A Matching Theory of Alliance Formation and Organizational Success: Complementarity and Compatibility
Hitoshi Mitsuhashi Keio University Faculty of Business and Commerce Mita 2-15-45 Minato Tokyo 108-8345 JAPAN Tel. +81-35427-1754 Fax +81-35427-1578 Email:
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Henrich R. Greve INSEAD 1 Ayer Rajah Avenue 138676 Singapore Tel. +65-6799 5259 Fax +65-6799 5499 Email:
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November 2008 We are grateful for comments from Gabriel Benito, Wesley D. Sine, Bala Vissa, and seminar participants at the Norwegian School of Management, University of Tsukuba, and Kyoto University. Research support from the Norwegian Research Council is gratefully acknowledged (Grant number 86469).
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A Matching Theory of Alliance Formation and Organizational Success: Complementarity and Compatibility
Abstract This study advances our understanding of network dynamics by applying matching theory to the formation of interorganizational alliances. We introduce market complementary and resource compatibility as two critical matching criteria in alliance formation, and argue that good matches increase firm performance. Using data from liner shipping, we find effects of matching on alliance formation. But contrary to our expectations, alliances by networked firms, rather than isolate firms, exhibit better match quality, suggesting that networks facilitate matching rather than sacrifice it. We also find evidence that alliances with matched partners improve firm performance and survival chances. (95 words)
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INTRODUCTION Interorganizational networks formed by alliances have been documented in many industries (e.g., Gulati, 1995; Powell, Koput, & Smith-Doerr, 1996; Rowley, Behrens, & Krackhardt, 2000), and have been shown to affect outcomes such as innovation (Hoang & Rothaermel, 2005; Powell et al., 1996) and performance (Anand & Khanna, 2000; Baum, Calabrese, & Silverman, 2000). Doz and Hamel (1998: ix) noted that “for industry giants and ambitious start-ups alike, strategic partnerships have become central to competitive success in fast-changing global markets.” While alliances are important, opportunities to form alliances are unequally distributed across firms (e.g., Ahuja, 2000; Gulati & Gargiulo, 1999; Rosenkopf, Metiu, & George, 2001). Alliance formation is a selective process in which organizational characteristics influence the likelihood of participation and the specific pairings that result (Powell, White, Koput, & Owen-Smith, 2005), so firms meeting certain strategic and social criteria hold advantageous positions to develop alliances. It is therefore critical to understand the criteria that determine their chances of forming alliances. Two such criteria are preexisting ties and resource endowment. First, much research has focused on how organizations find it easier to initiate new collaborations with their current partners or organizations tied to their partners than with organizations lacking such preexisting ties (Gulati, 1999; Gulati & Gargiulo, 1999; Podolny, 1994). The reason is that there is uncertainty about whether a potential partner is willing and able to solve unanticipated problems during the lifetime of an alliance (Parkhe, 1993), which leads to a preference for establishing additional ties with existing partners (e.g., Gulati & Gargiulo, 1999). Preexisting ties transmit information useful for judging the capabilities and intentions of potential partners, and hence the risks of collaborating with them (Williamson, 1981). Second, research has shown that organizations with more resources are more attractive as alliance partners (Ahuja, 2000; Eisenhardt & Schoonhoven, 1996; Li & Atuahene-Gima, 2002; Stuart, 1998). The 3
reason is that alliances are used to obtain resources, so organizations endowed with valuable resources have easier access to collaboration opportunities. In sum, current research suggests a rich-get-richer phenomenon where firms with preexisting alliances or ample resources can form alliances more easily. However, these findings provide limited guidance for managers interested in selecting promising alliance partners and growing their alliance networks. The emphasis on preexisting ties limits our understanding of whether organizations are able to make alliances outside their current set of partners, such as when they need access to resources that their preexisting partners do not hold. We currently know little about how organizations initiate collaborations with strangers (Beckman, Haunschild, & Phillips, 2004; Li, Eden, Hitt, & Ireland, 2008), and it has become common to state that organizational preference for past partners is so strong that managers sacrifice match quality in order to continue working with past partners (Baum, Rowley, Shipilov, & Chuang, 2005; Goerzen, 2007; Gulati & Gargiulo, 1999). In addition, managers pursuing new alliance opportunities typically face resource constraints. For them, knowing that possessing more resources is better is less useful than knowing what kind of resources would increase their chance of forming alliances. We need evidence on how specific organizational resources affect alliance formation, and thus how firms can allocate scarce resources to obtain more alliances and better alliances (Rothaermel & Boeker, 2008). In response to these gaps in our knowledge, we propose and test a matching theory of alliance formation. Matching theory is often applied in investigations of employer-employee matching in economics and sociology (Fujiwara-Greve & Greve, 2000; Hannan, 1988; Logan, 1996; Simon & Warner, 1992). Its fundamental tenet is that a theory of relationships needs to simultaneously consider all parties’ preferences, opportunities, and constraints by using data “on the characteristics or resources that each side values in the other” (Logan, 1996: 117). Matching theory assumes that: a) matches are voluntary and entered when actors estimate the
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benefits to be positive; b) match quality is affected by observable criteria with effects that can be judged by the actors; c) match quality is also affected by unobservable criteria and hence uncertain; d) search costs prevent some optimal matches from occurring; and e) actors use signals of the unobservable criteria whenever these are available. Some testable implications of matching theory are: 1) realized matches have high fit on the observable criteria according to the actors’ criteria but will not necessarily be optimal; 2) actors withdraw from matches at a decreasing rate as they discover mismatches in unobservable criteria; 3) actors withdraw from matches when better alternative matches on observable criteria become available; 4) matches have a stochastic distribution of benefits with a positive mean; and 5) the distribution has lower variance when signals of the unobservable match criteria are available. Because alliances are used to combine heterogeneous resources held by multiple organizations, research on alliance formation and partner selection can benefit from applying matching theory to interorganizational contexts. The distinction between observable and unobservable match criteria is an important insight from matching theory that can be transferred to the alliances and used to further research on observable matching criteria. The standard matching theory concerns matching of preferences and skills in employment, but we develop theory of how match criteria are determined in alliance formation and test predictions on match fit (1) and consequences (4). We identify market position and production resources as the observable criteria used to determine matches, and we specify that firms judge match quality as high when these criteria show complementarity and compatibility, respectively. By applying matching theory to alliance formation, we offer extensions and correctives to prior work. While theoretical and empirical effort has gone into identifying matching of knowledge and routines, these are unobservable criteria that lead researchers into investigation of signals such as status (Podolny, 1994). Conversely, they lead to neglect of observable criteria, which are also fundamental in matching. Matching of production assets, in
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particular, is important for the resource-based view of the firm (Barney, 1991) because it shows how firms create value from assets that are insufficient on their own but valuable when combined with assets from other firms. When a firm’s production assets fail to generate strategic advantages because they can be easily imitated (Reed & DeFillippi, 1990), complex interfirm resource combinations become an attractive approach to obtain unique resource positions. The matching of production assets is also informative for managers wondering what kind of resources they should procure for increasing their chance of forming alliances. Hence, we investigate matching on observable criteria, controlling for the signals of unobservable match quality identified in earlier work (e.g., Baum et al., 2005; Gulati & Gargiulo, 1999). We also analyze the consequences of alliances in order to examine whether the sheer number of alliances or their match quality affect organizational growth, performance, and survival. The latter analysis is needed to test the matching theory prediction of a positive effect of matches against the alternative hypothesis that search costs or unexpected coordination costs cause many suboptimal alliances (Goerzen, 2007; Rowley et al., 2000). We study alliances in the global liner shipping industry, which are made to start new routes through pooling ships and port access. There are two reasons for pooling (Midoro & Pitto, 2000; Stopford, 1997; Ryoo & Thanopoulou, 1999). First, many large customers prefer to transact with shipping firms that have a large menu of routes, even in a single market region, but each route requires so much resources that few shipping firms can obtain a large route network except by having some routes that are shared with others. Second, many shipping firms are stronger in certain regions as a result of long experience, loyal customers, and access to ports, and they find alliances with shipping firms with strengths in complementary regions useful for providing services with a broader network to customers. These two motives for initiating alliances have clear implications for which kinds of alliances will be most valuable to shipping firms. Combining different areas of market strength requires
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complementary route networks. Resource pooling requires ships that can be used in the same routes. If these reasons for initiating network ties are consequential, we should see matching effects on the partner choice even if firms pick partners that they have prior ties with. Indeed, joint modeling of these factors may reveal whether match quality on observable criteria is more important than prior ties, or whether these concerns are balanced. THEORY AND HYPOTHESES An alliance is established when two or more organizations mutually see collaboration as beneficial, so organizational goals and external opportunities jointly determine alliance formation. The logic of matching theory is that organizations form alliances with a mutual fit of resources. For a match to occur, it is not enough that a given organization needs to obtain a certain resource—an organization holding that resource must also need something that the given organization can provide in return. The distribution of organizational characteristics in a population creates the pool of prospective alliance partners, which determines how close to the ideal partner each organization can get. This model allows serendipity in the matching process (Kilduff & Tsai, 2003), but also lets organizational goals and resources affect choices so that realized alliances will match organizational characteristics better than random encounters of organizations would have. The match quality is determined by the availability of partners with the desired characteristics, so matches are expected to be worse when actors look for rare characteristics than for common ones. Hence, matching theory does not assume that matches will be perfect, only as good as availability allows; and likewise the methodology tests observed matches against randomly drawn matches to control for the availability constraint. Some prior work suggests that firms do match resources. In their analysis of firms’ choice between alliance formation and acquisitions by the large U.S. corporations, Wang and Zajac (2007) argued that when two firms operate in the same industry, competition between 7
them causes conflict of interests, resulting in a greater likelihood that the firms choose a hierarchical form of governance (i.e., acquisitions), rather than a market one (i.e., alliances). Rothaermel and Boeker (2008) used a technological measure of similarity and found that alliances occur between firms that cite each other’s patents and have similar patenting propensity. These studies both suggest that matching matters in alliance formation, but the findings are not as specific as one would like. It seems obvious that organizations have more alliances within a given industry than across industries, so such an investigation has less value than one of how organizations find partners within the same industry. Finding that technological similarity increases alliance formation is important for R&D alliances, but does not generalize to alliances for production and service delivery that involve exchange and pooling of observable resources. Thus, evidence for matching is still limited, and there is also a need for theoretical development. Below, we propose that market complementarity and resource compatibility are observable matching criteria in alliance formation; that the role that the matching criteria play in alliance formation differs for already-networked firms and isolate firms; and that matching influences subsequent organizational outcomes of firm growth, survival, and performance. The concepts of complementarity and compatibility are important parts of our extension of matching theory to alliances because they help specify the match quality of potential partners. Complementarity gives match quality through differences—capabilities are complementary if they are different in a way that can be combined in order to create greater value. Examples are R&D and commercialization capabilities, or terminal facilities in two ports that can be connected with a route. Compatibility gives match quality through similarities—the capabilities can be combined to create value because they are similar or they share a standard interface. Examples are when two software development teams use the same methodology, or when ships are exchangeable because they have the same operational
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characteristics. Complementarity and compatibility are independent criteria for assessing the match quality of a specific capability or resource combination, and either or both of them may be relevant depending on the goals of the alliance. Market Complementarity A major use of alliances is to overcome resource acquisition problems that each organization would have difficulty solving individually (Gulati, 1995; Levine & White, 1961; Pfeffer & Nowak, 1976; Rowley, Greve, Rao, Baum, & Shipilov, 2005). Firms seek to develop business opportunities jointly when each has resources that need to be combined in order to realize the opportunity (Gulati & Gargiulo, 1999; Hennart, 1988; Kogut, 1988). A key step in the reasoning is that these resources give competitive advantage when they are not easily appropriable by a firm on its own (Barney, 1991; Reed and DeFillippi, 1990). Scale may be the cause, i.e., the firm cannot afford the necessary amount of resources, but additional motives can also be found. If the resource is only obtainable through the market at high cost or with some delay, then an alliance can produce the desired result at lower cost or higher speed. If firms holding the missing resource are potential entrants to the focal market, the firms can obtain the resource by entering an alliance with one or more of them while co-opting potential competitors (Kogut, 1988). Consistent with this reasoning, previous studies have found that firms with complementary resources are more likely to collaborate (Chung, Singh, & Lee, 2000; Gulati, 1995; Powell et al., 2005; Rowley et al., 2005; Stuart, 1998). An important extension of this argument is that firms also seek alliances when each firm has access to markets not possessed by the other, which we term market complementarity. This is because presence in a market requires buildup of resources that have specific value in that market (Gimeno, 2004). These include scarce tangible resources such as advantageously located properties and local intangible resources such as reputation, customer networks, and 9
knowledge. Such resources give competitive advantage in the focal market to the organization holding the resource and to alliances in which it participates (Barney, 1991; Dierickx & Cool, 1989; Kogut & Zander, 1992; Shan & Hamilton, 1991). For example, shipping firms in alliances can swap access to port terminals that they own, share information about customer demand and preferences in specific markets (e.g., customer shipping schedules) and jointly access social networks (e.g., port authorities and customers). Hence market complementarity is related to resource complementarity because the value of having complementary markets can often be traced back to market-specific resources. Market complementarity is an especially strong incentive for collaboration in industries in which expanded market access increases the quality of services. This effect is seen in firms that operate networks such as transportation or communication systems, because the value of connecting to the network increases as a function of the number of nodes that can be reached (Barnett & Carroll, 1987; Stabell & Fjeldstad, 1998). When firms operate competing networks, those with networks having more nodes offer sufficient added value to win more customers even if they charge the same prices as those with smaller networks. As a result of these network economies (Katz & Shapiro, 1986), growth strategies through alliances with partners having market complementarity are a prominent feature of such industries (e.g., Gimeno, 2004). An effect of market complementarity on alliance formation thus seems to follow from extant theory and evidence on resource complementarity, but it has not been shown empirically. Hence, there is a need to test the following hypothesis: Hypothesis 1 (H1): Organizations are more likely to establish alliances with partners that have complementary markets. Resource Compatibility The concept of compatibility has been most used in economics and technology studies to describe a situation in which an element can function with other elements in a system 10
without deterioration in overall performance (Farrell & Saloner, 1985). Computer hardware products are compatible when they can use the same software; different firms’ nuts and bolts can be used together owing to compatibility; and cell phones with different makers and carriers can communicate because of compatibility (Farrell & Saloner, 1985; Katz & Shapiro, 1985). Product compatibility creates network externalities, standardization of product designs, skill transfers, and greater availability of complementary products (Sheremata, 2004). We extend this idea to propose a role of resource compatibility in production and service delivery. Compatibility has different forms in organizations operating sequential and network technologies (Stabell & Fjeldstad, 1998). In sequential technologies such as assembly production, compatibility occurs when suppliers have resources to manufacture the designs required by the assembler, and these resources are often different from those of the assembler. For example, an assembly plant may not have any molding machines, but requires molded parts from its suppliers. In network technologies, the production system is made to transmit goods, messages, or transactions in any direction that suits the customer needs. In such industries, compatibility is gained through the use of similar resources, so that the cost and output are made homogeneous regardless of which organization performs the task. For example, a route in a shipping network has to have a capacity suited to the demand, which requires the ships to be of similar size, speed, and age even if they are supplied by different alliance partners. In this study, we focus on this latter type of resource compatibility. Compatibility has four important consequences. First, compatibility gives customers products and services of consistent quality from any of the alliance members. Second, the jointly produced service is as efficient as a service produced by a single firm, and thus it is less vulnerable to competition from a single-firm entrant to the market. Third, the pooling of compatible assets in an alliance can increase the production capacity sufficiently to give scale advantages. Fourth, it is easier to distribute the benefits of the collaborative activity because
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the provision of compatible resources simplifies the task of equalizing inducements and contributions, which is an important task in collaborative relations (March & Simon, 1958; Williamson, 1981). For example, a firm collaborating with another firm that has substantially older ships could experience problems with older ships having more frequent service disruptions and lower efficiency, and cost sharing between new ships with high capital costs and low operating costs versus older ships with low capital costs and high operating costs would be complex as well. Conversely, collaborations with compatible resources would be protected from stresses originating from customer demands, competitive attacks, scale effects, and internal conflict, and thus gain greater stability (Gouldner, 1960). In order to pursue these benefits, firms prefer alliances with other firms with compatible resources. These consequences highlight important differences between resource compatibility and social similarity or homophily. Homophily theory suggests that actors collaborate with each other when having higher similarity on socially salient attributes (Wholey & Huonker, 1993; Powell et al., 2005). Podolny (1994) demonstrated that investment banks having similar social characteristics such as status have greater likelihood of forming syndicates with each other. Like resource compatibility, homophily concerns organizational similarity. Indeed, previous empirical studies have obscured the difference between them because variables indicating resources have been used to proxy social attributes (Chung et al., 2000; Wholey & Huonker, 1993). However, the concepts are different because homophily promotes trust through the “tendency for similar actors to be drawn to one another” (Lincoln & McBride, 1985: 4), whereas compatibility increases the efficacy of combining firms’ resources for achieving their strategic goals. Trust is not equally important for all types of alliances, and is more important when firms exchange tacit knowledge and develop long-term reciprocal relations adaptable to environmental contingencies (e.g., Larson, 1992). Resource compatibility, on the other hand, is needed when firms make alliances to combine resources to satisfy current customer
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demands. Hence we test the following hypothesis: Hypothesis 2 (H2): Organizations are more likely to establish alliances with partners that have compatible resources. Networked versus Isolate Firms An alliance between two firms establishes a network tie between them, while alliances between multiple firms establish a network tie between each pair of the firms. The network ties established by multiple alliances become an alliance network. Alliance networks contain the already-networked firms that have entered into alliances, but an industry will also contain isolate firms that have no alliances. We predict that the effects of complementarity and compatibility differ for these two groups. Firms that are already embedded in alliance networks leverage preexisting networks when searching for prospective partners and form alliances with others with which they have direct or indirect previous ties (Gulati & Gargiulo, 1999). This local search sacrifices match quality because managers “consider first those potential exchange partners about whom they have the greatest knowledge and then choose the best partner from this restricted set” (Podolny, 1994: 459). Work on alliance performance suggests that local search is costly (Goerzen; 2007; Uzzi, 1996), but did not test whether the cost is a result of sacrificing match quality. Conversely, isolate firms search broadly because they do not have a potential set of collaborators close at hand, and thus they view all other organizations as equally uncertain. Already-networked firms will instead distinguish between their current alliance partners, which they view as more certain, and all other firms. We therefore expect that the effect of matching based on observed criteria is greater for search by isolate firms. Note that this argument suggests a reason why alliances initiated with existing partners could have a high match quality: if the original choice of partners was determined by match quality, then the match quality may remain high even when the partners make a second 13
alliance. Hence, in order for a tradeoff between match quality and prior ties to exist, it is necessary that the match quality of existing partners changes over time as a result of changes in their resources or in the availability of alternative partners. That is, market positions and resource combinations that the firms in the alliance possess may change, and those held by other firms that would be alternative matches in a population may also change, causing the same pair of firms to be more poorly matched than they were at the outset. Thus, the prediction of higher match quality of isolates assumes dynamic markets and resources. It also assumes that the matching is done on easily observable resources. In search for partners in alliances in which tacit knowledge and unobservable resources are exchanged (e.g., research alliances), existing ties may instead improve matching because assessment of prospective partners requires private information circulating only through network ties (Larson, 1992; Uzzi, 1996). If the assumptions of dynamism and observability hold, it follows that: Hypothesis 3 (H3): The effects of market complementarity and resource compatibility on alliance formation are greater for isolate firms than already-networked firms. Alliance Consequences Although organizational performance is a consequence of a wide range of internal and external factors, one research tradition examines the impact of alliances. Stuart (2000) found that firms perform better when allying with large and innovative alliance partners. Baum et al. (2000) demonstrated that firms perform better when alliances give them access to diverse resources with minimal redundancy. Goerzen (2007) and Rowley et al. (2000) found harmful effects of repeated ties and densely-interconnected alliance networks on firm performance in uncertain technological environments. Zaheer and Bell (2005) found that firms enhance their performance by bridging structural holes in innovation networks. Thus, among the numerous antecedents of organizational success, research has consistently showed that alliances matter. So far, no research has empirically examined the impact of matching in alliances on 14
objective measures of organizational outcomes. Sarkar, Echambadi, Cavusgil, and Aulakh (2001) found a positive impact of resource complementarity on managers’ subjective assessments of collaborative project performance in alliances, which is an encouraging finding that should stimulate further inquiries. It is also necessary to show that the effects are generalizable to alliances of production and service delivery, that they hold with objective measures, and that the alliance-level effects aggregate up to give higher firm performance. Testing effects on performance is important because a comparison of the findings on alliance formation with those on organizational outcomes helps us understand whether the criteria managers use in alliance formation reflect actual drivers of performance. Because of the complexity of predicting alliance consequences and constraints that managers face in finding appropriate partners with high levels of match on multiple criteria, managers may use incorrect matching criteria, leading them to form alliances that reduce firm performance. Also, while previous studies have demonstrated a positive association between firm alliance counts and performance (e.g., Baum et al., 2000; Powell et al., 1996; Rowley et al., 2000), they have not tested whether this relationship is affected by the complementarity and compatibility of alliance partners. The baseline prediction from matching theory is that firms enter alliances when they estimate that the advantages from market complementarity and resource compatibility exceed the added governance and coordination costs. When matching is based only on observable characteristics, these judgments should on average be correct, but the uncertainty introduced by unobservable characteristics can lead to some alliances that incur losses. In addition, the reasoning from hypotheses 1 and 2 suggests that the matching of alliances matters. Alliances with greater market complementarity or resource compatibility contribute more to the organizational performance than other alliances. These predictions can be evaluated through three outcome variables: firm growth, survival, and performance. Firm growth through
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investment in production asset is a function of managerial choice and financial constraints, and can reveal the intention behind alliances. Firm survival and financial performance are functions of market success and costs, and can reveal the effectiveness of alliances. We discuss these in turn. First, alliances enable firms to serve the same production or service delivery capacity with fewer resources, or to obtain greater capacity than they would solely be able to serve with their own resources. If the customers prefer more highly interconnected service networks such as communication and transportation, firms with more alliances and enhanced network externalities can increase the demand and attract more customers. However, the market complementarity of alliances determines whether firms use the added demand for organizational growth through investment in resources. Because alliances with partners having high market complementarity enable firms to expand their service networks through connecting markets, such alliances decrease organizational need for obtaining additional resources to develop and connect the networks. Conversely, firms allying with partners having lower market complementarity have an incentive to expand their networks further, extend their market reach, and hence pursue more growth. Such firms need to obtain extra resources because mismatched alliances do not help them save resources. It follows that: Hypothesis 4 (H4): The growth of organizations is lower when entering alliances with partners that have low market complementarity. Having examined firm growth, we can turn to performance and survival. Here the argument is simple because the reasoning for hypotheses 1 and 2 implies that alliances are a low-investment approach to growing networks, and high market complementarity and resource compatibility result in better matched alliances. If managers on average make the right choice on whether to form an alliance as opposed to creating a single-operator route or doing nothing, it follows that alliances increase their performance, and this effect is stronger 16
for alliances with greater market complementarity and resource compatibility. The greater performance, in turn, improves the survival chances of firms with alliances, especially if they have greater market complementarity and resource compatibility.1 Thus, under this assumption of (possibly bounded) rationality, we predict: Hypothesis 5 (H5): The failure rates of organizations are lower when entering alliances with partners that have high market complementarity and resource compatibility. Hypothesis 6 (H6): The performance of organizations is higher when entering alliances with partners that have high market complementarity and resource compatibility. METHODOLOGY Liner Shipping We examine alliance formation in the global liner shipping industry. Modern liner shipping mainly uses ships specialized in transporting containers, which are packing crates with standardized interfaces that substantially reduce costs of loading and moving cargo among ships, trains, and trucks. In liner shipping, frequent and reliable routes are a major selling point, so the number and quality of available ships are important for new route entry. However, container ships for long-distance routes are very expensive and require access to dedicated port facilities, making alliances to pool ships and port access common. Regulatory changes in the mid to late 1980s were an important spur toward interfirm alliances. Until then, shipping firms had made price fixing agreements by establishing conferences. The conferences sought to coordinate the pricing of all firms serving a given trade lane, which is a deep-sea route connecting two regions. A trade lane has multiple competing routes because a route is defined by the stops made along the way. For example, North Asia to North America is a trade lane served by multiple routes that compete based on 1
Because some work has shown that performance and survival effects are not exactly parallel (Gimeno, Folta, Cooper, & Woo, 1997), we could make separate predictions for these outcomes, but here we make the same prediction in order to maintain parsimony.
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the stopping points, service frequency, and pricing of the route operators. The 1984 US Merchant Shipping Act placed strict limitations on the activities of the conferences, and it was followed by increased activism against price fixing from the European Commission. Along with the globalization of the world economy, these regulatory changes weakened the ability of the conference systems to enforce price agreements, leading to greater competition. In response, they made alliances to generate economies of scale and expand coverage of service regions. Because alliances are made to combine ships and other resources in a specific route, they have a narrower scope and fewer members than conferences. Attempts were made to form multi-route alliance networks during this period, but they proved unstable (Midoro & Pitto, 2000; Ryoo & Thanopoulou, 1999). However, firms having competitive and operational interdependencies that are too complex to resolve across multiple routes may still be able to cooperate on a single route (Song & Panayides, 2002), which has led to cooperation in single routes among firms that operate competing routes elsewhere. Firms form alliances with other firms possessing compatible resources in order to provide products and services of consistent quality and to facilitate the division of costs and benefits. The key compatibility characteristics of ships are size, speed, and age. Pooling of ships for a joint service results in a shipping route where (say) a weekly scheduled departure uses ships from alliance member firms. If these ships have unequal size, customers will plan their regular shipments with respect to the smallest available ship, and the firms are forced to try to fill the larger ships with incidental traffic. This is likely to cause under-utilization of the larger ships. Ship size also influences the choice of routes due to geographical constraints such as the width and depth of the Panama Canal. Developing routes with similar-size ships is thus easier. Similarly, ships in a route move in lock step, so the slowest ship determines the speed and service frequencies. Fast ships are more expensive to operate than slow ones even when going at low speed because of the weight and size of the engine, so shipping firms also
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match the nominal ship speed on the same route. Ship age matters because different age ships have different reliability and maintenance schedules, which complicates the provision of backup ships for scheduled or unscheduled maintenance of ships serving the route. It is easy to assess the number, types, size, and speed of vessels that another firm can provide, so managers are likely to use this information when searching for partners. When purchasing container ships, shipping firms plan the assignment of the ships to trade lanes because trade volume and journey length differ by lane. This causes some interdependence between market complementarity and resource compatibility, but firms frequently reassign their ships to different trade lanes according to changes in market conditions. To better understand modern shipping alliances and find suitable measures of complementarity and compatibility, we conducted semi-structured interviews with six managers of two Japanese operators and two Norwegian operators, and one manager of a Japanese container terminal operator. Our informants noted that it was possible to gain access to the routes of other firms through slot purchases or slot swaps, which mean that an operator purchases or trades a certain amount of freight capacity on routes served by another operator. Although the terms of contract are subject to negotiation, they are generally commitments to pay for (or swap) a certain capacity even if it is not used. The route is still operated by the ships of the main operator, who maintains full authority over their use, so they do not involve resource pooling in the way that alliances do. Vessel sharing agreements, however, are joint route operations in which operators pool vessels and have shared authority over the vessel. One of our informants pointed out the benefits of alliances: Alliances make it possible to create more routes than our own resources allow. We may have enough ships for three routes in Trans-Pacific, for example, but with alliance partners exchanging the spaces we now can offer nine routes instead to our customers, bringing us more customers. In fact, many large volume customers such as auto parts suppliers will not even invite us to their bidding processes if we only have one or two Trans-Pacific loops, as they want as much service coverage as possible. Large customers prefer to transact with a few shipping firms with highly interconnected route 19
networks and prefer term contacts to spot contracts because too many logistics vendors and contracts inflate the cost of transactions and operations. Alliances allow shipping firms to build networks of sufficient size to participate in bids for such term contacts. However, operators also incur costs of alliances. Another manager commented that: The de-merit of alliances is that they compromise our products. Our historical strength through uniqueness becomes watered out because it is now available to everyone in the alliances, and we may also have to adjust it in ways that fit our alliance partners better than us. … The decision-making process is also slower, and involves decision rules such as majority rule or agreement by all. … Also, managing alliances can be complex. The problems associated with alliances suggest that entering new markets as a sole operator is still an attractive option, and slot purchases or swaps are useful when operators want to gain access to a route without forming a collaborative relation. In the following we define alliances as vessel sharing agreements in order to maintain the theoretical focus on collaborations in which the partners pool resources and have shared authority over their use. Our informants also commented on the criteria used for finding partners. One of them depicted his search as systematic and rational: he selects markets to enter, formulates plans of transportation capacity, ports of call, and service frequencies in routes, decides minimum vessel specifications, finds appropriate vessels of their own that meet the specifications, and makes a list of prospective partners by searching for firms that own vessels that also meet the specification and can allocate them to routes that he proposes. His firm routinely collects information about other firms’ strategies, entry, alliances, transportation capacities, and vessel types through trade journals, newspapers, and databases. While this manager denied that prior ties play a critical role in this search process, another manager said that current partners receive special attention in search processes. Our informants also stressed the importance of market complementarity in considering alliance partners. A German shipping firm, for instance, does not own onshore facilities on the East coast of the United States, so it has alliances with other firms with strength in this region. Similarly, many firms have Japanese 20
partners for routes with Japanese destinations in order to gain easier port access. Moreover, we confirmed the validity of the compatibility operationalization by learning that the minimum vessel specifications that the two Japanese shipping firms use in search processes cover ship size, speed, and age. One manager, however, told us that he does not directly check ship age, but infers it from the ship designs. While our informants noted the importance of resource matching in alliance formation, they also said that matching of “culture” and “philosophy” also matters. They occasionally collect information about prospective partners’ culture from customers who have experience with them. In particular, they are interested in prospective partners’ priority in keeping transportation schedules and their way of overcoming shipping delays (e.g., whether the partners skip ports to catch up or accelerate the vessel speed). By using their customers as a source of information, they can gain some insights on firms that they have not yet had any alliances with. We are unable to measure such cultural differences directly, but firms that are concerned with timeliness generally maintain a younger fleet with newer navigation and engine technologies. From a matching theory point of view, culture may be an unobservable criterion with customer reputation and ship age as signals. Overall our model is consistent with our field observations, but it is also limited due to our focus on the matching of observable resources. Sample Our sample consisted of 602 new alliances (559 after missing-data deletions) made by 137 shipping line operators originating from 37 nations. The data extend from 1988 through 2005, but we lose the four first years of observations because one of the control variables captures organizational momentum in alliance formation which we measure by the number of alliances entered during the three past years. Hence, the analysis contains 13 years of observations. The source of alliance data is the International Transportation Handbook 21
published annually by Ocean Commerce Ltd., a Japanese publisher specializing in the liner shipping industry. It includes all line operators having cross-national routes connected to at least one port in Japan and those partnering with them. The data include operators that do not have routes connected to Japan, but ally with operators having routes connected to Japan. Hence the data is a snowball sample design with all operators serving a Japanese port as the seed and all their contacts as the snowball. The data are highly reliable, as only information original from line operators is compiled. Our data include all new alliances in this period, as identified through comparison of successive volumes of the handbook. We constructed our network of preexisting alliances by coding all routes operated jointly by multiple operators. We regard joint operation of a route as a network tie between the operators. Thus, the original list of operators of each route becomes an affiliation network (two-mode network) where one or multiple operators are affiliated with a route. This is transformed into a regular one-mode network by letting operators have a network tie with strength equal to the number of routes that they jointly operate. The one-mode network is used to calculate the network measures with Ucinet 6 (Borgatti, Everett, & Freeman, 2002). We update the network data every year, as we do for all the other covariates. Data Structure and Model Our data consist of the new alliances that were initiated during the study period. In addition, we know which alliances could have taken place—but did not—because we have data on all firms that were active in liner shipping in the same year. In order to investigate the matching rules used by the firms we need to compare the matches that actually happened with the matches that did not happen. To accomplish this, we create a data set of randomly sampled alternative alliances with the same number of members that did not happen but would have been possible given the firms that were present in that year. On average, there were 146 firms active in a given year, which means that there are 10,585 potential dyadic alliances. Hence a 22
given dyadic alliance has less than a one-hundredth of a percent chance of occurring through a random draw. We set an indicator variable equal to unity for the actual matches and to zero for the alternative alliances and used a probit model to analyze how the actual and the alternative alliances differ. The approach of comparing actual matches with alternative ones (i.e., unrealized ones) is the same as that used in prior work on alliances (Gulati & Gargiulo, 1999; Powell et al., 2005; Sorenson & Stuart, 2001), but prior analyses have differed somewhat in the details. Some analyses have taken all possible alternative matches (e.g., Gulati & Gargiulo, 1999) or a large probability sample (Powell et al., 2005). This approach has been criticized for generating too many observations with the same firm in the data, which makes the standard errors difficult to interpret (Sorenson & Stuart, 2001), and this issue is particularly serious when alliances involve more than two firms. In our data, the most common alliance size is dyads (329 alliances), but there are also alliances with three (148), four (50), or more members (32). With 146 members to choose from, the number of unrealized alliances becomes very large. On the other hand, it may be too conservative to use just one alternative for each realized match, because each alternative match provides information about criteria combinations that could occur by random but do not result in alliances. Hence we sample a fixed number of alternatives for each actual alliance. We estimate robust standard errors clustered by firm in order to reduce the influence of repetitions (White, 1980). We experimented with different numbers of sampled alliances and found the clustered standard errors (but not unclustered ones) to be stable across sample sizes. However, as a precaution we still use only four sampled alliances for each realized one. For each alliance, we compare the characteristics of each focal firm with the average characteristic of other firms in the alliance. For a dyad, this is just a comparison of the firm and its partner. For alliances with more members, this measures how well each firm fits with
23
the average characteristics of other firms. For instance, suppose that firm X forms an alliance with Y and Z. We add four three-member alliances randomly drawn from the population (say, AGH; TUW; CST; EIK) to the alternatives. The alliance formation variable is coded 1 for XYZ and 0 for AGH, TUW, CST, and EIK. For each of these realized and unrealized combinations, we assess the match quality by comparing each member firm’s characteristics with the average characteristics of the other two. Again, this is done for caution, because breaking multi-member alliances into dyads would result in many observations from just one alliance. However, we do generate one firm-versus-others observation for each firm in the alliance, so there is some overrepresentation of multi-member alliances in the data. This is not harmful because these alliances are less frequent to begin with. We control for observations lost as a result of missing data on ships. For some of the firms we were unable to find matching ship data, which could be either due to leasing of ships or to ownership through an entity with a different name. Although the database that we use traces ship ownership through group firms or special-purpose entities, we cannot be sure that these data are complete. The likelihood of missing data may be related to the outcome of interest, and hence we control for this by estimating a selectivity-corrected probit model with the joint (maximum likelihood) method (Heckman, 1979). It is possible to use a conditional logit estimator to take into account that the data has been constructed such that each realized alliance has a set of unrealized control alliances. This estimator compares within each case-control set, which is an advantage, but it is less suitable for capturing the selectivity effects that result from missing data. We thus prefer the selectivity-corrected probit, but found that our conclusions were preserved in a supplementary analysis with a conditional logit and a two-step selectivity correction (Lee, 1983). For regressions predicting effects on organizational outcomes, we use different models depending on the outcome. We specify firm size as owned shipping capacity in container units
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(TEU), and use linear regression with random effects and autocorrelated disturbances because the capacity in a given year is highly correlated with that of the previous year. The analysis of firm exit measures whether a firm is no longer seen in any liner market and thus has failed, been acquired, or has exited liner shipping. There are 40 exits in our sample, of which 17 terminated shipping operations in the liner shipping markets we study but continued other shipping services. Seven firms were acquired, and 16 firms failed. We do not count mergers as exits. We apply discrete event-history analysis using the conditional log-log model (Allison, 1982). The analysis of performance uses return on assets (ROA) and is a linear regression with random effects. All analyses include firms that do not participate in any alliance as well as firms that do, and use a selectivity correction for missing data (Lee, 1983). There is missing data on ROA because many firms have a legal form or place of incorporation that allows them to not publicly disclose accounting statements. The selectivity control should prevent a bias, but the data loss is sufficiently high that the results should be interpreted with caution. Variables Hypothesis-testing variables. To test hypothesis 1, we use a variable based on the International Transportation Handbook classification of routes into 16 regions.2 We set the complementarity to zero for firms that do not meet in at least one market because their route networks have to connect in order to obtain complementarity benefits. For firms that meet, we define complementarity as the (count of the) complement of the markets the firms are represented in divided by the union of the markets the firms are in. The formula is: Complementarity =
A ∪ B − ( A ∩ B) A∪ B
Thus, if firm i is represented in three markets (the set A) including two markets that firm j is also represented in, and firm j is represented in five markets (the set B) including two markets 2
The regions are Africa, Asia, Arabia, Australia, Caribbean, Central America, China, Europe, India, Korea, Meditteranean, North America, South America, Russia, Transatlantic, and Round-the-World.
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that firm i is also represented in, then the union of their market presence is 3+5-2=6 markets. The complement is 3-2 + 5-2 = 4 markets. The complementarity is then 4/6=0.667. We calculate the average complementarity of the firm and its alliance partners. For parsimony we do not attempt to weight the complementarity measure by the market size or the focal firm commitment to the market. To test hypothesis 2 on resource compatibility, we calculate the average size, speed, and age of ships in the fleets of each firm and take the absolute difference between the focal firm and that of the average of its alliance partners for each of these characteristics. Greater difference indicates worse match. We calculate these variables using the data of the container ships owned by each firm, but in a preliminary analysis we verified that the findings were similar when using data of both container and non-container ships. Using only container ships is more appropriate because shipping firms pool container ships only for the joint operations of liner routes. It is important to note that the relation of these measures to observed matches, if any is found, is not a trivial result of shipping firms rejecting any combinations of ships that do not fit. First, there are routes combining ships with different characteristics, both when the routes are operated by one shipping firm and when the routes are operated by multiple. More important, however, is the intrafirm variation in ship type and the lag structure of the data. Whereas we use the previous-year fleet average to assess match fit, shipping firms that establish new route through alliances may acquire new ships to serve the route, and they may have or acquire ships that differ from their fleet average. Shipping firms operate a variety of ship types, but they seek to control this variety so that they can easily reassign ships if needed (for example, to cover for a ship undergoing maintenance or repairs). Hence these match characteristics will have the hypothesized effects if shipping firms optimize the fleet structure both within a given route and overall.
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Control variables. As control variables characterizing the focal firm we enter the firm age and two measures of size: market size (the number of routes it operates) and asset size (the number of ships it owns). We enter levels measures of the ship characteristics used in the difference measures in order to adjudicate between resource levels and resource matching effects. This is important because of the hypothesis that higher resource levels facilitate alliance formation (e.g. Eisenhardt & Schoonhoven, 1996). Hence we measure the average ship size, speed, and age. To control for firm positions in the alliance network, we enter three variables. First, the logarithm of the firm’s number of alliance ties controls for the centrality effect on alliance formation (Powell et al., 1996). The second variable is a firm’s mean tie strength with each partner, expressed as an average of the number of routes that the firm jointly operated with partners in the alliances. This variable equals zero if they have no prior ties. The third is the proportion of firms in the alliance with a prior tie (in dyads this is just an indicator for whether they have a prior tie). These variables all capture the greater access to alliance opportunities gained by firms that are central in the alliance network. Although the second and third variables are similar, earlier work has found that tie strength explains alliance formation in dyads even when tie presence is controlled for (Gulati & Gargiulo, 1999). In order to capture the organizational tendency to repeat recent actions, we create a variable that equals the number of routes entered in the past three years. Finally, we enter a matching variable that we do not give a theoretical interpretation. The fleet size difference is measured by the difference in the number of vessels that firms possess, which can indicate matching along (size-related) status, but may also be interpretable as a measure of economic power. The dual interpretations make this variable unsuitable for hypothesis testing, but give two reasons to control for it. All independent variables vary annually and are lagged by one year. The analyses of alliance outcomes use a subset of the control variables entered in the
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matching analysis, as we found that the firm resource variables did not have measurable effects, nor did the variables describing the position in the alliance network. Because the analyses have few observations, particularly that of return on assets, inclusion of many extraneous variables would make type I errors highly likely. Because the analyses of alliance consequences have firm-years as the unit of analysis, the alliance-level variables are aggregated through taking the mean over all alliances. This aggregation step is necessary, but illustrates a potential dilemma in measuring effects of alliance matching on firm performance. It is possible that one poorly matched alliance will have effects that are more adverse than the simple averaging procedure assumes, but we are left with the simple average because of its greater parsimony and the difficulty of constructing tests of alternative measures that will not have high risk of type I or II errors. RESULTS Table 1 summarizes the descriptive statistics and correlations of variables used in the regression analyses. The two firm size measures are correlated with each other and with measures of network centrality, constraint, and alliance activity. The alliance measures are in turn correlated with each other. However, the overall level of multicollinearity is low, as seen through a maximum variance inflation factor (VIF) in the final model of 4.62, well below the threshold of 10 (Belsley, Kuh, & Welsch, 1980). ===== Insert Tables 1 and 2 Here ===== Alliance Formation Table 2 shows the results of the matching analysis. We begin with the analysis of all firms in the data, and proceed to analyze isolate firms and networked firms separately. Because the full model 2 preserves the findings of the reduced model 1, we interpret the full model only. The coefficient of market complementarity is positive and significant (p < .01), 28
supporting hypothesis 1 that alliances are made between firms with high market complementarity. Hypothesis 2 predicts the effects of resource compatibility. The coefficients of ship speed difference and ship age difference are negative and significant (p < .01), showing that a firm is less likely to ally with other firms when the average speed or age of their ships is different. Of the numerous alternative prospective alliance partners in the population of the global liner shipping industry, a firm is more likely to be chosen by the focal firm when the physical assets that they possess are compatible. However, the coefficient of log ship size difference is positive and significant (p < .01), suggesting that two or more firms are more likely to establish alliances when their average ship sizes are different. This surprising finding called for additional investigation, which revealed the following pattern. Although the alliances occur nearly exclusively on cross-ocean routes, which use large ships, some firms also have feeder routes with small ships. When small firms enter alliances to operate cross-ocean routes with larger firms, they often have a feeder network in one of the route nodes. This market complementarity is matched by a size difference in the ships of the allying firms. The results in model 4 for already-networked firms that have entered one or more alliances are consistent with those of model 2. Two of the variables representing resource compatibility (ship speed difference and ship age difference) are still significant in the hypothesized direction, whereas the last compatibility variable, ship size difference, is significant in the direction opposite to the hypothesis, as before. Market complementarity is significantly related to the partner choices in the hypothesized direction. The results for the networked firms thus mirror the results from the entire population. For isolates, variables measuring network position have equal values for all firms, so they are dropped from the analysis. Isolates are firms that have not entered any shipping alliance. Two of the resource compatibility variables, ship speed difference and ship age
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difference, are not significant, while the third compatibility variable, ship size difference, is significant in the direction opposite to the hypothesis. The coefficient magnitude of ship size difference is significantly greater in model 6 than in model 4 (p < .10), showing its greater role in the search processes for isolates. As we predicted in hypothesis 1 and found in models 2 and 4, results for market complementarity are reproduced when we use only isolates for prediction in model 6. Isolated firms have greater likelihood of forming alliances with firms offering complementary market access. The magnitude of this coefficient is significantly greater for isolate firms than for networked firms (p < .05), suggesting that isolate firms are more dependent on market complementarity when seeking to enter alliances than networked firms are. The results for hypothesis 3 are thus mixed, as isolates have better matches on market complementarity but worse on resource compatibility. The findings indicate that preexisting networks facilitate matching rather than substitute for it. The effects of resource matching were stronger for networked firms than for isolates, suggesting that social networks may be leveraged to optimize the matching between allying firms. Isolates appeared to have disadvantages in collecting information about the resource distribution in the population or to have difficulty obtaining their desired matches with respect to ship characteristics, though they were able to match with firms having complementary market access. To see how the ship characteristics of isolates affected their matching, it is important to note that the main resource predictor of matching success for isolates was whether they had ships of sufficient size to get the attention of other firms, as reflected in the positive and significant coefficient for ship size. Although results supportive of matching theory were seen in the pooled sample as well, the results from the disaggregated populations suggest that matching is more easily accomplished by firms already embedded in networks. The effect sizes were large. In probit models, the dependent variable is a probability,
30
so it is between zero and one, and the effect of a covariate is nonlinear and dependent on the level of the other variables. For illustrative purposes we calculated the standardized marginal effects from a base probability of 0.2, which we obtained by normalizing the variables, re-estimating the model, and using the mfx command in Stata. The marginal effect of market complementarity is 0.148, so a one standard deviation change of market complementarity increases the probability by 0.148 (75% increase). The marginal effects of the difference in ship size, speed, and age are 0.201, -0.057, and -0.041, respectively. All variables have substantial effects, and naturally the negative effects are smaller when the probability starts out at 0.2. The controls include variables describing prior ties such as tie proportion and tie strength, which gave results in line with prior work. The proportion of alliance members with preexisting ties had a positive and significant effect on alliance formation, and so did the mean tie strength, a variable capturing the number of routes that the focal firm shares with each alliance partner. Several variables on firm resources were incorporated into the models as controls. Among them, the ship speed has a positive and significant effect in model 1 in which we use control variables only, but is not significant at the 5% level in other models. Log ship size and ship age are positive and significant (p < .05) in model 6 in Table 2 in which we use isolates only, but both of them are insignificant in model 4 in which we use networked firms. Matching Consequences Table 3 shows the results of the analysis of firm growth, survival, and performance. The model of firm growth shows positive and significant effects of alliances, but also for single-operated routes. Thus, any new route will be associated with firm growth, but the size of coefficients of alliance routes is larger, suggesting that firms have higher growth when entering alliances. Market complementarity has a negative effect that is marginally significant, which is consistent with hypothesis 4. The negative and marginally significant effect of size 31
difference is also consistent with market complementarity. As noted above, ship size differences occur when firms operating feeder routes make alliances with firms operating routes in the major trade lanes. No resource compatibility coefficients are fully significant for firm growth (we had no hypothesis for these variables). ===== Insert Table 3 Here ===== Next, the analyses of failure and ROA demonstrate no effects of the number of alliance routes, so the effects of sheer alliance count predicted in hypotheses 5 and 6 lack support. Similarly, there is no effect of the number of single-operated routes. Market complementarity has coefficient estimates that are consistent with hypotheses 5 and 6, but they are not significant. The difference in ship age with alliance partners increases firm failure, supporting hypothesis 5, but there are no effects of the other resource compatibility variables on firm failure. The difference of ship age with alliance partners also reduces performance (ROA), in support of hypothesis 6. The ship size difference reduces performance (marginally significant), but ship speed difference increases performance. The most robust finding is clearly the adverse effect of making alliances with firms that have ships of different age, suggesting possible problems when firms make alliances with partners that have different capability to operate reliably and possibly also different policies on service quality. DISCUSSION AND CONCLUSION We have sought to advance a view of alliance formation as a matching process guided by market complementarity and resource compatibility. This view is useful because the purpose of alliances is to assemble dispersed resources and capabilities, and hence a primary concern in alliance formation is the match quality. Because firms have a choice of how to obtain resources, the insight that resource matching occurs in alliances is not a trivial one: it implies that the availability of promising partners determines both partner selection and, more fundamentally, the decision of whether to use an alliance in the first place. This point has not 32
received sufficient attention in treatments that assume the necessity of an alliance and proceed to analyze how prior ties influence partner selection. Our theory has borrowed insights from matching theory, but also developed it through the introduction of complementarity and compatibility, and hence we contribute to work on alliance formation and network dynamics by presenting arguments on the effect of matching on organizational behaviors and outcomes. We found that market complementarity had a strong effect on the alliances of networked firms and isolates, and especially the latter. In addition, our analysis gave support to resource compatibility through the speed and age of the ships. In our subgroup analysis, we found that these two criteria account for the alliance formation of already-networked firms, but not that of firms that entered their first alliance. This finding demonstrates how social networks facilitate matching processes in alliance formation, contrary to the usual interpretation of prior ties as restrictions of firm search (Baum et al., 2005; Goerzen, 2007; Gulati & Gargiulo, 1999). We also found effects of prior ties, which may capture the quality of existing matches. One of our informants in the fieldwork commented that because there are few firms in this industry, firms may have already formed alliances with partners that have compatible resources. Indeed, the coefficients of alliances in past three years in models 1 through 4 in Table 2 are positive and highly significant, suggesting that firms engage in repeated ties because of matched resources that repeated partners possess. When resources that each of the firms in a population possesses do not change rapidly, it is likely that partners with optimal resources for new alliances are the same as those who possessed optimal resources for the previous alliance. The analysis also showed that firms having different sizes of ships tend to collaborate, probably due to the role of alliances in connecting firms with cross-ocean and feeder routes, which is also a form of market complementarity. Although we did not anticipate this
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relationship between market presence and resources, the findings are consistent with matching behaviors when criteria are interdependent and one criterion (market complementarity) has precedence over the other (ship size). Our findings on alliance consequences show that ship age is a key matching feature: firms had lower performance and greater risk of failure if they formed alliances that were poorly matched on ship age. It is interesting to compare this finding with the analysis of matching criteria, because ship age was actually the criterion with the weakest effect on actual matches. Likewise, a comparison of size and speed matching raises questions about the efficacy of the criteria in use because alliances with unequal ship sizes were frequent but apparently harmful, whereas unequal ship speeds were uncommon but apparently beneficial. These findings imply that managers have erroneous cause-effect relations about matching criteria and consequences due to bounded rationality or limited opportunities to find better matches. We also found that alliances with low complementarity lead to firm growth, as predicted. The use of matching theory distinguishes this study from other work that has examined similar concepts. For example, Wang and Zajac (2007) used business similarity and complementarity as predictors of firms’ choice between alliances versus acquisitions. Their reasoning for complementarity was similar to ours, but in other respects their study differs. First, they focus on business similarity, whereas we consider the use of similar resources to increase compatibility in alliances. Second, their rationale for a business similarity effect was based on the conflicts of interests that competition causes, whereas we argue that complementarity and compatibility increase the instrumental value of alliances. Third, their measure of business similarity was shared industry, whereas our compatibility measures capture detailed specifications of resources. Of these differences, the most important is theoretical. They predicted a weaker effect of similarity on alliances than on acquisition.
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Matching theory does not predict an effect of business similarity per se, but instead distinguishes between resource compatibility (leading to alliances) and other kinds of similarity (no prediction). Businesses can be similar in ways that either generate compatibility or do not, and we suspect that an unobserved phenomenon in Wang and Zajac (2007) is that the firms picked partners in the same industry that had the greatest compatibility and complementarity for the pursuit of scale and network externalities. They did not collect data that allowed tests of this prediction. Likewise, Rothaermel and Boeker (2008) investigated whether old-technology (pharmaceutical) firms established alliances with new-technology (biotech) firms with similar or complementary capabilities. Again, compatibility was not discussed, and instead similarity was proposed because the focal industry is bifurcated into groups of research-frontier and generic-drug firms. The study gave good results on the cruder measures of complementarity in downstream and upstream focus and similarity in overall patenting propensity, but not on finer measures of the same concepts. While the authors treat the failure to find effects on the finer measures as an unexpected finding, it could have been predicted by matching theory. The finer measures captured technological and market similarities that would have been difficult to observe for the focal managers, and hence would not have been good measures for matching on observable criteria. Also, like Wang and Zajac (2007), they did not theoretically distinguish similarity from compatibility, but they examined an industry where these concepts correlate highly. These are pioneering studies that show some of the promise of matching concepts, but both studies would have had somewhat different theoretical reasoning and empirical measures if the authors had set out to test matching theory. We believe that matching theory would have given additional insights. Our models also contained variables describing the effect of resource levels and prior ties, and we obtained conventional results on those controls. In support of an effect of more
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and better resources (Ahuja, 2000; Eisenhardt & Schoonhoven, 1996; Li & Atuahene-Gima, 2002; Stuart, 1998), we found that firms with faster ships, and isolate firms with larger ships, were more likely to enter alliances. In support of an effect of prior ties (Gulati, 1999; Gulati & Gargiulo, 1999; Keister, 2001; Podolny, 1994), we found that firms repeated past alliances, and especially when they had multiple ties with another firm. These findings not only support previous arguments about the dual requirements of inducements and opportunities in alliance formation, but also highlight the complementary role played by the matching model in predicting alliances. Previous studies have shown that more resources make an organization a more attractive alliance partner, and that prior ties reduce uncertainty and search costs involved in alliance formation. By contrast, matching theory emphasizes the fit of observable resources held by sets of organizations rather than the level of resources of each organization, and it examines firm characteristics rather than interfirm connections. This theory has some advantages over the other two arguments. First, alliance formation occurs when two or more organizations are mutually useful for each other, so it is reasonable to focus on their fit as the matching model does, rather than the absolute amount of resources that firms hold. Second, while network theory highlights the role of trust and reciprocity that social ties provide, matching theory focuses on the instrumental use of alliances for achieving strategic goals through pooling of observable resources. This orientation makes it possible to articulate mechanisms behind tie creation not only with strangers but also with repeated partners. Third, many alliance investigations assume exchange of knowledge in alliances, but the matching model relaxes this assumption by focusing on observable characteristics, including available information on tangible resources (e.g., manufacturing facilities and equipment, retail store space) and signals of intangible resources (e.g., patents, design expertise). Matching theory thus brings issues to alliance research that are closely related to the commercial rationale for
36
making alliances, and fills in gaps left open by prior work. The data used in this study cover trans-ocean shipping routes operated by firms originating from 37 nations, so its generalizability across cultural and institutional contexts should be high. This multi-location research orientation is important when studying interorganizational networks because the formation and development of interorganizational networks are subject to cultural influence (Doney, Cannon, & Mullen, 1998). In addition, these findings are from a context in which firms match specific resources with easily measurable characteristics in order to extend and connect route networks. We chose liner shipping for this analysis exactly because it is easy to specify a priori which motives drive alliances, and the relevant resource characteristics are observable both for the focal managers, who can use the same data sources as we did, and for researchers. Because resources that firms in alliances exchange and pool are observable, it is likely that managers have less concern not only for ex-ante transaction costs that partners’ intentional misrepresentation of resource possession causes but also for ex-post transaction costs due to ambiguities in monitoring partners’ resource inputs in alliances. Thus, the research context enables us to partial out potential unobserved effects of transaction costs. We expect the same patterns of alliance formation in other contexts in which managers know which resources to obtain and where to get them. This study has some limitations. First, the predictions on resource matching were tested on ships, which are tangible production assets and hence different from intangibles such as the technological knowledge that have been the focus of much work on alliances (Powell et al., 2005; Rothermael & Boeker, 2008). This limitation is inherent in the theory, which only assumes matching on criteria that the decision makers can observe. For unobservable assets, the predictions from matching theory are different because the theory emphasizes signals, as well as exits as a way to leave matches where the unobservable criteria turned out to be poorly
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matched. Likewise, while the prediction of advantageous (on average) alliances is consistent with matching theory for observable criteria, it is more questionable whether unobservable criteria inherently more difficult to judge the match are used in alliance formation. Mis-estimation is more likely and has greater consequences because there is more uncertainty to assess. Work on matching needs to recognize this limitation and apply the correct prediction depending on the observability of the criteria of interest. An additional limitation of this study is the assumption that firms obtain resources by either going it alone or establishing alliances. It is likely, however, that the strategic options are more varied. Indeed, shipping firms frequently use leasing of ships in order to adjust to demand fluctuations. While such actions may not have long-term consequences on alliance formation and firm strategy, future research may consider the effect of such strategic options on matching outcomes. The investigation of match consequences had limitations that stem from the difficulty of constructing models that conclusively separate the different sources of performance and survival. For example, firms with a competitive advantage may find it easier to enter alliances or, alternatively, may be less interested in entering alliances. Although the models used firm effects to control for this possibility, such controls assume time-constant firm differences and are thus simpler than what would be realistic for a dynamic industry such as shipping. Better controls are not available, however, so the only possible response is to be cautious in interpreting the findings. There are many promising extensions of this work. Firms often make alliances for pooling complementary market access and compatible resources, and further research on how this is done seems important. Measures of complementarity or compatibility may in some cases be more difficult to construct than in this study, but the important role that matching played in this investigation suggests that additional research would be valuable. For example,
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our measure of market complementarity did not weight markets by importance. Although we prefer the parsimony of our measure, measures that incorporate the importance of complementary resources are a potential extension. This would require knowledge of how managers value the resources used in alliances. Another potential direction for future research is to investigate additional consequences of resource matching. Do alliances last longer when allying firms have a presence in complementary markets and compatible resources? Do better matched alliances innovate more? Can such alliances more effectively meet customers’ needs? There is much room for additional work on consequences. Future work can also analyze the routines and rules that govern organizational assessment of complementarity and compatibility. Our analysis of the shipping industry and interviews with shipping managers indicated that the matching criteria were market, ship size, ship speed, and ship age. However, we expect that different contexts require different types of resources for matching. Future research can examine which resources managers consider to be critical when assessing potential matches and how competitive structures influence managers’ perceptions of matching. Research on the cognitive basis of matching has potential to integrate work on managerial cognition and decision making with interfirm network research. In conclusion, we propose a matching theory of alliances. Unlike past work, we emphasize market complementarity and resource compatibility as critical drivers of alliance formation. The empirical analysis supports matching theory predictions on alliance formation, firm growth, survival, and performance. Comparison between the criteria in use and the performance consequences revealed some evidence of nonoptimal matching, suggesting that managers have difficulty in finding best matches as a result of either bounded rationality or limited opportunities. Future research should pursue the effects of complementarity and compatibility on matching in other contexts, and should investigate whether actual matches and optimal matches differ. We believe that matching theory opens up exciting new research
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opportunities.
40
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45
TABLE 1: Descriptive Statistics and Correlations
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
5 6 7 8 9 10 11 12 13 14 15 16
Alliance formation Log firm age Route count Log fleet size Log ship size Ship speed Ship age Log degree Mean tie strength Proportion ties Alliances in past 3 years Log fleet size difference Market complementarity Log ship size difference Ship speed difference Ship age difference
1 2 3 4 Mean S.D. Min. Max. .37 .48 0 1 1 2.96 1.08 0 5.02 .02 1 12.82 10.31 1 37 .36 .13 1 2.35 1.16 .69 4.79 .16 .23 .41 1 7.04 1.35 3.7 8.75 .16 .28 .14 .61 19.12 8.83 5 25 .30 .04 .40 .25 10.87 5.94 1 33 -.12 -.04 -.18 -.13 2.03 1.59 0 4.58 .44 .17 .82 .34 1.71 3.59 0 18 .57 .03 .46 .12 .27 .41 0 1 .72 .06 .45 .19 .45 1.51 0 11 .38 .05 .22 .07 2.61 1.07 0 4.78 .07 .17 .21 .42 .40 .29 0 .91 .31 .05 .29 .22 6.90 1.35 0 8.68 .12 .09 .15 .09 4.10 3.34 0 23 -.23 -.04 -.17 -.11 6.33 4.64 0 25.47 -.24 -.11 -.25 -.24
5 6 7 8 9 10 11 12 13 14 1 .51 1 -.13 -.26 1 .31 .56 -.20 1 .01 .28 -.07 .52 1 .16 .30 -.11 .53 .76 1 .08 .18 -.06 .26 .68 .53 1 .21 .11 .03 .15 .09 .10 .11 1 .21 .29 -.16 .30 .05 .18 .06 .13 1 .17 .11 -.01 .19 .06 .07 .04 .13 .06 1 -.16 -.35 .01 -.21 -.24 -.23 -.16 -.10 -.15 .22 -.21 -.18 .29 -.29 -.20 -.24 -.13 -.13 -.15 -.05
N=2283
46
15
16
1 .11
1
TABLE 2: Models of alliance formation Isolate firms All firms Networked firms Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Log firm age -0.08* -0.09** -0.08 -0.11* -0.10 -0.17* (0.04) (0.03) (0.05) (0.04) (0.07) (0.07) Route count 0.01 1.00E-03 0.02 0.01 -0.01 -0.04 (0.01) (9.00E-03) (0.01) (0.01) (0.03) (0.04) Log fleet size 0.01 0.02 -0.05 -0.03 0.17 0.16 (0.07) (0.06) (0.08) (0.07) (0.13) (0.11) Log ship size 0.06 -4.00E-03 0.04 -0.01 0.37+ 0.62* (0.08) (0.06) (0.09) (0.07) (0.20) (0.25) Ship speed 0.04* 0.03 0.04 0.04 0.01 -0.09+ (0.02) (0.02) (0.03) (0.03) (0.03) (0.06) Ship age -0.01 -0.00 -0.03* -0.01 0.04+ 0.05* (0.01) (9.00E-03) (0.01) (0.01) (0.02) (0.02) Log degree 0.05 -2.00E-03 -0.12 -0.12 (0.07) (0.07) (0.16) (0.12) Mean tie strength 0.19 0.15 0.14 0.13 (0.15) (0.12) (0.13) (0.12) Proportion ties 0.13** 0.16** 0.14** 0.15** (0.04) (0.04) (0.04) (0.04) Alliances in past 3 years 1.97** 1.93** 2.12** 2.01** (0.24) (0.19) (0.23) (0.20) Log fleet size difference -0.03 -0.09+ -0.01 -0.04 -0.29+ -0.39** (0.05) (0.05) (0.06) (0.05) (0.10) (0.13) Market complementarity 1.61** 1.24** 2.92** (0.17) (0.19) (0.77) Log ship size difference 0.19** 0.17** 0.40** (0.05) (0.05) (0.143) Ship speed difference -0.07** -0.07** -0.01 (0.02) (0.02) (0.03) Ship age difference -0.03** -0.04** -3.00E-03 (0.01) (0.01) (0.02) Constant -2.49** -3.26** -1.43+ -2.35** -3.34 -6.42** (0.49) (0.46) (0.75) (0.55) (2.04) (2.36) Observations 6204 6204 3400 3400 2804 2804 Firms 303 303 112 112 267 267 Log likelihood -4185.35 -4080.49 -2605.47 -2554.32 -1488.83 -1421.22 LR Chi2 604.09 1172.54 1166.87 1396.25 8.31 19.46 Degrees of freedom 12 17 12 17 7 11 Standard errors in parentheses are adjusted for clustering at firm level. + significant at 10%; * significant at 5%; ** significant at 1%; two-sided tests
48 TABLE 3: Models of alliance consequences
Log firm age Log fleet size Number of single routes Number of alliance routes Log fleet size difference Market complementarity Log ship size difference Ship speed difference Ship age difference Constant
Containers 514.30* (204.24) -1,417.56 (1,045.25) 4,800.59** (810.05) 5,939.97** (918.01) 17,934.70** (6,787.54) -12,127.27+ (7,293.61) -5,769.90+ (3,057.38) -2,220.01 (5,313.52) 4,060.78 (2,823.88) 63,370.88** (16,116.22) 0.79 1075 137
Firm failure -0.04 (0.03) -0.07 (0.09) -0.20 (0.25) 0.02 (0.08) -0.65 (0.99) -0.62 (1.05) -0.21 (0.22) -0.08 (0.45) 0.46* (0.19) -1.65* (0.76)
ROA 0.09* (0.04) 0.10 (0.20) -0.14 (0.29) -0.13 (0.23) 2.70 (1.72) 1.39 (2.19) -1.68+ (0.97) 2.58* (1.03) -0.91* (0.41) 9.68** (2.57)
Autocorrelation Observations 1082 175 Firms 154 27 Failures 40 R square 0.58 0.06 Wald Chi2 152.99** 42.84** 34.31** Degrees of freedom 11 10 10 Standard errors in parentheses are adjusted for clustering at firm level. + significant at 10%; * significant at 5%; ** significant at 1%; two-sided tests
48
Hitoshi Mitsuhashi (
[email protected]) is an associate professor of management and organization in the Faculty of Business and Commerce at Keio University. He received his Ph.D. from the New York State School of Industrial and Labor Relations at Cornell University. His research interests include the evolutionary dynamics of interorganizational relations and the path dependency of organizational behavior.
Henrich R. Greve (
[email protected]) is a professor of Entrepreneurship and Organizational Behavior at INSEAD. He received his Ph.D. in business from the Graduate School of Business, Stanford University. His research examines learning and strategy in networks, performance feedback effects on strategic change, and the effects of diffusion processes on organizations and communities.
