’, ‘normal’, ‘important’, and ‘criti-cal’, respectively. The four categories were anchored byranges of resource commitments relative to the firm’savailable resources: less than 5% for ‘marginal’, be-tween 5% and 10% for ‘normal’, 10–25% for ‘impor-tant’, and more than 25% for ‘critical’ agreements. Sec-ond, we controlled for the type of parent firms involvedin the collaborative agreement.Alliance typeindicateswhether the alliance was between a biotech firm and apharmaceutical firm (i.e., alliance type = 1) or betweenbiotech firms (i.e., alliance type = 0). Above and be-yond the other alliance-level controls, this variable cap-tures differences in partners’ resources and objectivesacross alliances as well as other unobservable initialconditions.

4.3. Model specification

The model specification used to test the hypothesesdeveloped earlier is as follows:

alliance termination outcome

= β0 + β1 collaborative experience

+β2 technological experience

+β3 partner-specific experience

+β4 partner-specific experience

B odelw mul-ti erat-i ther anda

5

cor-r 18

entage indicating parent 2’s responsibility for taji.e.,P1ij +P2ij = 100% for allj). Hence, division of laor is a continuous variable that ranges from 0 then the collaborators equally share responsibil

or each project stage, the variable attains its minimf zero. When one partner is wholly responsibleach project activity, the variable takes on its mum of one. Thus, the larger the variable, the gre

he alliance’s division of labor.Finally, we included two additional controls into t

odel. First, we introduced a control foralliance rel-vancebecause alliances that demand greater resommitments attract managerial attention (Ocasio997) and encourage commitment by supplyingentives to make the best of the collaborative arraent (Williamson, 1983). Respondents were askedescribe the relevance of the alliance for the ovusiness of the parent firm in terms of the rela

+β5 alliance scope+ β6 division of labor

+β7 equity+ β8 coordination committee

+β9 alliance relevance

+β10 alliance type+ ε. (2)

ecause the dependent variable is ordinal, the mas estimated using an ordered logit model. The

iplicative term partner-specific experience× equity isncluded in the model to test the hypothesized modng effect of the alliance’s governance structure onelationship between partner-specific experiencelliance termination outcomes (i.e.,Hypothesis 6).

. Results

Table 1 presents descriptive statistics and aelation matrix for our sample of alliances. Only

110 J.J. Reuer, M. Zollo / Research Policy 34 (2005) 101–115

Table 1Descriptive statistics and correlation matrixa

Variable Mean S.D. (1) (2) (3) (4) (5) (6) (7) (8) (9)

1 Alliance termination outcome 1.81 0.68 –2 Collaborative experience 1.67 1.32−0.08 –3 Technological experience 0.51 0.73 0.04 0.41** –4 Partner-specific experience 0.09 0.25 0.02 0.27* 0.04 –5 Alliance scope 4.92 1.83 −0.17 0.11 0.02 0.11 –6 Division of labor 0.73 0.30 0.22 −0.06 0.03 −0.06 0.43** –7 Equity 0.17 0.38 0.05 0.09 −0.02 0.09 0.24† 0.16 –8 Coordination committee 0.43 0.50 0.19 −0.08 −0.44** −0.08 0.10 −0.02 −0.02 –9 Alliance relevance 1.85 0.97 −0.07 −0.16 −0.17 0.22 0.28* 0.06 0.02 −0.02 –

10 Alliance type 0.55 0.50 0.05 0.03 −0.06 −0.11 0.36* 0.18 0.07 0.27* 0.03

a N= 53.† p< 0.10.∗ p< 0.05.

∗∗ p< 0.01.

(or 34.0%) alliances ended due to failure, while 8 (or15.1%) ended following the achievement of partners’objectives and 27 (50.9%) involved a more neutral ter-mination in as much as the contract expired as plannedor one firm unilaterally withdrew from the collabo-ration due to a change in its strategic interests. Themost common alliance termination outcome, unilat-eral withdrawal, occurred 41.5% of the time. Four al-liances in the original sample ended by acquisition. De-spite arguments that alliances provide firms with calloptions on the partner’s equity position and are oftenstepping stones to acquisitions or other market entriesinto industry subfields (e.g.,Kogut, 1991; Mitchell andSingh, 1992), the frequency with which alliances ter-minated due to partner buyouts was very low for oursample of high-tech collaborations. This finding par-allels Hagedoorn and Sadowski’s (1999)finding thathigh-tech alliances rarely precede partner acquisitions.Given that this alliance termination outcome was veryrare and this outcome does not fit within the ordi-nal classification that is possible for other terminationevents, these four observations were not included in theanalysis presented below.

On average, firms had 12.8 alliances prior to the fo-cal collaboration, and 26% of the firms had no prior ex-perience with alliances. Firms’ average number of prioralliances in the same technological area was 1.4, and11.3% of the firms had prior alliances with the specificpartner. Coordination committees are more likely to beemployed by firms lacking alliance experience in thes s are

less likely to use them (p< 0.01). 11.3% of the alliancesinvolved a single activity, three-quarters of which wereresearch-based, yet roughly two-thirds of the collabora-tive agreements comprised all six project activities. Forcomplex alliances that are broad in scope, parent firmsclarify partner responsibilities through a sharper divi-sion of labor (p< 0.001) and also tend to institute con-trols and coordination through the use of equity-basedgovernance (p< 0.10). Seventeen percent of the al-liances involved equity, and the average alliance scopewas 4.73 activities for non-equity alliances and 5.89activities for equity alliances (p< 0.001), whereas thedivision of labor for equity and non-equity allianceswere statistically equivalent. Fifty-five percent of thecollaborations were between biotech and pharmaceu-tical firms, and the remainder were between biotechfirms.

Table 2presents the results from the multivariateanalysis. Model 1 provides estimates for a specifica-tion excluding the interaction term for partner-specificexperience and governance design (i.e., equity versusnon-equity), and model 2 represents the full model.Model 1 is significant at the 0.10 level, and model 2provides greater explanatory power (p< 0.01). For bothmodels, a score test indicated that the parallel lines as-sumption is valid (i.e.,χ2 = 11.98, 9 d.f.;χ2 = 11.17,10 d.f.).2

2 ourm r all

ame technological area, whereas experienced firm

To investigate whether multicollinearity posed a problem forodels, we investigated the variance inflation factors (VIFs) fo

J.J. Reuer, M. Zollo / Research Policy 34 (2005) 101–115 111

Table 2Ordered logit estimation resultsa

Independentvariable

(1) (2)

Collaborativeexperience

−0.13 (0.25) −0.16 (0.26)

Technologicalexperience

0.59 (0.48) 0.65 (0.50)

Partner-specificexperience

−0.50 (1.45) 5.03* (2.54)

Partner-specificexperience×equity

– −8.42** (3.24)

Alliance scope −0.58** (0.22) −0.65** (0.23)Division of

labor3.25** (1.19) 3.34** (1.26)

Equity 0.50 (0.89) 1.70 (1.04)Coordination

committee1.73* (0.73) 2.33** (0.81)

Alliancerelevance

0.08 (0.33) 0.14 (0.34)

Alliancetype

0.26 (0.64) −0.05 (0.67)

χ2 16.48† 23.61**

Log likelihoodL(β)

−42.77 −39.21

−2[L(β1)−L(β2)]

– 7.12**

a N= 53. Standard errors appear in parentheses.† p< 0.10.∗ p< 0.05.

∗∗ p< 0.01.

Contrary to the hypotheses that general collabora-tive experience and technological experience improvealliance termination outcomes (i.e.,Hypotheses 1 and2), neither variable is significant. Thus, we find thatfirms do not generally benefit from broad-based col-laborative experience or alliance experience in similartechnological domains. In order to examine whetherpositive experience effects only occur after a thresh-old level of experience is reached (e.g.,Haleblian andFinkelstein, 1998), we also re-estimated the model us-ing the raw general experience variable and its squared

of the specifications we estimated. The maximum VIF for the vari-ables in these models was 4.1, which is below the rule-of-thumbcutoff value often for multiple regression models (Neter et al., 1985,p. 392).

term. The linear term was negative and the squared termpositive, and both were significant at the 0.05 level.Interpreting the point estimates by taking the partialderivative of the equation with respect to general al-liance experience indicated that the effect of such ex-perience only turns positive after 72 alliances, whichrepresents roughly 4% of the firms. Thus, there is noevidence for experiential learning in general or withinthe confines of the technological domain of the focalalliance.

The results do indicate that the effects of partner-specific experience differ across equity and non-equityalliances. While the partner-specific experience vari-able is insignificant in model 1, the overall effect ofpartner-specific experience is indicated by model 2.This model has significantly greater explanatory powerthan a reduced model that excludes the partner-specificexperience variable’s main effect and the interactionterm (i.e.,χ2 = 7.24, 2 d.f.,p< 0.05). Taking the partialderivative of model 2 with respect to partner-specificexperience yields 5.03–8.42× equity. Thus, in sup-port of Hypothesis 6, partner-specific experience hasa positive impact on the favorability of alliance ter-mination outcomes for non-equity collaborations (i.e.,equity = 0), and such experience is less valuable forequity structures affording incentive and monitoringrights that facilitate coordination (p< 0.001).3

The ordered logit results also indicate that the com-plexity of alliances influences their termination out-comes. Consistent withHypothesis 4, firms tend tof al-l ew cet thesF ightc lab-

xperi-e mit-t

iancep ation-s f la-b thesems ciento asi

are better in more focused collaborations than iniances that are broad in scope (p< 0.01). In accordancith Hypothesis 5, the results also indicate that allian

ermination outcomes tend to be more favorableharper is the alliance’s division of labor (p< 0.01).4

inally, alliances coordinated by a board or oversommittee are likely to end more favorably than col

3 We also tested the interaction between partner-specific ence and whether parent firms’ put in place a coordination com

ee, but the interaction effect was not significant.4 If experienced firms are better able to manage complex allrojects, partner-specific experience might moderate the relhip between alliance complexity (e.g., scope and division oor) and alliance termination outcomes. When we introducedultiplicative terms, the coefficient on the alliance scope× partner-

pecific experience interaction was insignificant, and the coeffin the division of labor× partner-specific experience interaction w

nsignificant, however.

112 J.J. Reuer, M. Zollo / Research Policy 34 (2005) 101–115

orative agreements lacking this governance mechanism(p< 0.01).

6. Discussion

The results of the analyses offer implications for sev-eral streams of theoretical work and future alliance re-search. One striking piece of evidence uncovered fromthe data is that the majority of alliances in the sam-ple do not end because of failure. This finding is inclear contrast with the commonly held assumption inprior studies that termination reflects failure and al-liance longevity is an indicator of success. The nor-mative implications drawn from survival analyses ofalliances can therefore be called into question, and fu-ture work would benefit from studying the performanceimplications of alliances more directly. Our finding thatalliances infrequently terminate due to acquisition sug-gests that firms are selective in bringing alliances to anend in this fashion, as predicted by real options theory(Kogut, 1991). The fact that most alliances terminatedue to changes in parent firms’ strategic priorities is inaccordance with the view that alliances should be seenas being embedded in parent firms’ adaptation choicesover time (Koza and Lewin, 1998).

Another important implication that can be drawnfrom the results of the analysis presented above is thatboth alliance and parent firm levels of analysis matterin shaping alliance termination outcomes. For instance,p ncei rr n ofl Re-c s thesm torss nces( rT al at-t

ac-t in-fl esefi ch ar-g ticsi ee-m to bev lab-

orative agreements are embedded in the parent firms’strategies. Our results situate strategic alliances withinthe history of firms’ adaptation practices and point tothe need for further work to consider multiple levels ofanalysis for specifying models of alliance performance.

Our results also demonstrate the importance of dis-aggregating alliance experience trajectories in futureresearch. We find that partner-specific alliance experi-ence has a significant impact on the quality of termi-nation outcomes, particularly for non-equity alliances.Evolutionary economics suggests that the interactionbetween partner-specific experience and governancedesign can be interpreted to indicate that firms thathave developed an alliance history together and a corre-sponding set of partner-specific collaborative routineshave less need for equity structures to align incentives,provide monitoring rights, and institute formal controlsover the relationship. By contrast, firms that lack suchroutines will find equity structures helpful in facilitat-ing coordination.

However, we do not find evidence that general al-liance accumulation or alliance experience in the tech-nology domain of the focal agreement are beneficial tocollaborators. There are several potential explanationsfor these results. First, alliances occur with lower fre-quency in comparison with manufacturing processesand other organizational activities for which positiveexperience effects have been documented. Thus, orga-nizations are apt to experience decay in their abilitiesto recall past events, either due to personnel turnovero ory.S pro-d ativet ll bed ssd opri-a ousc tivep y tob f de-c andi thep

cur-r ncet re an turew ur

rior studies suggest that the complexity of an allianfluences its efficiency (e.g.,Pisano, 1989), and ouesults reveal that an alliance’s scope and divisioabor do influence firms’ termination outcomes.ent research has suggested that variables such aight be important decision variables for collabora

eeking to manage exchange hazards in R&D alliaOxley and Sampson, 2004), which challenges prioCE treatments of them as exogenous transaction

ributes driving firms’ governance decisions.More importantly, the results show that the inter

ion between factors from the two levels of analysisuence firms’ termination outcomes in alliances. Thndings are relevant to the debate between researuing for the primacy of transactional characteris

n determining the efficiency of collaborative agrents and research suggesting that alliances need

iewed from a parent firm’s perspective since col

e

r because of natural limitations to human memecond, alliances are more heterogeneous thanuction processes or other standardized administr

asks. Heterogeneity in alliances means that it wiifficult for individuals to see commonalities acroiverse experiences and transfer knowledge apprtely. Third, alliances are more causally ambiguompared to typical manufacturing or administrarocedures. Thus, organizational learning is likele impeded by the number and interdependence oisions and managerial actions required to designmplement a collaborative agreement as well asaucity of performance metrics.

As this is the first paper that explores the ocence and determinants of different types of alliaermination based on their performance, there aumber of opportunities to extend this analysis. Fuork will need to explore the generalizability of o

J.J. Reuer, M. Zollo / Research Policy 34 (2005) 101–115 113

findings on the distribution of alliance termination out-comes in other industry contexts, for alternative typesof alliances, and in different geographic markets. Addi-tional work is needed to further investigate the bound-ary conditions of alliance experience effects. For exam-ple, future research might specify alliance experiencetrajectories in other ways or explore other contingen-cies that bear upon firms’ abilities to benefit from prioralliances of different types. Furthermore, while our ar-guments and results underscore the relevance of evo-lutionary economics and transaction cost theory, thereare opportunities to apply different theoretical perspec-tives (e.g., organizational sociology, institutional the-ory, etc.) as well as to investigate the embeddednessof alliances in other levels of analysis (e.g., in industrynetworks, host countries, etc.).

Finally, our research examined how firms might de-velop knowledge about managing alliances throughlearning-by-doing processes, and we have not consid-ered other mechanisms for the creation and evolution ofalliance capabilities (Kale et al., 2002). These includeexplicit steps for articulating, codifying, and diffusingknowledge such as developing databases for dissemi-nating lessons learned and best practices, conductingalliance post-mortems, implementing structural solu-tions such as corporate alliance groups, and institutinginternal training programs (e.g.,Harbison and Pekar,1997). Research in directions such as these could sig-nificantly also expand our understanding of how firmscan enhance their capabilities in managing alliances.

A

vedf n,M rch.F nterf

R

A The315.

A ven-

A anSci-

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