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COMPETITION WITHIN AND BETWEEN NETWORKS: THECONTINGENT EFFECT OF COMPETITIVE EMBEDDEDNESS ON

ALLIANCE FORMATION

JAVIER GIMENOINSEAD

I examine how firms use alliances to respond to the alliance networks of their rivals,by either allying with their rivals’ partners or by building countervailing alliances.Evidence from the global airline industry (1994–98) suggests that these strategic re-sponses depend on alliance cospecialization. Cospecialized alliances by rivals mayinvolve exclusivity, precluding alliances with the rivals’ partners and thus encourag-ing countervailing alliances. Nonspecialized alliances are less exclusive and are usedwhen rivals share the same partners.

Over the last decade, the network metaphor hasbecome influential in research into strategic alli-ances and interorganizational relationships (Gulati,1998), along with more established perspectives,such as the transaction cost and capability views(Hennart, 1988; Richardson, 1972). Alliance net-works may provide members such benefits as ac-cess to capabilities and information from direct andindirect partners, referrals to other potential part-ners and opportunities, brokerage opportunitieswith other relations, and an effective network gov-ernance context for individual alliances (Burt,1992; Coleman, 1990; Dyer & Singh, 1998; Jones,Hesterly, & Borgatti, 1997). Yet, if—as is suggestedin the literature—firms obtain competitive advan-tages from their alliances and network membership,it follows that rivals may be negatively affected bysuch alliances (Silverman & Baum, 2002). As a result,they would be motivated to respond in ways thatmatch and neutralize their opponents’ advantages.

This article examines how competitive dynamicsshape alliance formation, partner selection, andnetwork evolution. Prior research on this questionhas examined the likelihood of formation and dis-solution of alliances among rivals (Garcia-Pont &Nohria, 2002; Kogut, 1989; Park & Russo, 1996;Pfeffer & Nowak, 1976). Evidence shows that alli-

ances between rivals are often hazardous, sincecompetitive goals encourage opportunistic behav-ior and appropriation of capabilities (Hamel, 1991;Kogut, 1989; Park & Russo, 1996; Park & Ungson,2001). However, beyond direct rivalry between po-tential partners, competitive relations with thirdparties may also influence dyadic alliance forma-tion. For example, a firm may develop allianceswith its rivals’ partners in an attempt to match thenetwork benefits of its rivals. Alternatively, it mayseek out countervailing alliances with similar part-ners as a way of duplicating the rivals’ benefits.

This study contributes a theoretical model andempirical examination of how competitive rela-tions with third parties influence alliance forma-tion and partner selection. In particular, factorssuch as whether a firm’s rivals are already alliedwith a potential partner, or whether a potentialpartner faces competitive pressures from the samerivals or rival alliances, influence alliance and part-nership choices. The notion that actors are embed-ded in networks of relations with third parties hasbeen at the core of network-oriented social sciencefor decades (Burt, 1992; Coleman, 1990; Granovet-ter, 1973; Heider, 1946; Simmel & Wolff, 1950), andthis idea has been widely applied in alliance re-search. The present work departs from earlier re-search, however, by combining the two networks ofcompetitive and cooperative relations to describeindirect, third-party competitive influences on al-liance formation and partner selection.

To observe these indirect competitive effects,consider the following situations: After witnessingseveral important transatlantic alliances, such asthose made by KLM with Northwest, by AmericanAirlines with British Airways, and by Lufthansawith United Airlines, in 1999 Air France an-nounced that it had selected Delta rather than Con-

I thank Eui Jeong for research assistance and Tai Kimfor data collection assistance. I also thank Erin Anderson,Africa Arino, Tina Dacin, Yves Doz, Clara Garcia, Es-teban Garcia Canal, Martin Gargiulo, Bruce Kogut, DavidKrackhardt, Rich Makadok, Hemant Merchant, and SubiRangan for their comments on prior versions of this pa-per. Thanks also to Mr. Attilio Costagusta, chief of theStatistics and Economic Analysis Section of ICAO, andMr. Richard Whitaker, former editor of Airline Business,for advice on data-related issues.

� Academy of Management Journal2004, Vol. 47, No. 6, 820–842.

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tinental to be its major alliance partner for transat-lantic travel. Although Air France had a “code-sharing” agreement (an agreement to jointly operateroutes) with Continental in a few markets, AirFrance’s choice appeared to be influenced by Con-tinental’s alliance plans with KLM, Air France’srival. Four days later, Delta’s European partnersSwissair and Sabena announced that they wouldswitch to an association with Delta’s rival, Ameri-can Airlines. These decisions appeared to be moti-vated by a desire to both avoid sharing alliancepartners with rivals, and to counterbalance rivals’alliances. In contrast, CSA Czech Airlines simulta-neously maintained alliances with Air France,Lufthansa, KLM, Iberia, and Austrian Airlinesthroughout most of the 1990s. Similarly, BritishMidland maintained code-sharing agreements withboth American and United for seven years, until2000. In these cases, rivals managed to share thesame partner for years without apparent structuralconflict. These contrasting situations are notunique to the airline industry. In some industries,such as automobiles and mainframes, firms tend topolarize into competing alliance constellations, inwhich direct rivals select different partners (Axel-rod, Mitchell, Thomas, Bennett, & Bruderer, 1995;Gomes-Casseres, 1996; Nohria & Garcia-Pont,1991). In other industries, such as biotechnology orinvestment banking, however, direct rivals mayshare the same partners (Baum, Shipilov, & Row-ley, 2003; Silverman & Baum, 2002; Stuart, 1998).

This study explores how dyadic alliance forma-tion between potential partners is influenced by thecompetitive embeddedness of the dyad, repre-sented by the competitive relations of the potentialpartners with third parties. In particular, I focus onhow a firm’s selection of partners is influenced byits rivals’ choice of partners. Because strategic alli-ances provide access to the resources and informa-tion of direct partners and their networks, theyprovide a competitive advantage to participatingfirms . . . and a competitive disadvantage to theirrivals. Generally, firms can respond to their rivals’alliances in at least two ways: (1) by linking intotheir rivals’ networks, thus trying to obtain thesame network benefits from the same partners(leading to intranetwork competition) and (2) bydeveloping countervailing alliances that providesimilar benefits from different (but substitute) part-ners (leading to internetwork competition).

When do firms favor allying with their rivals’partners, and when do they favor forming counter-vailing alliances? I argue that there is no universaltendency for either intra- or internetwork competi-tion to predominate in alliance networks. Instead,drawing on transaction cost and social exchange

theories, I claim that the relative predominance ofone logic of network competition over the otherdepends on the level of alliance cospecialization ina network. Alliance cospecialization demandsgreater relational exclusivity. As a result, it reducesintranetwork competition (alliances with rivals’partners) and increases internetwork competition(countervailing alliances against the rivals andtheir partners). Thus, according to the theory, alli-ance cospecialization is a critical contingency thatdetermines the direction of alliance formation andthe competitive evolution of an alliance network. Itested hypotheses in the context of the alliances inthe global airline industry from 1994 to 1998.

THE COMPETITIVE EMBEDDEDNESS OFALLIANCE FORMATION

Since theory integrates competitive consider-ations in alliance network evolution, I first definethe two basic constituent relations: competitive re-lations and alliance relations. It is nowadays com-mon to view alliances as elements of an alliancenetwork, and therefore to theorize about networkinfluences beyond those of direct relations, such asindirect ties (partner’s partners), centrality, status,autonomy, and so forth. This network perspectivehas not yet been adopted for competitive relations,thus hindering the development of theory aboutindirect competitive relations (such as rivals’ ri-vals, or rivals’ partners). By adopting a networkperspective on competitive relations, it is possibleto theoretically and empirically integrate the anal-ysis of competitive and cooperative relations.

Competitive Relations and Niche Overlap

I define competitive relations in terms of nicheoverlap; that is, competitive relations exist whenfirms seek out the same limited resources or targetthe same markets or customers (McPherson, 1983).High niche overlap indicates that two firms aresubstitutes for one another in markets and, there-fore, that their outcomes are competitively interde-pendent. Advantages obtained by firms with highniche overlap (that is, competitive advantages) areimportant for firm-level profitability: Without col-lusive coordination, a firm’s profit decreases whenits competitors are more effective, and it increaseswhen its competitors are less effective (Salop &Scheffman, 1983). A firm may therefore attempt toexclude its competitors from sources of advantage,such as access to its partners (Krattenmaker &Salop, 1986).

Because firms differ in their strategic positionswithin an industry, they also vary in their degrees

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of niche overlap with other incumbents. Tradition-ally, strategy researchers have used the concept ofstrategic groups to cluster firms into discretegroups with high strategic similarity and nicheoverlap (Caves & Porter, 1977; Cool & Schendel,1987; Hatten & Hatten, 1987). However, the ideathat firms fit into discrete strategic groups has beenseriously challenged (Barney & Hoskisson, 1990;Reger & Huff, 1993). In recent years, competitivestrategy research has turned toward dyadic repre-sentations of relative competitive positions thatrecognize firm-specific niches (Chen, 1996; Stuart,1998). In this study, I assumed that firms occupyfirm-specific niches and, therefore, that niche over-lap is a dyadic relationship between firms. Takentogether, these dyadic competitive relations form acompetitive network in which firms are embedded.The structure of that competitive network may in-fluence behavior beyond the effect of dyadic nicheoverlap. For example, firms that are indirectly con-nected (that is, firms that share common rivals)may be interdependent, even if they don’t havedyadic niche overlap.

Defining competitive relations in terms of nicheoverlap makes it necessary to draw a conceptualdistinction between niche overlap and the relatedconcept of complementarity, which has been oper-ationally defined in prior alliance research in termsof lack of niche overlap. Complementarity, an im-portant antecedent of dyadic alliance formation(Chung, Singh, & Lee, 2000; Gulati, 1995), existswhen it is possible for two firms to combine theirscopes, resources, or capabilities to jointly pursuenew strategic opportunities that they could not pur-sue independently in an effective way (Dyer &Singh, 1998; Richardson, 1972). Researchers haveassessed complementarity by questioning whetherfirms occupy different niches or possess differentcapabilities (Chung et al., 2000; Gulati, 1995;Nohria & Garcia-Pont, 1991). Implicitly, they haveassumed that niche overlap and complementarityare diametrically opposite concepts, whereby theabsence of niche overlap necessarily entails thepresence of complementarity. Such an assumptionis problematic, since it ignores the likely case ofdissimilar firms that are not complementary. Notall combinations of dissimilar scopes or capabili-ties lead to value creation; different capabilitiesheld by dissimilar firms may be mutually incom-patible. Low niche overlap may be a necessary con-dition for complementarity, but it is not a sufficientcondition: firms may be dissimilar without beingcomplementary. This conceptual distinction is im-portant for untangling, theoretically and empiri-cally, the effects of complementarity from those ofcompetitive relations.

Alliance Relations

Horizontal alliances represent voluntary inter-firm agreements involving the exchange, sharing,or codevelopment of products, technologies, or ser-vices among firms engaged at the same stage in thevalue chain. In contrast to market contracts, alli-ances involve incomplete contracts that do notfully specify the conditions of exchange. Accord-ingly, alliances allow more flexible and adaptiveinterfirm exchanges, but their success depends oneffective governance of an ongoing relationshipamong parties with possibly divergent interests.Given that alliance partners have an ex post “in-alienable de facto right to pursue their own inter-ests at the expense of others” (Buckley & Casson,1988: 34), the design of self-enforcing governancemechanisms is critical (Dyer & Singh, 1998).

Alliances differ on the intensity or strength ofinterorganizational dependence and, more particu-larly, on their levels of relation-specific investment(cospecialization) and sensitive knowledge sharing(Contractor & Lorange, 1988; Doz & Hamel, 1998).To reflect these differences in alliance relations, Idistinguish between cospecialized and nonspecial-ized alliances. Cospecialized alliances, which in-volve investments in partner-specific assets andactivities and sharing of sensitive or proprietaryknowledge, can create value by exploiting efficien-cies of mutual specialization. However, they alsoput firms at risk of “hold-up” and “leakage” (Klein,Crawford, & Alchian, 1978; Williamson, 1983) andare difficult and costly to reverse. Given the rela-tional risk cospecialized alliances entail, the gover-nance of these alliances tends to involve contrac-tual safeguards, frequent and joint decisionmaking, equity control, mutual adaptation, and in-terorganizational commitment and trust (Dyer &Singh, 1998; Jones et al., 1997; Uzzi, 1997). Alli-ance cospecialization may occur among both hori-zontal and vertical alliances, and among both“scale” and “link” alliances (defined respectivelyas alliances in which partners contribute similarresources and those in which they contribute dif-ferent resources). Thus, alliance cospecialization isan indicator of the intensity and irreversibility ofpartner dependence, rather than a description ofthe nature of alliance activities.

In contrast, nonspecialized alliances—those withless cospecialization or proprietary knowledgesharing— entail less mutual dependence and lowerrisk of leakage. They are easier for a partner toreverse without incurring high exit costs. Becauseof the lower dependence and greater reversibility,firms may use actual or potential competitionamong its partners in a nonspecialized alliance to

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maintain effective relations, and firms can easilyterminate ineffective relations (Uzzi, 1997). Thelower exit costs of nonspecialized alliance alsomake them safe instruments for exploring uncer-tain opportunities and unproven partners (Ring &Van de Ven, 1994; Rowley, Behrens, & Krackhardt,2000).

Competitive Influences on Alliance Formation

Interest in the effect of competition on allianceformation is well established. An important themewithin this literature is whether firms can indeed“collaborate with competitors and win” (Hamel,Doz, & Prahalad, 1989). However, empirical evi-dence on the effectiveness of alliances between di-rect competitors has generally been negative.Bleeke and Ernst (1992) found a rate of alliancesuccess of 62 percent when firms had minimalgeographic overlap, versus 25 percent when firmshad moderate or high overlap. Kogut (1989) foundthat market share instability (a proxy for rivalry)increased alliance dissolution, while Park andRusso (1996) found that alliances among directcompetitors (firms from the same four-digit SICcode) were more likely to fail. Two general reasonsseem to underpin these cooperation difficulties.First, the alliances of direct competitors may lackgoal alignment, given the strong incentives to be-have opportunistically and gain a benefit in marketcompetition (Park & Ungson, 2001). For direct ri-vals, benefits gained from an alliance that theyshare equally will not result in competitive asym-metry between them. Rivals will, therefore, havestrong incentives to draw private benefits beyondthe common benefits of the alliance (Khanna, Gu-lati, & Nohria, 1998). Second, direct competitorsmay face a risk of uncontrolled information disclo-sure that would allow competitors to appropriatecapabilities and disband alliances (Bresser, 1988;Hamel, 1991).

Aside from the effects of direct competition be-tween potential partners, the indirect competitiveeffects of third parties may influence alliance for-mation. These indirect competitive effects are im-portant because alliances are potential sources ofcompetitive advantage for rivals. Thus, a firm maybe negatively affected by its rivals’ alliances andnetworks (Silverman & Baum, 2002). Although afirm may have multiple potential partners offeringdiverse opportunities for value creation, competi-tive threats focus attention on particular opportu-nities that rivals are exploiting (Greve, 1998). Thus,alliances by rivals could lead a firm to realize thatrivals are exploiting some particular potential

value creation opportunities and are thus under-mining the firm’s competitive standing.

A firm may respond in at least two ways. First, itmay seek entry into its rivals’ networks, thus cre-ating alliances with its rivals’ partners. This re-sponse would create intranetwork competition,since the firm and its rivals would become substi-tute partners within the network. Intranetworkcompetition undermines the rivals’ unique advan-tage and power to appropriate rents from the rela-tionships, and such competition will therefore re-duce the firm’s competitive disadvantage.Alternatively, the firm may seek to match the ri-vals’ advantage by creating countervailing allianceswith other partners who also face the same compet-itive threat. This response creates internetworkcompetition, since the networks themselves willcompete with each other for customers. In this case,the firm and its rivals maintain their bargainingpower to appropriate rents within their respectivenetworks, but the rents generated by the networkswill decrease. Figure 1 illustrates three types ofcompetitive embeddedness. In the figure, firms iand j are rivals, and firm j has an alliance with firm1. Firms i and 1 have a “rival’s partner” indirectconnection. Formation of an alliance in this contextwould lead to intranetwork competition. Firms iand 2 have a “rival alliance” indirect connection,since both face a common threat from partners of arival alliance. Firms i and 3 have a “common rival”indirect connection, since both firms face the samecommon threat from rival j. Countervailing alli-ances by firm i with firms 2 or 3 would lead tointernetwork competition, since they polarizerather than connect the alliance network. The fol-lowing sections examine predictions for thesetypes of competitive embeddedness in detail.

Alliances with Rivals’ Partners: Inclusivenessversus Exclusivity of Rivals’ Networks

When are firms likely to favor or avoid allianceswith their rivals’ partners? Transaction cost eco-nomics and social exchange theory offer predic-tions about the emergence of exclusivity in ex-change relations. For clarity, I will label thepotential partners as firms i and 1 and assume thatfirm i competes with a rival j that is currently alliedwith firm 1 (as described in Figure 1).

The formation of an alliance between a firm i andits rival’s partner 1 would undermine the advan-tage and bargaining power of rival j and improvethe power of partner 1. On the one hand, rival jwould strive to keep its alliance exclusive in orderto maintain its differential advantage relative to itsrival (Krattenmaker & Salop, 1986; Salop & Scheff-

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man, 1983). Assuming that the partner has capabil-ities or market access that make it an attractivepartner for firm i, the formation of that alliancewould erode rival j’s competitive advantage. More-over, by increasing the bargaining power of partner1, rival j would be able to capture less value fromthe relationship (Emerson, 1962; Pfeffer & Salancik,1978). Singh and Mitchell (1996) found supportingevidence that hospital software firms’ failure haz-ard increased after the firms’ partners entered alli-ances with the firms’ rivals.

On the other hand, an alliance between firms iand 1 would benefit partner 1 by allowing it toexploit its resources with other partners and byimproving its bargaining position relative to bothfirms i and j. In the absence of special inducementsfrom rival j to keep partner 1 exclusive, partner 1would likely favor the development of an alliancewith firm i. To keep partner 1 exclusive, rival jwould have to provide its partner 1 with an “exclu-sivity premium” sufficient to compensate it for theopportunity costs of both missed opportunitieswith other partners and reduced bargaining power(Krattenmaker & Salop, 1986). Yet the provision ofthis exclusivity premium would reduce the netbenefit that rival j could obtain from exclusivity. Itis possible that initial asymmetries in bargainingpower, arising from resources or market position,may allow some firms to obtain exclusivity fromtheir partners without sufficient compensation formissed opportunities. If partners have equally valu-able capabilities, market positions, or network con-tacts, however, generally it will be difficult forfirms to prevent partners from also allying with

rivals. Because of the cost of exclusivity, it is un-likely that there will be a general predispositiontoward exclusivity in alliance networks.

Exclusivity may be more likely when it facilitatesincentive alignment and efficient governance of re-lations. Transaction cost and social exchange theo-ries suggest that exclusivity may be demanded andobtained as a safeguard governance mechanism inalliance relations with high cospecialization(Anderson & Weitz, 1992; Cook & Emerson, 1978;Fein & Anderson, 1997; Klein, 1980; Williamson,1983). To be accepted voluntarily by partners, ex-clusivity should improve the efficient allocation ofresources, not simply shift bargaining power acrosspartners. Transaction cost economists have arguedthat exclusivity may be an economically efficientmechanism to make relational contracts more self-enforcing against the lure of opportunism. Exclu-sivity increases the cost of ending a relationship byincreasing the cost of switching to alternatives out-side the relationship. It therefore serves as a credi-ble commitment, or “hostage,” that can be used tosafeguard transactions with high degrees of cospe-cialized asset investments (Klein, 1980; William-son, 1983) and those whose parties may have anincentive to free ride on other parties’ investments(Heide, Dutta, & Bergen, 1998). Exclusivity can actas a pledge of relational commitment that sustainscooperation (Anderson & Weitz, 1992; Cook & Em-erson, 1978; Jones et al., 1997). Evidence in themarketing channels literature shows that the degreeof exclusivity granted to a supplier or distributor isrelated to the transaction-specific investments

FIGURE 1Illustration of Competitive Embeddedness Typesa

a Closer distance implies greater niche overlap.


made by the supplier or distributor (Fein & Ander-son, 1997).

In the context of alliance formation, this viewimplies that cospecialized alliances will be morelikely to develop voluntary norms of competitiveexclusion as a governance mechanism, both as ahostage against possible costly defections and as abarrier against rivals’ potential free riding on therelational investment, proprietary knowledge, orcapabilities (Jones et al., 1997). Nonspecialized al-liances will not require such a high level of rela-tional commitment, and therefore exclusion wouldnot be expected.

I examine two testable implications of the previ-ous logic. First, the level of cospecialization in thepreexisting alliances between rivals and their part-ners should negatively influence the likelihood offirms making new alliances with their rivals’ part-ners. Thus, a firm would be more likely to be ex-cluded from its rivals’ cospecialized partners thanfrom its rivals’ nonspecialized partners.

Hypothesis 1. The likelihood of alliance forma-tion between a firm and its rivals’ partners islower when the rivals’ alliances are cospecial-ized rather than nonspecialized.

Second, the presence of preexisting alliances be-tween a firm’s rivals and its potential partnershould affect the type of alliances (cospecialized ornonspecialized) that might be formed. Because co-specialization involves a relational risk, the avail-ability of safeguard governance mechanisms, in-cluding exclusivity, should affect a firm’swillingness to engage in cospecialized alliances. Afirm will be less willing to start a cospecializedalliance with a partner that already maintains alli-ances with the firm’s rivals, because such a partnercould use its bargaining power to appropriate thequasi-rents generated by the transaction-specificinvestments made by the other firm. Thus, a firmwould be more likely to initiate a nonspecializedalliance rather than a cospecialized alliance whenthe partner is allied with the firm’s rivals.

Hypothesis 2. Alliances between rivals and po-tential partners increase the likelihood of for-mation of a nonspecialized alliance ratherthan a cospecialized alliance.

The theory I am formulating here does not lead toa prediction of a general tendency toward or againstexclusivity in alliance networks. Instead, I arguethat exclusivity is contingent on the level of alli-ance cospecialization in a network. Industries inwhich alliances involve greater cospecializationshould display a greater tendency toward compet-itive exclusion than industries in which alliances

involve little cospecialization. Even within an in-dustry with heterogeneous alliance types, the pres-ence of more highly cospecialized alliances shouldgenerate greater competitive exclusion than thepresence of less cospecialized alliances.

Countervailing Alliances

The practice of competitive exclusion, whilebeneficial for supporting cospecialization withinan alliance, generates a competitive risk. If rivalscannot link into a firm’s network and benefit fromaccess to the same partners, they may instead formcountervailing alliances that replicate the networkbenefits by enlisting similar (but not the same) part-ners. Such a response would lead to competitionamong rival alliance groups (Garcia-Pont & Nohria,2002; Gomes-Casseres, 1996; Nohria & Garcia-Pont,1991).

A countervailing alliance is one intended tomatch and neutralize the rivals’ alliance advantageby aligning firms facing a common competitivethreat. The rival alliance would increase the sa-liency of some potential complementarities ex-ploited by rivals. With a countervailing alliance, afirm could replicate the complementarities gener-ated by the rival alliance by selecting a partner thatis a close substitute (and thus a direct rival) of therival’s partner (firm 2 in Figure 1). Also, counter-vailing alliances align the competitive incentives offirms toward the mutual goal of counteracting theeffectiveness of the common rival alliance. Alli-ances among direct competitors generally sufferfrom incentive misalignments, because the questfor competitive advantage encourages the pursuitof asymmetric private benefits from the alliances,which leads to opportunistic behavior. Yet whentwo firms form a countervailing alliance against acommon rival entity, their incentives become com-petitively aligned. Efficiencies gained by the part-ners are used to gain market position from thecommon rival, not from the other alliance partners.A countervailing alliance therefore is likely togroup firms that rank the threat from the commonrival entity higher than the threat from other alli-ance partners. The incentive alignment within thecountervailing alliance would increase as the alli-ance partners’ niche overlaps with the commonrival entity increase, but the partners’ incentivealignment would decrease as their own niche over-lap increases.

Competitive dynamics research suggests thataction visibility and competitor dependence (thatis, level of niche overlap) increase the likelihood ofcompetitive response and the likelihood of match-ing moves (Chen & MacMillan, 1992). A matching

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move is a risk-averse response to a competitor’sthreatening action, a response intended to achievecompetitive parity (Knickerbocker, 1973). In thecase of an alliance between a rival and a resource-rich partner, a firm may either seek a countervail-ing alliance with a rival of that partner, as de-scribed above, or ally with the attractive partner, asdiscussed in the previous section. If the relation-ship between the rival and its partner is not exclu-sive, a countervailing alliance may not be the mosteffective response, since the firm could instead allydirectly with its rival’s partner. A countervailingresponse would require the identification of a po-tential partner that could effectively match the ri-val’s partner’s capabilities and network connec-tions. In some situations, available partners areonly second best relative to the rival’s own part-ners. Thus, firms may not systematically favorcountervailing alliances when they have the choiceof linking to their rivals’ networks. The choice be-tween partnering with a rival’s partners or formingcountervailing alliances will depend on the poten-tial partner’s relative endowments, not on a system-atic structural tendency. In contrast, when a rival’spartners are exclusive, a firm’s only option to neu-tralize its competitive disadvantage is to create acountervailing alliance.

Following the theoretical logic described above,the cospecialization of the alliances between rivalsand their partners will influence their level of ex-clusivity. When rival alliances are cospecialized,and thus more exclusive, countervailing allianceswill be more likely to be systematically selected.When rival alliances are nonspecialized, however,countervailing alliances may not be necessary, andtherefore should be less likely to be systematicallyfavored. In addition, the nature of the rival alli-ances may also influence the competitive reaction.Cospecialized rival alliances are likely to be morecompetitively effective than nonspecialized rivalalliances, since the ability to cospecialize opera-tions gives rivals the opportunity to generate effi-ciencies that cannot be obtained in nonspecializedalliances. Thus, countervailing alliance formationin response to rival cospecialized alliances wouldalso be more likely.

Hypothesis 3. The likelihood of alliance forma-tion between two firms is greater when thealliances between the firms’ respective rivalsare cospecialized rather than nonspecialized.

The level of cospecialization of rival alliances isalso likely to influence the level of cospecializationused in countervailing alliances. Because cospe-cialized rival alliances are more likely to be exclu-sive, they reduce the pool of partners available to

other firms. In most industries, the number of via-ble and effective substitutes for a rival’s partner islimited. In these situations, firms engaged in inter-network competition risk “strategic gridlock,” a sit-uation in which eligible partners for a countervail-ing alliance have been locked out by prior movers(Gomes-Casseres, 1996: 158). The more numerousthe cospecialized rival alliances that form aroundtwo potential partners, the greater the incentive topreemptively lock in the potential partner with acospecialized alliance. Therefore, potential part-ners facing cospecialized rival alliances would bemore likely to form a cospecialized countervailingalliance than a nonspecialized one.

Hypothesis 4. Cospecialized alliances betweentwo firms’ respective rivals increase the firms’likelihood of forming a cospecialized counter-vailing alliance rather than a nonspecializedalliance.

Countervailing alliances may be formed not onlywhen partners face rival alliances, but also whenthey face a single firm whose capabilities or scopeputs it in competition with both partners. The in-centive alignment among firms facing common ri-vals is sometimes problematic, since firms facingthe same rivals are likely to be rivals themselves.However, incentive alignment can be achieved in acountervailing alliance if potential partners facegreater competitive threat from the common rivalthan they face from each other. For example, in theEuropean mobile phone industry, several interna-tional “roaming” and data services alliances havebeen launched to compete against Vodafone, theleading multinational mobile operator in most Eu-ropean markets. The alliance partners have lowgeographical niche overlap with each other, butVodafone is a major competitor to all of them intheir respective markets.

According to transaction cost economics, alli-ance transactions are quasi-integration relationsthat permit levels of cospecialization betweenarm’s-length market transactions and hierarchicaltransactions (Hennart, 1993; Williamson, 1991). Ifthis is so, common rivals pose a greater competitivethreat than cospecialized rival alliances, since thehierarchical governance of common rivals shouldallow them to sustain a greater cospecialization andcoordination of activities than alliances betweenindependent firms. The competitive niche of com-mon rivals may reflect potential complementaritiesin scope, resources, and capabilities that the com-mon rival has integrated within its boundaries.Firms facing common rivals would be confrontedby the particular complementarities their rivals hadinternalized and would likely strive to replicate or


respond to those complementarities by the com-mon rivals rather than focusing on other potentialcomplementarities not explored by their rivals. Asa result, firms facing common rivals should bemore likely to form alliances. More specifically, ifintegrated common rivals are more competitivelyeffective than cospecialized rival alliances, the ef-fect of common rivals on alliance formation shouldbe greater than that of cospecialized rival alliances.That is, the likelihood of an alliance between twofirms increases if the members of a cospecializedrival alliance facing these firms merges into a singlecommon rival.

Hypothesis 5. The likelihood of an alliancebetween two firms is greater when they face thesame common rivals than when they face co-specialized alliances among their respectiverivals.

In summary, I propose that greater cospecializa-tion of alliances by rivals should lead firms towardinternetwork competition, as reflected by lowerlikelihood of their forming alliances with their ri-vals’ partners (Hypothesis 1) and higher odds oftheir forming countervailing alliances with the ri-vals of their rivals’ partners (Hypothesis 3). More-over, the odds of countervailing alliance formationshould be even higher when potential partners facea common integrated rival rather than a cospecial-ized rival alliance (Hypothesis 5). The type of alli-ances firms form should also be influenced bythose of rivals. Alliances with rivals’ partners, ifthey happen at all, are more likely to be nonspe-cialized (Hypothesis 2). If rivals form cospecializedalliances, countervailing alliances are also morelikely to be cospecialized (Hypothesis 4). Overall,the hypotheses provide a logically consistent set ofpredictions reflecting how cospecialization of alli-ances generates competitive dynamics that limitintranetwork competition and enhance internet-work competition.

METHODS

I tested the hypotheses for this study in the con-text of the international airline industry between1994 and 1998. The prevalence of cooperative re-lationships and the heterogeneity in dyadic com-petitive relationships made this industry a naturalcontext for the study. During the 1990s, strategicalliances among major global airlines dramaticallyincreased in frequency and scope. By the early1990s, alliances tended to involve route-specificagreements, but some alliances formed in the 1990sinvolved broad-based agreements covering multi-ple cooperative activities (GRA Incorporated, 1994;

U.S. General Accounting Office [GAO], 1995). Thegeographic focus of airline services also creates het-erogeneity in competitive relations: some pairs offirms compete over many markets, while manypairs do not overlap at all.

Few interorganizational networks are closed sys-tems with natural boundaries; network boundarydetermination often requires an arbitrary decision(Wasserman & Faust, 1994). In addition to horizon-tal alliances with other, similar airlines, largeglobal airlines also ally with small local “feeder”airlines, to increase hub traffic, and with providersof related services, such as airports, hotel chains,car renters, and so forth. This study focused on thestructure of the competitive and cooperative rela-tionships among the world’s major internationalscheduled passenger airlines. Accordingly, I didnot include in the sample airlines that only carriedcargo, only served domestic routes, or only flewcharter (unscheduled) flights. Moreover, I focusedonly on horizontal alliances between firms in thestudy population, excluding domestic feeder alli-ances and cross-industry alliances.

Global airline alliances perform a variety of co-operative activities. Some coordinated activities in-clude logistic or marketing cooperation, such asfacility sharing, joint maintenance, and frequentflyer program agreements (GAO, 1995). With veryfew exceptions, however, the most important coop-erative activity of global airline alliances is jointroute operation, a practice known in the industry as“code sharing.”1 International code sharing began

1 In code sharing, reservation systems list a flight seg-ment with the codes of both an operator and a partner, sothat both companies can market seats under their ownflight numbers. This arrangement allows the partner: (1)to market direct flights that it does not fly and (2) tomarket as its own “online” connecting flights some one-stop flight trajectories in which it only serves one seg-ment. For instance, Northwest can market as its ownconnecting flight the Detroit-Amsterdam-Budapest tra-jectory, even though its partner KLM operates the Am-sterdam-Budapest segment. Most customers strongly pre-fer “online” flights (traveling multiple segments with thesame airline) to “interline” flights (combining segmentson multiple airlines). Airlines with code-sharing agree-ments do not offer true online service, but they oftenimplement supportive practices (coordinated check-in,shared executive lounges, coordinated frequent flier pro-grams, or coordinated schedules) that allow them to offerservice quality near that of online service. Alliances cancoordinate revenues from shared flights in multipleways, including block-seat purchases (where the partnerbuys a block of seats from the operating airline), revenue-sharing agreements, etc. Pricing in code-sharing trips,may be coordinated in order to avoid a double marginal-

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in 1985, and it stimulated a wave of alliances in theindustry. Indeed, code sharing has been describedby airlines as “the glue that holds other cooperativeactivities together” (GRA Incorporated, 1994: ES-4).There are a few alliances that exclude code sharing,often because of antitrust restrictions. Code sharingincreases economies of scale in specific routes (al-lowing two firms to jointly serve more passengerswith fewer planes) and economies of scope throughthe network (extending the firms’ networks withsegments served by their partners).

The extent of alliance activities (particularlycode-sharing activities) varies widely among alli-ances. Most alliances involve code sharing and co-operation in one or a few routes (point-specificalliances). Yet some strategic or extensive alliancesinvolve a broad range of cooperative activities, in-cluding many code-sharing routes that integrate theregional or global networks of the partners (e.g.,KLM-Northwest, United-Lufthansa, etc.). These al-liances involve substantial cospecialization of net-work structures and ongoing information exchangeto facilitate joint coordination of activities. Sinceregulation in many countries limits foreign owner-ship of local airlines (and therefore cross-bordermergers) and also prohibits foreign airlines fromserving domestic markets (cabotage rights) or fromflying passengers directly to other third countries(fifth freedom rights), extensive alliances allowfirms to globalize their networks within the currentregulatory environment.

The time period in the sample (1994–98) was acritical junction in the evolution of alliance net-works in the global airline industry. Although in-ternational alliances began in the middle of the1980s, airline alliances remained mostly point-spe-cific until 1994 (about 78 percent of all alliances in1994 were point-specific). During the 1994–98 pe-riod, many strategic or extensive alliances wereinitiated (increasing 115 percent, while point-spe-cific alliances increased 20 percent). Major alliancegroups (Oneworld, Star, SkyTeam) began to emergetoward the end of this period.

Data

Actors in the network of large international air-lines were selected from the 1994–98 Airline Busi-ness 100 rankings, which list the 100 largest worldairlines by revenues for the years 1993–97. AirlineBusiness, which publishes these rankings annually,

is the leading monthly magazine for strategy-re-lated issues in the global airline industry. From the114 airlines that appeared in the rankings for atleast one year, I selected 99 firms after eliminatingpurely domestic airlines, all-cargo or all-charter air-lines, and majority-owned subsidiaries of other air-lines in the sample. Data on some airlines were notcontinuously available for all the years in question.Since the independent variables reflect relationswith third parties in the industry, missing data forone firm would influence all observations. To en-sure that longitudinal changes in the independentvariables reflected true changes in relations ratherthan sampling differences, I selected a stable subsetof 67 firms that reported consistent data on theircooperative and competitive relationships for allthe years of the study. The results reported here,based on the subset of 67 airlines, are fully consis-tent with those for the full network of 99 firms.

Alliance network. Data on alliances among air-lines were obtained from the alliance survey pub-lished by Airline Business for 1994–98. This isarguably the most comprehensive public source ofinformation on international airline alliances. Toensure maximum coverage of the alliances, AirlineBusiness collected data from multiple sources, in-cluding company self-reports and press releasescollected by industry experts. The survey, pub-lished in June of each year, included alliances ac-tive up to the previous month. Therefore, the alli-ance survey actually was a census of alliances inthe industry, except for feeder alliances, which itdoes not include.

I interpreted the term “alliance” broadly, includ-ing cooperative relationships ranging from point-specific code-sharing and narrow marketing agree-ments to extensive equity-based strategic alliancesinvolving joint operations and revenue poolingacross many markets. A symmetric network matrixL was created for each year, with each elementindicating whether an alliance existed between apair of firms. Therefore, the elements of matrix Lwere defined as:

lijt � �1 if firms i and j are linked by analliance during year t.

0 otherwise.

Matrix L can be divided to capture differencesbetween cospecialized and nonspecialized alli-ances. Cospecialized alliances (sijt � 1) were de-fined as extended alliances including a broad set ofcode-sharing routes (typically, alliances to provideconnections to a region or a continent) togetherwith a broad range of cooperative activities (fre-quent flyer programs, joint use of ground facilities,

ization problem, where the maximization of profits byboth partners independently does not lead to maximumprofits for the alliance (Brueckner & Whalen, 2000)


and so forth). The implementation of these alli-ances requires mutual adaptation of activities innetwork operations (cospecialization), sharing ofsensitive information, and joint coordination. Incontrast, nonspecialized alliances (nsijt � 1) weredefined as those including a few code-sharingroutes that did not comprise a regional or globalfeeding network (typically, they involve one or tworoutes between the partners’ home countries thatmay not have sufficient density to allow multipleairlines to serve the markets effectively). Thesepoint-specific alliances entail little mutual adapta-tion and are easy to reverse. They typically involvethe agreement to buy a block of seats on certain ofa partner’s flights but rarely entail attempts to co-specialize operations or provide service levels con-sistent with those of the partner. I defined the co-specialized and nonspecialized categories asmutually exclusive. Two raters knowledgeableabout airline alliances rated all the alliances usingthe short descriptions of alliance activities pro-vided by Airline Business.2 The raters reached aninitial interrater agreement of .82 and attained con-sensus after discussion with the author.

Competitive network. Competitive relationshipsreflect the extent of niche overlap between airlines.Niche overlap describes the dependence of a firmon resources and markets in which another firm ispresent (Chen, 1996). The competitive structure inan industry may then be represented as a network(an adjacency matrix) formed by these pairwisecompetitive relations. Although niches can be char-acterized in terms of markets, capabilities, andtechnologies, research suggests that overlap in out-put markets (geographic markets, product seg-ments, and customer niches) tends to exert a stron-ger influence in competitor identification andcompetitive response (Chen, 1996; Clark & Mont-gomery, 1999; Porac, Thomas, Wilson, Paton, &Kanfer, 1995). In the airline industry, which ischaracterized by multiple discrete geographicalmarkets and relatively homogeneous services,niche overlap reflects the degree of market com-monality or multimarket contact between airlines(Chen, 1996). Since firms have different scopebreadth and different levels of presence in specificmarkets, competitive relations may be asymmetric.Moreover, competitive relations may be “intransi-

tive” (that is, two firms facing high niche overlapfrom a common rival may not experience highniche overlap with each other).

I calculated dyadic niche overlap between air-lines by assessing their overlap over 12,847 inter-national city-pair markets connecting the world’s200 largest cities by air traffic volume. The measureof niche overlap of airline i with airline j at timet(�ijt) is the ratio between the size-adjusted sum ofthe markets of overlap between airlines i and j andthe size-adjusted sum of the markets served byairline i. The asymmetry of the measure capturesthe egocentric saliency of competitive relationshipsfor each firm, since the same level of overlap affectslarge and small firms differently (Chen, 1996; Han-nan & Freeman, 1989). The Appendix presents de-tails of the calculation of these niche overlap mea-sures. Dyadic niche overlap between airlinesranged from 0 to 1, with a mean of 0.03 and astandard deviation of 0.07. Fifty-seven percent ofairline dyads had zero market overlap.

Dependent Variable

Alliance formation, the dependent variable, rep-resents events in which an alliance did not existone year (lijt � 0) but existed the following year(lij, t � 1 � 1). For the analysis of alliance nature,additional indicators represented whether the alli-ances formed were cospecialized (sij, t�1 � 1, or 0otherwise) or nonspecialized (nsij, t�1 � 1, or 0 oth-erwise). Since the dependent variables were sym-metric for each pair of firms, the unit of analysiswas defined as the nondirectional dyad (that is,either dyad ij or dyad ji was in the sample, notboth). There were 144 new alliances formed be-tween 1994 and 1998, 112 nonspecialized and 32cospecialized.

Independent Variables

The first set of independent variables measuredwhether a potential partner already had alliancerelations with the rivals of the other potential part-ner, and the nature of those relations. If firms seekto connect to their rivals’ networks, these variableswill have a positive effect on alliance formation. Onthe other hand, if firms seek to exclude rivals fromtheir networks, or firms shun positions of redun-dancy inside networks, these variables will have anegative effect on alliance formation. My workingtheory suggested that the direction of the effectwould depend on whether the exchange relation-ships involved cospecialization. Since I examinedunordered pairs, I had to consider two possiblesituations: when firm i was a partner of firm j’s

2 Airline Business provides a one- or two-sentence de-scription of the activities each alliance entails. Althoughthose descriptions were adequate for classifying allianceswith sufficient reliability, they were not detailed enoughto permit calculation of continuous measures of alliancestrength or cospecialization.

2004 829Gimeno

rivals, and when firm j was a partner of firm i’srivals. Both conditions were equally relevant andwere aggregated in the measures. To aggregateamong all possible third parties, I calculated aweight pk, ij for each third party k equal to thepercentage that k accounted for of total revenues forall airlines in the sample except firms i and j (thatis, the percentage of all third-party revenues).Therefore, for each dyad, the sum of the weightsadded up to 1. The weights provided a consistentscaling for the effects through different third par-ties. The variables, which measured whether a po-tential partner had cospecialized or nonspecializedalliances with a firm’s rivals, were formally definedas:

Rivals’ cospecialized partnerijt �

�k�i, j

(sikt�jk, t � Sjkt�ik, t) pkt, ij

and

Rivals’ nonspecialized partnerijt �

�k�i, j

(nsikt�jk, t � nsjkt�ik, t) pkt, ij.

The second set of independent variables mea-sured whether potential partners had congruentcompetitive relations that would motivate counter-vailing alliances against a rival entity (either a rivalalliance or a common rival). The variables cospe-cialized rival alliances and nonspecialized rivalalliances evaluated whether the respective rivals oftwo potential partners maintained cospecialized ornonspecialized alliances. The measures were thesums of the number of such cospecialized and non-specialized alliances, weighted by the competitiverelations of those rival firms with the potentialpartners. The countervailing logic discussed aboverequires that the competitive relations of potentialpartners with the members of rival alliances bestronger than those with each other. For example, iffirms i and j had rivals k and l, respectively, thecondition requires that �ik be greater than �ij, and�jl greater than �ji. The variables looped through allthe alliances in the industry involving differentfirms and aggregated those that involved rivals ofthe potential partners. I aggregated these relationsusing weights reflecting the combined revenues ofboth members of the rival alliances.

Cospecialized rival alliancesijt �

�k�i, j

�ikt��ijt

�l�i, j, k�jlt��jit

�iktSklt�jlt( pkt, j � plt, ij)

and

Nonspecialized rival alliancesijt �

�k�i, j

�ikt��ijt

�l�i, j, k�jlt��jit

�iktnsklt�jlt( pkt, ij � plt, ij).

The variable common rivals measured whetherpotential partners had competitive relations withthird-party rivals that were more intense than thecompetitive relations with each other. The variablewas the sum of the combined competitive ties tocommon rivals, provided that those ties weregreater than those linking the pair to each other,and weighted by the revenues of those commonrivals:

Common rivalsijt � �k�i, j

�ikt��ijt,�jkt��jit

�ikt�jktpkt, ij.

I chose a unit scale of the rival alliances andcommon rivals variables that facilitated compari-son of coefficients. Thus, the magnitude of the ef-fect on alliance formation of an alliance betweentwo nonoverlapping rivals who jointly captured 5percent of the revenues of third parties in the sam-ple would be comparable to the effect magnitude ofa single common rival with the same combinedscope that alone controlled 5 percent of the third-party revenues.

Control Variables

To avoid spurious correlations, it was importantto control for other variables that might influencealliance formation. Such control was particularlyimportant in this study because the independentvariables represented structural measures based oncompetitive and cooperative networks, yet othernetwork dimensions might also affect alliance for-mation.

First, I controlled for previously used anteced-ents of alliance formation, such as size and perfor-mance (Burgers, Hill, & Kim, 1993; Gulati, 1995).Larger or better-performing firms may be more at-tractive partners as they can bring superior re-source endowments to alliances. The tendency forfirms with high status to ally with other high-statusfirms may encourage alliance formation amongfirms of similar size and performance. To accountfor these effects, I controlled for the size of thelarger firm of a dyad (the logarithm of the sales ofthe larger firm) and for the ratio of the sizes in adyad (the log of the ratio of the smaller firm’s salesto the larger firm’s sales). I also controlled for theaverage performance of firms (the mean return onsales) and for the difference in performance (theabsolute difference between two firms’ perfor-mance levels). I also included year dummy vari-


ables to control for industry-level effects influenc-ing alliance formation propensities in differentyears. The effects captured by these year dummiesmay be exogenous, such as regulation or macroeco-nomic context, or endogenous, such as the globalalliance density in the industry.

Second, I controlled for the level of dyadic nicheoverlap between potential partners. Moderateniche overlap between firms might facilitate theefficient integration of networks and the potentialfor consolidation of activities and scale economiesin some routes, but substantial niche overlap mightindicate excessive duplication and a strong com-petitive orientation. I controlled for the linear andquadratic effect of dyadic niche overlap, after cen-tering the variable. Since niche overlap is asymmet-ric, I used the maximum directional level of nicheoverlap (a maximum of �ij and �ji) as the controlvariable. Moreover, since the emphasis was on in-ternational alliance formation, I excluded from therisk set any pairs of firms from the same country.

Third, I controlled for dyadic complementaritybetween potential partners. If two potential part-ners occupy complementary niches, they may facealliances between their respective rivals or con-front common rivals spanning their respectiveniches. Controlling for complementarity avoidsspurious results. Complementarity requires morethan just low niche overlap; it requires that thecombination of scopes and capabilities result invalue creation given the alternative offerings in theindustry. Complementarity was measured as theratio between the size-adjusted sum of city-pairmarkets not currently served by the airlines thatcould be served effectively if firms were to fullyintegrate their networks and the average size-adjusted market scope of the airlines. For example,a value of 0.1 indicated that the new markets thatcould be effectively served through an alliancewould represent 10 percent of the firms’ currentactivities. The Appendix presents technical detailsof the calculation of this measure.

Fourth, I controlled for the different propensitiesof dyad members to engage in cooperative and com-petitive relationships. The total volume of relationsof firms to all other firms in the industry mayindicate a propensity by actors to form ties. Disre-garding these differences in actors’ propensities tocreate ties may create a network autocorrelationbias, since errors in multiple dyads that share thesame firm may be correlated because of the “com-mon actor” effect (Lincoln, 1984). A solution forthis problem is to control for the competitive andcooperative relations of dyad firms to all otherfirms in the sample, since those control variableswould capture a potential common actor effect

(Lincoln, 1984). Four variables controlled for theprior alliance activity of dyad members: the sum ofthe cospecialized alliances by both firms weightedby partner size, its square (after mean centering),and the size-weighted sum of the nonspecializedalliances of both firms, and its square (after center-ing) (Gulati, 1995). In addition, these variables alsocontrolled for the tendency of firms to partner withhigh-centrality firms and the tendency of firmswith high status in an alliance network to ally withother high-status firms.3 The quadratic effects ac-counted for possible reduction in alliance forma-tion propensity for firms with large portfolios ofalliances. Firms also occupy different competitiveniches in an industry in terms of their overall com-petitive relations to other firms; some niches maybe more crowded than others. Like technologicalcrowding (Stuart, 1998), competitive crowding in afirm’s niche may influence alliance activity. I mea-sured a firm’s competitive crowding as the reve-nue-weighted sum of the competitive relations ofthe firm to every other firm in the industry, exclud-ing the other member of a given pair. I controlledfor a dyad’s competitive crowding using the sum ofthe two firms’ crowding measures (Stuart, 1998)and controlled for the pair’s difference in crowdingusing the absolute difference in competitive crowd-ing between potential partners.

Finally, I controlled for other network mecha-nisms that might lead to alliance formation. Alli-ance network research has shown that firms tend toselect partners from others with direct and indirectnetwork cohesion (Gulati, 1995; Gulati & Gargiulo,1999), since a network provides knowledge andreferrals about the capabilities and reliability ofpotential partners. The study examined dyads atrisk of forming their first alliance and thereforelacking direct ties before the event. However, suchfirms could have indirect ties through alliances tocommon partners. I controlled for these indirectties through three variables that provided revenue-weighted sums of indirect ties through commonpartners in three cases: when the potential partnershadcospecialized alliances with the common part-ners, when the ties to common partners were non-specialized alliances, and when they were a mix-ture (one cospecialized, one not).

3 Traditional network status measures should be usedin directional networks, since they focus on the “in-degrees” received by an actor and the status of originat-ing actors. For nondirectional relations such as thosestudied here, network status converges to centrality(Wasserman & Faust, 1994).

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TABLE 1Descriptive Statistics and Correlationsa

Variable Mean s.d. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1. Rivals’ cospecialized partner 0.01 0.012. Rivals’ nonspecialized partner 0.01 0.02 �.093. Nonspecialized rival alliances 0.01 0.01 �.04 .134. Cospecialized rival alliances 0.01 0.01 .39 .04 .095. Common rivals 0.00 0.01 .16 .12 .18 .196. Large firm salesb 8.10 0.96 .26 .07 .05 .03 �.057. Small/large firm salesb �1.28 0.91 �.06 .07 �.01 �.02 �.02 �.648. Mean performance 0.00 0.07 .06 .06 .07 .06 .03 .08 �.059. Performance difference 0.09 0.12 �.04 .00 �.14 �.03 �.04 �.07 .08 �.55

10. Niche overlap �0.01 0.07 .05 �.01 �.20 �.18 .10 .24 �.09 .01 �.0211. Niche overlap squared 0.01 0.03 .00 �.02 �.07 �.07 .11 .08 �.05 .00 �.01 .7212. Complementarity 0.04 0.07 .15 .05 .00 .19 .03 .07 .06 .03 .02 �.03 �.0213. Dyad’s cospecialized alliances �0.01 0.08 .58 .08 �.02 .15 .09 .45 �.11 .13 �.06 .17 .04 .0414. Dyad’s cospecialized alliances squared 0.01 0.01 .34 �.04 .02 .05 .09 .05 .08 .04 �.06 .08 .04 �.02 .4715. Dyad’s nonspecialized alliances 0.00 0.11 .02 .55 �.04 �.16 �.12 .26 �.01 .10 �.02 .09 .04 .03 .21 �.0216. Dyad’s nonspecialized alliances squared 0.01 0.02 �.07 .27 .03 �.05 �.07 .08 .00 .07 .02 .03 .01 �.01 .01 �.04 .4817. Competitive crowding, sum 0.12 0.05 .36 .14 .39 .69 .45 .11 �.06 .06 �.03 �.07 .01 .23 .12 .06 �.20 �.0418. Competitive crowding, difference 0.04 0.03 .14 .10 .20 .36 .16 �.03 �.03 .07 �.03 �.14 �.03 .13 �.03 .01 �.21 �.06 .6919. Common partners, cospecialized 0.00 0.01 .17 .06 �.01 .03 .04 .10 .03 .03 .00 .05 .02 .02 .32 .32 .11 .07 .03 �.0120. Common partners, mixed 0.01 0.02 .16 .19 �.01 �.02 .01 .16 .03 .07 �.02 .06 .00 .00 .36 .15 .33 .20 �.01 �.06 .0221. Common partners, nonspecialized 0.01 0.02 �.05 .35 �.01 �.11 �.06 .09 .08 .07 .01 .07 .01 �.04 .12 .04 .55 .52 �.14 �.16 .12 .14

a n � 6,835; r � .02 implies significance at p � .05.b Logarithm.

TABLE 2Discrete-Time Event History Model of Alliance Formation Using Logistic Regression Analysisa

Variable Coefficient Model 1 Model 2 Model 3 Model 4Effect MultipliersBased on Model 4

Rivals’ cospecialized partner b1 �47.67* (13.73) �47.86* (13.47) 0.55*Rivals’ nonspecialized partner b2 10.22 (6.44) 10.09 (6.62) 1.17Nonspecialized rival alliances b3 16.76† (9.46) 5.28 (9.96) 1.06Cospecialized rival alliances b4 64.14* (17.53) 70.23* (17.68) 2.00*Common rivals b5 106.36* (20.84) 99.36* (22.12) 1.66*Large firm salesb 0.49* (0.19) 0.53* (0.19) 0.74* (0.19) 0.78* (0.19) 2.10*Small/large firm salesb 0.35* (0.13) 0.37* (0.13) 0.46* (0.12) 0.50* (0.13) 1.57*Mean performance �1.29 (1.51) �1.40 (1.54) �1.86 (1.64) �1.96 (1.64) 0.86Performance difference �1.10 (1.06) �1.10 (1.08) �0.96 (1.18) �1.05 (1.20) 0.88Niche overlap 5.58* (1.75) 5.74* (1.87) 7.11* (1.91) 6.88* (1.97) 1.63*Niche overlap squared �8.11 (6.44) �10.05 (7.19) �11.31* (4.93) �12.41* (5.28) 0.67*Complementarity �0.90 (1.66) 0.10 (1.27) 0.42 (1.23) 0.99 (1.05) 1.07Dyad’s cospecialized alliances 1.23 (2.25) 4.35† (2.38) 0.63 (2.20) 3.44 (2.25) 1.30Dyad’s cospecialized alliances squared �16.94 (11.44) �10.48 (11.28) �16.86 (11.14) �10.91 (11.36) 0.91Dyad’s nonspecialized alliances 1.91 (1.31) 1.08 (1.37) 2.22† (1.33) 1.61 (1.44) 1.19Dyad’s nonspecialized alliances squared �9.91† (5.81) �11.41† (6.23) �9.24 (5.83) �11.23† (6.32) 0.81†

Competitive crowding, sum �4.90 (3.85) �3.36 (4.17) �28.51* (6.23) �25.09* (6.27) 0.31*Competitive crowding, difference �4.84 (5.72) �6.27 (5.43) 5.16 (6.35) 2.60 (6.06) 1.09Common partners, cospecialized 23.29* (4.53) 22.31* (4.56) 24.65* (4.66) 24.14* (4.74) 1.33*Common partners, mixed 11.26* (3.89) 11.76* (3.92) 11.79* (3.90) 12.28* (3.89) 1.29*Common partners, nonspecialized �0.24 (3.61) �2.59 (3.71) �0.61 (3.61) �2.92 (3.73) 0.94Year 1995 0.48† (0.24) 0.50* (0.24) 0.52* (0.24) 0.55* (0.24) 1.27*Year 1996 �0.21 (0.29) �0.24 (0.29) �0.38 (0.30) �0.40 (0.29) 0.84Year 1997 0.10 (0.29) 0.03 (0.29) �0.04 (0.30) �0.10 (0.30) 0.96Constant �6.78* (1.52) �7.08* (1.43) �7.63* (1.51) �7.93* (1.44)

Hypothesis testsc

H1: b1 � b219.29* 19.55*

H3: b4 � b3 7.75* 13.85*H5: b5 � b4 4.51* 1.80

Observations 6,835 6,835 6,835 6,835Log-likelihood (df) �624.18 (19) �614.15 (21) �611.51 (22) �601.79 (24)Log-likelihood, null model �698.31 �698.31 �698.31 �698.31Likelihood ratio chi-square, relative to null

model204.10* 229.05 205.02 215.77*

McFadden’s R2 .11 .12 .12 .14

a Robust estimates of standard errors are in parentheses.b Logarithm.c Values are Wald chi-squares with one degree of freedom.† p � .10* p � .05Two-tailed tests.

Statistical Technique

Because alliance formation is inherently dy-namic, I modeled it using a discrete-time eventhistory approach based on logistic regression (Alli-son, 1982), in which the odds of an alliance forma-tion event in a given year is a function of time-varying covariates. I divided event histories foreach dyad into yearly spells and used logistic re-gression analysis to determine whether an alliancehad not formed yet or had formed in that year . Theyear 1994 was taken as the initial network condi-tion, and models evaluated alliance formation from1995 to 1998. Alliances that had not formed by1998 were considered right-censored.

Since there was a remaining risk of unobservedheterogeneity among the dyads in the model, Itested random-effects “logit” models with dyad-level unobserved heterogeneity (Gulati, 1995). Therandom effects were insignificant in all cases. Thelack of significance may indicate lower unobservedheterogeneity in this single-industry sample thanthere would be in a multi-industry sample (Gulati,1995). Nevertheless, to avoid any potential effectsof autocorrelation, I based results on a robust esti-mation of the logistic regression that relaxed theassumption of independence of errors within dyads(Rogers, 1993).4

I used a competing-risks discrete-time event his-tory approach to model the formation of cospecial-ized and nonspecialized alliances by dyads (Alli-son, 1982; Hachen, 1988). The competing risksapproach allowed me to simultaneously model thehazards of forming cospecialized and nonspecial-ized alliances in a given dyad and to compare theeffect of independent variables on both hazards.Following Allison (1982), I estimated the discrete-time version of the competing-risks model using amultinomial logit model, where each pair-yearspell has three possible outcomes: no alliance yet (aright-censored observation); weak alliance formed;and strong alliance formed. Dyads stayed in thesample until an alliance of some type was formedor until the end of the sample window. The “noalliance yet” category was used as the baselineoutcome. I report results based on a robust estima-tion of the multinomial logit model that relaxed theassumption of independence of errors withindyads.

RESULTS

Table 1 presents descriptive statistics and corre-lations. Table 2 presents the results of the discrete-time event history analysis of alliance formation,using a logistic regression model, described above.Table 3 presents the competing risks analysis ofcospecialized and nonspecialized alliance forma-tion, using a multinomial logit model in whichthese risks are jointly estimated. Since the hypoth-esis tests are based on comparisons between coef-ficients, the tables present the relevant Wald testsof comparisons of coefficients. Beyond statisticalsignificance, the relative magnitude of the effectsshould also be assessed. Coefficients that share thesame scale can be compared directly in raw form.All hypothesis tests were based on comparisons ofraw coefficients. I also present effect multipliers tocompare coefficients on different scales. Thesemultipliers are akin to standardized coefficientsand reflect the relative magnitude of the effects.They describe the change in the odds-ratio associ-ated with a one standard deviation increase in therespective independent variable, calculated asexp(�x�x).

Hypothesis 1 states that the likelihood of allianceformation between a firm and its rivals’ partners islower when the rivals’ alliances are cospecializedrather than nonspecialized. Models 2 and 4 of Ta-ble 2 (the first two rows) show the relevant effects.Alliance formation was significantly lower whenpotential partners have cospecialized allianceswith rivals, but not when they had nonspecializedalliances. With multipliers assessing the magnitudeof the effect, an increment of one standard devia-tion in cospecialized alliances between rivals andpotential partners decreased the odds of allianceformation by 45 percent; an increment of one stan-dard deviation in nonspecialized alliances betweenrivals and potential partners increased alliance for-mation by 17 percent (a statistically insignificanteffect). The specific test of Hypothesis 1 involvedthe comparison of the coefficients for these twoeffects. These two coefficients were significantlydifferent from each other (p � .01), supporting Hy-pothesis 1. The differences suggest that the ten-dency toward competitive exclusion is not a gen-eral feature of alliance networks but contingent onthe level of cospecialization among the allianceslinking rivals with potential partners. Competitiveexclusion results when rivals are tied to potentialpartners with cospecialized alliances, but not whenrivals are tied to potential partners with nonspe-cialized ties.

Hypothesis 2 proposes that alliances between ri-vals and potential partners have a greater exclusion

4 The Stata procedure used were the logit and “mlogit”commands with the “robust cluster (pair)” options,where “pair” is a variable that indicates an airline dyad.


TABLE 3Discrete-Time Competing Risks Model of Alliance Formation Using Multinomial Logit Analysisa

Variable Coefficient

Model 5 Model 6

Wald Testsc

Effect Multipliers based onModel 6

Nonspecialized Cospecialized Nonspecialized Cospecialized Nonspecialized Cospecialized

Rivals’ cospecialized partner b1 �31.10† (16.79) �112.52* (30.93) H2: bns1 � bs

1 5.42* 0.68† 0.25*Rivals’ nonspecialized partner b2 22.23* (7.14) �32.56 (20.03) H2: bns

2 � bs2 6.76* 1.42* 0.60

Nonspecialized rival alliances b3 7.24 (11.73) �4.69 (18.01) 0.33 1.09 0.95Cospecialized rival alliances b4 41.46† (23.76) 136.43* (32.55) H4: bs

4 � bns4 5.81* 1.51† 3.85*

Common rivals b5 83.88* (23.35) 157.74* (47.96) 2.09 1.53* 2.24*Large firm salesb 0.34 (0.22) 1.15* (0.43) 0.57* (0.21) 1.86* (0.45) 6.95* 1.72* 5.92*Small/large firm sales 0.31* (0.15) 0.48† (0.25) 0.40* (0.15) 0.92* (0.23) 3.76† 1.44* 2.31*Mean performance �1.39 (1.89) �1.13 (2.16) �1.88 (2.00) �2.57 (2.26) 0.05 0.87 0.83Performance difference �1.32 (1.23) �0.04 (2.11) �1.20 (1.34) �0.20 (2.60) 0.12 0.87 0.98Niche overlap 5.52* (1.70) 6.98 (4.95) 6.04* (2.05) 7.37 (5.26) 0.06 1.54* 1.69Niche overlap squared �6.44 (4.48) �26.30 (23.62) �8.25† (4.32) �32.31 (24.52) 0.93 0.76† 0.35Complementarity �1.76 (2.34) 0.51 (2.45) 0.45 (1.48) 2.97* (1.30) 1.72 1.03 1.24*Dyad’s cospecialized alliances 1.26 (2.63) 2.68 (5.06) 2.97 (2.67) 8.82* (4.28) 1.38 1.26 1.98*Dyad’s cospecialized alliances squared �28.22 (21.78) �8.06 (16.62) �23.24 (20.17) 1.19 (18.87) 0.83 0.81 1.01Dyad’s nonspecialized alliances 2.86† (1.46) �1.43 (2.97) 1.11 (1.57) 4.14 (3.52) 0.63 1.13 1.57Dyad’s nonspecialized alliances squared �11.71† (6.18) �3.58 (16.19) �11.54† (6.50) �12.41 (22.60) 0.00 0.81† 0.79Competitive crowding, sum �6.50 (4.63) 0.46 (7.20) �23.74* (7.07) �34.10* (12.48) 0.54 0.33* 0.21*Competitive crowding, difference �8.82 (7.33) 5.23 (9.51) �5.24 (7.54) 28.64* (11.81) 5.91* 0.84 2.61*Common partners, cospecialized 20.25* (5.91) 30.03* (7.70) 20.25* (5.84) 31.31* (7.91) 1.39 1.27* 1.44*Common partners, mixed 10.66* (4.35) 12.88† (7.72) 11.28* (4.32) 14.53† (7.76) 0.14 1.26* 1.35†

Common partners, nonspecialized 0.25 (4.00) 1.02 (7.44) �2.98 (3.94) �1.54 (8.48) 0.02 0.93 0.97Year 1995 0.39 (0.27) 0.84 (0.61) 0.44† (0.27) 1.05* (0.51) 1.11 1.21† 1.57*Year 1996 �0.20 (0.32) �0.12 (0.76) �0.33 (0.32) �0.62 (0.69) 0.14 0.87 0.77Year 1997 0.12 (0.32) 0.20 (0.68) 0.02 (0.32) �0.53 (0.71) 0.52 1.01 0.80Constant �5.40* (1.69) �15.36* (3.65) �6.38* (1.63) �18.58* (3.62) 9.61*

Observations 6,835 6,835Log-likelihood �682.74 (38) �648.77 (48)Log-likelihood, null model �774.59 �774.59Likelihood ratio chi-square, relative to

null model298.63 343.85

McFadden’s R2 .12 .16

a Robust estimates of standard errors are in parentheses.b Logarithm.c bns � bs.† p � .10* p � .05Two-tailed tests.

effect on cospecialized alliance formation than onnonspecialized alliance formation. The competingrisks analysis in model 6 in Table 3 (the first tworows) displays the respective effects of these vari-ables on cospecialized and nonspecialized allianceformation. An increase of one standard deviation inthe level of cospecialized alliances between a firm’spotential partner and its rivals reduced the odds ofcospecialized alliance formation by 75 percent butreduced the odds of formation of nonspecializedalliances by only 32 percent. A Wald test indicatedthat the coefficients associated with these multipli-ers were significantly different from each other inthe expected direction (p � .05). This result indi-cates that cospecialized alliances between rivalsand potential partners have a very powerful exclu-sion effect on the formation of cospecialized alli-ances but a weaker one on the formation of nonspe-cialized alliances.

The effect of nonspecialized alliances betweenrivals and potential partners is also noteworthy. Anincrease of one standard deviation in the level ofnonspecialized alliances between a firm’s potentialpartner and the firm’s rivals reduces the odds ofcospecialized alliance formation by 40 percent, butit increases the odds of nonspecialized allianceformation by 42 percent. These effects are signifi-cantly different from each other in the expecteddirection (p � .01), according to a Wald test. Theresults show that direct rivals can develop allianceswith the same partner, provided that those are non-specialized alliances.

Hypothesis 3 states that firms are more likely toform countervailing alliances in response to cospe-cialized rival alliances than in response to nonspe-cialized rival alliances. Models 3 and 4 in Table 2examine the effects of nonspecialized and cospe-cialized rival alliances on alliance formation (rows3 and 4). According to model 4, the odds of allianceformation were significantly increased by the pres-ence of cospecialized rival alliances (p � .01), butnot by the presence of nonspecialized rival alli-ances. An increase of one standard deviation incospecialized rival alliances increased alliance for-mation by 100 percent, but an increase of one stan-dard deviation in nonspecialized rival alliancesonly increased alliance formation by 6 percent. AWald test indicated that the effect of cospecializedrival alliances on alliance formation was greaterthan that of nonspecialized rival alliances (p � .01),supporting Hypothesis 3.

Hypothesis 4 examines the effect of cospecial-ized rival alliances on the odds of formation ofcospecialized and nonspecialized alliances. Model6 in Table 3 (row 4) presents the relevant effects.The presence of cospecialized rival alliances had a

very powerful effect on the formation of cospecial-ized countervailing alliances (p � .01) and, moremarginally, on the formation of nonspecializedcountervailing alliances (p � .1). Whereas an incre-ment of one standard deviation in cospecializedrival alliances increased the odds of nonspecializedalliance formation by 51 percent, the same changeincreased the odds of cospecialized alliance forma-tion by 285 percent. A Wald test showed that thesetwo coefficients were statistically significant fromone another in the expected direction (p � .05).

Finally, Hypothesis 5 compares the effect on thelikelihood of alliance formation of common rivalsand cospecialized rival alliances. Models 3 and 4 inTable 2 (rows 4 and 5) provide the results. Theresults suggested that both cospecialized rival alli-ances and common rivals lead to the formation ofcountervailing alliances (p � .01). A one standarddeviation increase in common rivals would in-crease the odds of alliance formation by 66 percent.Specifically, it would increase the odds of forma-tion of nonspecialized alliances by 53 percent, andthose of formation of cospecialized alliances by 124percent (multipliers in Table 3). Yet a Wald com-parison of the coefficients of common rivals andcospecialized alliances showed that these effectswere not significantly different from each other,failing to support the hypothesis. Countervailingalliance formation in response to common rivalentities was the same whether the rival entity wasan integrated rival or a cospecialized rival alliance.

DISCUSSION AND CONCLUSIONS

This study examined the embeddedness of dy-adic alliance formation in the context of competi-tive and alliance relations with third parties. Ifound that partner selection and the type of alli-ances formed were influenced by competitive em-beddedness. In particular, when responding to thealliances made by their rivals, firms focused onallying with their rivals’ partners (generating in-tranetwork competition) or on forming countervail-ing alliances against the rivals and their partners(generating internetwork competition). The find-ings do not support a naıve view that inter- orintranetwork competition will be systematicallypredominant: the outcome depends on the level ofalliance cospecialization. Intranetwork competi-tion is favored when alliances by rivals involve lowcospecialization, but it is avoided when alliancesby rivals are highly cospecialized. In situations ofhigh alliance cospecialization, internetwork com-petition is favored. Thus, the effects of competitiveembeddedness on alliance formation are contin-gent on the level of cospecialization of alliances.


Given the dearth of research that carefully modelsthe interactions between alliance and competitivenetworks, this is a promising avenue for futureresearch in alliance competition (Gomes-Casseres,1996; Silverman & Baum, 2002; Stuart, 1998).

This finding provides a point of convergence be-tween two current logics of competition in alliancenetworks: internetwork competition, emphasizingthe emergence of polarized and exclusive allianceconstellations, and intranetwork competition, em-phasizing the selection of powerful brokering andbridging positions within networks. Effective alli-ance strategies in one context may not be effectivein the other. Researchers and managers should notassume that one logic of competition systematicallypredominates in all contexts. The results suggestthat alliance cospecialization determines whichtype of network competition will predominate in agiven context. Most industries, like the airline in-dustry, probably have a mixture of intra- and inter-network competition, and changes in alliance co-specialization would shift that balance. In theairline industry, the network of cospecialized alli-ances seem to follow the logic of rival and exclu-sive complementary blocks described by Nohriaand Garcia-Pont (1991), while nonspecialized alli-ances allow rivals to share common partners. Theincreased levels of alliance cospecialization in themid-1990s triggered the emergence of competingalliance groups.

The model describes some rules of alliance for-mation and partner selection that, after interac-tions, could lead to the emergence of particularnetwork structures. In the cospecialized alliancenetwork, alliances influence the formation of coun-tervailing alliances by rivals, and the avoidance ofalliances with rivals’ partners. When combinedwith the strong tendency of firms to partner withthose firms with which they have prior direct orindirect ties, these competitive influences wouldlead to the formation of internally cohesive groupsthat are separate from other groups. Judging fromthe effect multipliers, it appears that competitiveexclusion and countervailing alliance effects are atleast as important as the effect of indirect allianceties (referrals from common partners) in selectingpartners and structuring these constellations. In away that resembles Heider’s (1946) famous theoryof balanced triads, in the present results group for-mation is not only driven by the inclusion of thefriends of one’s friends, but also, critically, by thetendency to include the enemy’s enemy and toexclude the friend’s enemy and the enemy’s friend.Without the latter pressures to exclude rivals, ahighly connected but inclusive group would even-tually emerge from the interaction of actors. In the

network of nonspecialized alliances, however, thetendency is for inclusive networks in which directrivals can connect to (and compete for) the samepartners.

Figure 2 depicts some tentative evidence of howthe different patterns of cospecialized and nonspe-cialized alliance formation resulted in emergingstructural network patterns. The figure plots a mea-sure of clusterability (the tendency of networks toform cohesive coalitions) over time for the overallalliance network and for the subnetworks of cospe-cialized and nonspecialized alliances. The measureis the weighted overall clustering coefficient,which measures the percentage of triads in whichalliances exist between ik, kj, and ij, over the triadswhere an alliance exists between ik and kj (Borgatti,Everett, & Freeman, 2002). During the period 1994–98, the overall alliance network had a slight ten-dency toward greater clusterability. Yet separatingthe effects of the two subnetworks reveals that thistendency was due to the increase in clusterabilityin the cospecialized alliance network, which nearlydoubled over the period. The nonspecialized alli-ance network, on the other hand, displayed stable,low levels of clusterability. This descriptive evi-dence demonstrates a link between the competitivedeterminants of alliance formation and the emerg-ing structural patterns in the network.

The study also has important implications for theresearch on partner selection. Researchers have tra-ditionally viewed partner selection as primarily aquest to reduce partner uncertainty and find reli-able partners. Partner selection has been depictedas a local network search among firms that haveprior direct or indirect ties to the searching firm.Those ties provide knowledge and referrals aboutthe capabilities and reliability of the potential part-ner and reduce partner uncertainty (Gulati, 1995;Gulati & Gargiulo, 1999). Firms then create alli-ances with the potential partners in the local setthat provide complementary capabilities. Thisstudy extends that view in two ways. First, it sug-gests that competitive embeddedness guides thechoice of partner beyond complementary capabili-ties. Given multiple potential partners with com-plementary capabilities, firms will select those thatallow them to match or neutralize their rivals’ ad-vantages. Second, competitive embeddedness mayfacilitate partner selection beyond the local net-work scan of direct and indirect contacts. Potentialpartners with compatible competitive networks (forinstance, with common rivals or cospecialized rivalalliances) may be less uncertain, since their com-petitive objectives are aligned. Potential partnerswith conflicting competitive networks (for in-stance, with cospecialized alliances to rivals) could

2004 837Gimeno

be avoided. Although alliance network embedded-ness clearly contributes to partner selection (theodds of alliance formation are four times higheramong firms with indirect connections), its impor-tance may have been overplayed in the literature.For example, 30 percent of the alliances in thesample were formed among firms with no priordirect or indirect alliance ties. It is likely that com-petitive embeddedness considerations contributedto the selection of those partners.

Limitations and Extensions

Given the single-industry data used in this study,the results should be examined for their generaliz-ability in other contexts. My theory did not rely onany idiosyncratic characteristic of the global airlineindustry and, therefore, the theoretical predictionsshould generalize to other industries. Generaliz-ability is only claimed for the contingent predic-tions. The relative prevalence of inter- or intranet-work competition may vary across industries andcontexts. However, the predictions that increasedcospecialization of alliances should lead to greatercompetitive exclusion and a greater tendency to-ward countervailing alliances should be generaliz-able.

The airline industry is idiosyncratic because ofregulation that limits competitive and corporate

strategy choices, and the government ownership ofmany airlines. Regulations generally forbid airlinesfrom establishing transfer hubs beyond their na-tional soil, even though they may serve foreignmarkets through connection to their home markets.Acquisitions and large equity stakes are also se-verely restricted. As a result, airlines have turned toalliances as a mechanism for globalization and maymaintain alliance relations to govern transactionsthat in other industries would perhaps be governedwithin firm boundaries. However, although theseconsiderations may affect the baseline hazard ofalliance formation, it is not clear that they wouldproduce systematic coefficient biases in the direc-tions of the results.

Several extensions appear worth exploring in fu-ture work. First, the performance effects of allianceformation choices could be examined, both at thefirm and the industry levels. Research could exam-ine the performance implications of inter- and in-tranetwork competition. Although internetworkcompetition creates value when firms cospecializetheir operations, such value probably dissipateswhen rivals form countervailing alliances. This dis-sipation might lead to situations in which firmsbecome irreversibly linked by cospecialized alli-ances, yet the benefits from cospecialization havebeen distributed away in the competition for cus-tomers. Second, the impact of environmental un-

FIGURE 2Evolution of Clusterability in the Network of Cospecialized and Nonspecialized Alliances

a The index is calculated as “nonvacuous transitive triples” divided by all “ordered triples” with indirect ties.


certainty could be examined, since it may influencethe predominance of internetwork and intranet-work competition. Because of “strategic gridlock”in internetwork competition, path-dependent evo-lutionary processes may lead to structural inertiaand “lock-in” in network structure. Firms may notbe able to change partners because alternative part-ners are already taken up by competitors. Such asituation is hazardous in an environment with highuncertainty and variability. Less cospecializationand greater intranetwork competition creates amore fluid and adaptive industry environment.Third, it would be interesting to examine compet-itive embeddedness in the context of vertical alli-ance relations, such as supply chain and distribu-tion channel alliances (Bonaccorsi & Giuri, 2001;Fein & Anderson, 1997). In nonspecialized verticalrelations, buyers and suppliers have an incentive tobe inclusive in order to reach the broadest market,and thus rivals may share suppliers and channels.Yet vertical cospecialization may lead to exclusivevertical alliances, which may incite other counter-vailing alliances and transform a vertical marketinto a set of exclusive vertical networks. Fourth, itwould be worth exploring the effect of firm-specificresource endowments on the structuring of net-works. While this study focused on exclusivity asan efficiency-enhancing mechanism in cospecial-ized transactions, exclusivity may also result fromasymmetries in initial bargaining power that are theconsequence of heterogeneous resource endow-ments. Powerful firms may expect exclusivity fromtheir partners without pledging exclusivity tothem. In that sense, initial power asymmetries mayaffect the structure of an emerging alliancenetwork.

In conclusion, the study shows that the structureof competitive and alliance relations surroundingpotential partners and the cospecialization of alli-ances in an industry influences alliance formation.In competitive contexts, firms with cospecializedalliances may pledge their mutual commitment byshunning alliances with their rivals’ partners andtheir partners’ rivals. These rivals, in return, mayform countervailing alliances that attempt to repli-cate their opponents’ alliance benefits. That behav-ior influences the evolution of the network towardpolarization and internetwork competition. Non-specialized alliances, on the other hand, do notrequire exclusivity, and therefore they facilitate amore fluid intranetwork competition. This articleshows how uncoordinated alliance formation be-haviors at the dyadic level aggregate into differentemerging structural patterns in a network. This un-derstanding of the link between firm behavior andemergent structure is important for developing

more prescriptive formal models and simulationsof how networks emerge and evolve.

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APPENDIX

Construction of Measures

Dyadic Niche Overlap

Niche overlaps between airlines in internationalcity-pair markets were measured using statistics fromthe 1993 to 1997 Traffic by Flight Stage of the Interna-tional Civil Aviation Organization (ICAO), a UnitedNations agency that promotes international coopera-tion in civil aviation among national regulatory bodies.The data, which contained information about the in-ternational segments flown by airlines of participatingcountries (most countries with important airline oper-ations), were used for reconstructing the network ofoperations of each airline. To restrict the number ofpossible city-pair markets under consideration, I con-sidered only traffic between the world’s 200 largestcities by air traffic volume (which accounted for 91.31percent of the international traffic in the sample). Afirm’s route structure includes either direct service orone-stop service to a given market. To avoid feasiblebut impractical one-stop itineraries such as Los Ange-les-London-Tokyo, I only considered one-stop servicerelevant when the ratio of the total distance flown tothe direct distance between origin and destination wasbelow 125 percent. These distances in miles were cal-culated from the latitude and longitude of the cities (inradians), using the great-circle distance formula:

DistanceAB � 3,959.74 � arccos[sin(latA) � sin(latB)

� cos(latA) � cos(latB) � cos(longA � longB)].

Since customers generally consider one-stop servicean inferior substitute for direct service, an airline was

2004 841Gimeno

deemed to be serving a city-pair market effectively (andtherefore considered as an incumbent) if either: (1) itoffered direct service in a city-pair market when otherrivals offered direct or one-stop service or (2) it offeredone-stop service in a city-pair market when other rivalsoffered one-stop service but no direct service.

The above procedures generated data about the activ-ities of the 67 airlines in 12,847 international city-pairmarkets. All city-pair markets with origin and destina-tion in the same country were eliminated. The variableNmt captured the number of incumbents in each market;the average number of incumbent airlines per city-pairmarket was 2.55. To account for heterogeneity in the sizeof markets, I obtained an indicator of the traffic volumein each city-pair market from the gravity model, a popu-lar traffic forecasting rule that states that traffic betweentwo cities is proportional to the total traffic in the originand destination cities and inversely proportional to thedistance between the cities (Doganis, 1991). The variableMktSizemt equals the product of origin and destinationtraffic divided by the great-circle distance in miles be-tween the cities.

Niche overlap between firms i and j at time t wascalculated as the ratio of the scope of overlap between iand j relative to the scope of the focal firm i. The measureranged from 0 to 1, where a value of 1 indicated that thefocal firm overlapped with the rival in all the markets itserved. I calculated scope overlap as the sum over all12,847 markets of the product of two presence dummies(Iimt and Ijmt) that represented firm presence in eachmarket, adjusted by the ratio of the size of the market(MktSizemt) to the incumbent density in the market (Nmt),to account for heterogeneity in market size and marketstructure. Unfortunately, information about actual mar-ket shares of firms in each market was not available. Thedenominator captured the scope of the focal firm in thesame size-adjusted terms.

�ijt �

�m�1

12,847

Iimt � Ijmt � (MktSizemt/Nmt)

�m�1

12,847

Iimt � (MktSizemt/Nmt)

.

Dyadic Complementarity

Complementarity was measured as the ratio of thesize-adjusted sum of international city-pair markets not

currently served by the airlines that could be servedeffectively if firms were to fully integrate their networks,relative to the average size-adjusted market scope of thefirms. To calculate this variable, I considered that a com-bined network could serve city-pairs by either one- ortwo-stop service (since a common aspect of alliancesinvolves serving city-pairs by traveling through the hubsof both partners, such as Barcelona-Frankfurt-Chicago-Cincinnati). I assumed that an alliance served a marketeffectively if the flown distance was not more than 25percent higher than the direct distance and if it did notoffer service with more connections than incumbents did(that is, two-stop travel would not be considered effectivein markets where companies were flying with zero or onestop). I identified those markets that could be served bythe combined networks but were not served by the firmsindependently with a dummy, Cijmt. As for the calcula-tion of niche overlap, I weighted each market by thefactor MktSizemt/Nmt, the ratio of market size to numberof incumbents, to account for differences in opportuni-ties across markets. To scale the magnitude of thecomplementarity, I divided the measure by the average ofthe scope of the potential partners in size-adjusted terms:

Complementarityijt �

�m�1

M

Cijmt � (MktSizemt/Nmt)

� �m�1

M

Iimt � (MktSizemt/Nmt) � �m�1

M

Iimt � (MktSizemt/Nmt)��2

.

Javier Gimeno ([email protected]) is an associ-ate professor of strategy at INSEAD. He earned his Ph.D.in strategic management from Purdue University. Hisresearch interests include competitive strategy, compet-itive dynamics in multimarket contexts, the effect oforganizational incentives and delegation on competitiveinteraction, and entrepreneurship.


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