Electronic copy available at: http://ssrn.com/abstract=1953996

Greener Pastures:

Outside Options and Strategic Alliance Withdrawal

Henrich R. Greve

INSEAD

1 Ayer Rajah Avenue

Singapore 138676

Tel: +65 67995388; Fax: +65 67995399

henrich.greve@insead.edu

Hitoshi Mitsuhashi

Faculty of Business and Commerce

Keio University

Minato-ku, Tokyo 108-8345 JAPAN

Tel/fax: +81-3-3453-4511

Email: mitsuhashi@fbc.keio.ac.jp

Joel A. C. Baum

Rotman School of Management

University of Toronto

105 St. George Street

Toronto, Ontario, M5S 3E6, Canada

Tel: 416-978-4914

baum@rotman.utoronto.ca

November 2011

Keywords: Interorganizational networks; Alliance withdrawal; Matching theory; Market

complementarity; Embeddedness; Rivalry; Shipping industry

Research support from the Norwegian Research Council is gratefully acknowledged (Grant number 86469). We are

grateful for comments from Michael Jensen and Hart E. Posen, as well as Organization Science senior editor

Gautam Ahuja and three anonymous reviewers.

Electronic copy available at: http://ssrn.com/abstract=1953996

2

Greener Pastures:

Outside Options and Strategic Alliance Withdrawal

Abstract

Departing from prior work that demonstrates the stickiness and stability of alliance networks resulting

from embeddedness, we extend matching theory to study firms‟ withdrawal from alliances. Viewing

alliance withdrawal as a result of firms‟ pursuit of more promising alternative partners – outside options –

rather than failures in collaboration, we predict that a firm is more likely to withdraw from an alliance

when there is a higher density of outside options that have better match quality than the current partners.

We also propose that, because matching is two-sided, outside options have a greater impact on a firm‟s

withdrawal when they are more likely to initiate new alliances. Using data on alliances in the global liner

shipping industry, we show that, controlling for internal tensions in the alliance, outside options predict

alliance withdrawals. Thus, despite the alliance stickiness and stability, firms alter their alliances in

response to the availability of promising outside options, even leaving alliances that appear successful.

3

Introduction

Theoretical treatments typically portray strategic alliances as means for firms to gain access to resources

not otherwise available to them (Dyer and Singh 1998, Teece 1992), and empirical research has indeed

found that alliances produce benefits for member firms (Baum et al. 2000, Burt 1992, Powell et al. 2005,

Uzzi and Gillespie 2002). Given this view, numerous studies focus on selection processes that afford

firms unequal access to alliances, and especially how existing alliances create network resources that

open further opportunities for alliance creation (Chung et al. 2000, Gulati 1995, Gulati and Gargiulo 1999,

Li and Rowley 2002).

The performance advantage view of alliances admits that some alliances outperform others, and

that some fail to produce benefits for one or more of the partners (Khanna et al. 1998, Oxley 1997).

Matching uncertainty can produce alliances in which partners discover that they cannot work well

together, or that the trust required for effective cooperation fails to materialize after disappointing initial

experiences (Levinthal and Fichman 1988, Reuer et al. 2002). Research on alliance withdrawal is far

scarcer than research on alliance formation, but, consistent with the performance view, has usually treated

withdrawals as the result of unsatisfactory alliance performance (Baker et al. 1998, Broschak 2004, Greve

et al. 2010, Rowley et al. 2005, Seabright et al. 1992). An exception to this internal focus is found in

work on the role of status in alliance withdrawal (Jensen 2006).

Although an understanding of alliance withdrawal is critically important in building models of

alliance behavior and network dynamics, absent from this approach is consideration of the possibility of

firms leaving alliances that produce the expected benefits in order to pursue “outside options”; that is, to

pursue alternative alliance partners rather than continue with current relations.1 Such alliance

withdrawals are consistent with firms pursuing their own interests, and hence reacting to the inducements

and opportunities in the situation (Ahuja 2000). This consideration allows us to view firms as embedded

1 In game theory, outside options are alternatives to the current relation (e.g., Binmore et al. 1989), as in the common

English usage of the term “option.” They differ from financial or “real” options by being freely available rather than

obtainable at a price.

4

in social networks, but willing to break existing ties if the benefit expected from a new alliance is

sufficiently large to justify it. The foundation is a theory of intermediate embeddedness of economic

behavior (e.g., Granovetter 1985) in which firms‟ decision makers are neither over-socialized “cultural

dopes” whose behavior is completely determined by their social context, nor under-socialized “atoms”

free of social constraints.

Our central argument is that the ecology of a firm‟s outside options predicts alliance withdrawal,

with withdrawal being an increasing function of the prevalence of outside options expected to deliver

greater value than the firm‟s current partners. Hence, outside options matter for the stability of interfirm

relations. This is an important claim because it points to a theory of alliance dynamics that takes

withdrawal as seriously as formation, and that views withdrawals as resulting from either successful

identification of a superior partner or failure of the current alliance to produce the intended results. At the

micro level, the construal of alliance dynamics differs because firms leaving alliances to pursue more

favorable outside options do not necessarily experience either alliance performance shortfalls or problems

with partner trust or cooperation. At the macro level, firms‟ leaving alliances for better outside options not

only implies an additional cause of instability of alliance networks not considered by a theory of alliance

withdrawals focused exclusively on dissatisfaction, but also suggests that a dynamic network is not

necessarily one in which failed cooperation is rampant. To the extent that such outside-option seeking is

responsible for a meaningful share of alliance withdrawals, extant empirical models will be misspecified,

misinterpreted, and potentially misleading. Alliance researchers need to distinguish option-driven and

failure-driven alliance withdrawals in order to understand the drivers of alliance and network change.

Our approach differs from one emphasizing alliance stability and inertia as a result of

embeddedness (e.g., Chung et al. 2000, Gulati 1995, Gulati and Gargiulo 1999, Larson 1992, Li and

Rowley 2002, Uzzi 1996) and alliance instability and withdrawal as a result of internal tension and

conflict among partners (e.g., Doz and Hamel 1988, Park and Ungson 1997). Prior work has seen

alliances as inert because trust among existing partners allows open collaboration with less friction and

fewer safeguards than would be the case for alliances among strangers (Kale et al. 2000, Uzzi 1996).

5

Extending this argument, alliances have also been seen as “over-embedded” when their members stay

together even when better potential partners are available outside the current alliance, because they either

fail to search while they are in a relation or overestimate the benefit of the current alliance relative to its

alternatives (Goerzen 2007, Sorenson and Waguespack 2006, Uzzi 1997). Although empirical evidence

supports the contention that alliances have some inertia, the evidence does not indicate that decision

makers inevitably reproduce their firms‟ past relationships (Beckman et al. 2004, Baum et al. 2005) or

that cohesion prevents alliance dissolution as a result of poor fit on task-based criteria (Greve et al. 2010).

Nor does it follow from this evidence that the role of outside options in promoting alliance withdrawal is

unimportant. Even relatively inert alliances may break apart in the presence of outside options that are

sufficiently and saliently tempting to their members; alliances may dissolve “because of the arrival of new

information about an alternative prospective match” (Jovanovic 1979: 973).

To examine the role of outside options in alliance withdrawals, we analyze withdrawals from

interfirm alliances in the global liner shipping industry. Shipping alliances are formed to deliver a

scheduled transportation service – a route connecting a set of destinations – using ships pooled from, and

orders accepted by, alliance member firms. They are open-ended collaborations whose termination is

costly and unplanned (Makino et al. 2007), and are thus more meaningful for investigating drivers of

member withdrawal than temporary collaborations such as R&D alliances and underwriting syndicates.

Requirements of regular and predictable liner shipping services mean that routes entail commitments of

substantial resources that must be reassigned if an alliance breaks up or members withdraw. Despite these

costs, there is considerable turnover in shipping alliances as member firms withdraw individually or in

groups, and routes are ended.

Alliance Inertia versus Outside Options

A major theoretical claim in alliance research is that alliances are inert because they are embedded in a

network of prior direct and indirect relations among partners (e.g., Chung et al. 2000, Gulati 1995, Gulati

and Gargiulo 1999, Larson 1992, Li and Rowley 2002, Uzzi 1996), and that either these relations or

6

managerial inability to judge outside options lead to fewer alliance withdrawals than would be expected

from a calculation of benefits (Goerzen 2007, Sorenson and Waguespack 2006, Uzzi 1997). Supporting

this contention, previous studies demonstrate a strong tendency toward stability in interfirm

collaborations, with high rates of repeated alliances between firms and new alliances with firms to which

they are indirectly connected through shared partners (Garcia-Pont and Nohria 2002, Gulati and Gargiulo

1999, Podolny 1994). Conversely, alliances become less likely to dissolve as cohesion and attachment

develop through interactions over time (Broschak 2004, Levinthal and Fichman 1988). Several factors

promote alliance stability. First, trust among existing partners allows open collaboration with less friction

and fewer contractual safeguards than would be possible for alliances among strangers (Kale et al. 2000,

Puranam and Vanneste 2009, Uzzi 1996). Second, the threat of a negative reputation quickly circulating

in highly interconnected networks constrains opportunistic partner behavior (Garcia-Pont and Nohria

2002, Walker et al. 1997). Third, existing ties provide information about available partners and reduce

costs for probing appropriate partners (Gulati 1995). Fourth, existing ties generate partner-specific

experience, smoothing interactions and facilitating resolution of conflict (Zollo, Reuer, and Singh 2002).

Finally, evaluation factors and partner selection criteria are highly routinized, causing organizations to

repeatedly select the same partners as appropriate ones (Li and Rowley 2002). For these reasons, firms

benefit from repetition, and the patterns of interfirm alliances tend to be perpetuated.

Contrary to this argument, some claim that the emphasis on embeddedness and inertia has resulted

in too little attention being accorded to the pursuit of partners that optimize the commercial or technical

value of alliances and alliance portfolios (Hagedoorn and Frankort 2008, Hite 2003; Hite and Hesterly

2001). As a result, the intuition that alliances serve instrumental goals is not matched with systematic

research on how these goals are pursued. As an illustration of the insights that the instrumental view can

offer, Beckman et al. (2004) and Baum et al. (2005) find that firms ally with strangers as a means of

addressing firm performance concerns and exploring knowledge unavailable in existing networks.

Research on firm withdrawal from alliances offers a more direct test of whether alliances are inert

as a result of embeddedness or are subject to reevaluation based on instrumental criteria. Jensen (2006)

7

finds that firms are more likely to terminate their ties with partners that experience a status loss. Baker et

al. (1998) and Rowley et al. (2005) find that firms terminate ties to rectify power imbalances. Thus, firms

appear to withdraw from alliances as a result of low performance and internal conflicts that reveal a lower

value of the alliance than was expected at the outset. Here we take the instrumental view of alliance

withdrawal one step further by predicting that firms also withdraw from well-performing alliances as a

result of finding outside options in the form of more promising alliance partners than the current ones.

Matching theory is a good starting point for analyzing the effect of outside options on alliance

withdrawal. Matching theory has been applied to different types of social matches such as employment,

marriage, and coalition formation (Axelrod et al. 1995, Bruderl 2000, Mortensen 1982). The core idea is

that actors are different both in the characteristics they seek in others and in the characteristics they offer

to others, and the matching problem they face is how to find the best possible partner among those who

view them as the best possible partner. The matching problem is thus heterogeneous, with different actors

pursuing different things, and two-sided, with both sides of the relation needing to agree to the match. The

matching problem can be subdivided into matching on observable criteria that can be judged in advance

(Logan 1996) and matching on unobservable criteria learned after the match has been initiated (Jovanovic

1979). When criteria are observable, the matching is done at the initiation stage, and mismatches occur as

a result of excessive search costs or bounded rationality (Logan 1996, Mitsuhashi and Greve 2009). When

criteria are unobservable, the initial match is essentially random, and is improved by selective withdrawal

from partnerships with low revealed quality (Bruderl 2000, Jovanovic 1979, March and March 1977).

This is a general theory of matching that is not specifically developed for alliances, and indeed it

has seen most study in employment matching (Fujiwara-Greve and Greve 2000, Greve and Fujiwara-

Greve 2003, Simon and Warner 1992). However, its basic assumptions fit alliances well. First, because

alliances are made when firms seek to use resources that they do not have, a good alliance partner is one

that can provide complementary resources (Gulati 1995, Gulati and Gargiulo 1999, Mitsuhashi and Greve

2009, Vissa 2010). Second, firms are heterogeneous in resource endowments and needs, so the result is a

matching problem where each pair of potential partners has a distinct match quality that cannot be

8

expressed as a simple function of their characteristics. Finally, some match-relevant firm attributes are

observable, such as market presence and resources, while others, such as knowledge and routines, are not.

In standard theories of matching, outside options are implicit in the form of an expectation of what

the “average” match would be if the current one were left (Jovanovic 1979). This is a useful abstraction

when the focus is on the discovery of mismatches, but it overlooks the ecology of outside options. As a

result of this undifferentiated view of outside opportunities, applications of matching theory to alliances

have focused primarily on match initiation, and have not addressed the alliance inertia claim (e.g.,

Mitsuhashi and Greve 2009, Vissa 2010). Studies of alliance termination, on the other hand, have focused

on internal factors that break relationships apart, which do not require matching theory (e.g., Greve et al.

2010). However, theory predicts that a more diverse set of actors that could serve as potential matches

increase the possibility of finding a good match (Hannan 1988), which in turn suggests that withdrawal is

more likely when potential partner diversity is high, a prediction that has been supported empirically for

employment matching (Fujiwara-Greve and Greve 2000).

When the match criterion can be observed, diversity is no longer the best predictor of withdrawal,

and instead, the prediction can be made based on how each potential partner scores on the focal actor‟s

criterion. The focus thus shifts to the inequality of match quality, and the likelihood of withdrawal from

the alliance becomes a function of how easily the focal firm can find a partner with greater match quality

than its current partners. However, it is not the case that firms leave alliances whenever a better potential

partner comes along. First, matching may be on a broad set of criteria that includes both observable and

unobservable criteria, and in such cases the focal firm may have a threshold for the quality difference

between current and potential partners that justifies leaving the alliance. Second, buildup of trust and

routines within an alliance would similarly create a threshold for leaving the alliance. Finally, the

willingness of the potential partner to join an alliance with the focal firm may also vary, and cause a

seemingly promising potential partner to be unavailable. Because of such unobservable thresholds and

availabilities, models of alliance withdrawal as a result of outside options should predict best if they

identify the set of potential partners that have better match quality than the current partners and model

9

alliance withdrawal as a function of the prevalence (or density) of such outside options.

To assess the match quality of potential alliances, an important consideration is the role of

complementarity as a motive for establishing interorganizational collaboration (Aiken and Hage 1968).

Complementarity in alliances occurs when each firm has access to resources needed by but not possessed

by the other, where resources are interpreted broadly to include not only financial and material assets, but

also intangible assets such as knowledge or market access (Aiken and Hage 1968). The use of alliances to

obtain complementary resources is especially likely when the resources are critical to success and can

only be obtained internally with a time lag (Teece 1986). Consistent with this reasoning, firms are more

prone to form alliances with other firms that are complementary along such dimensions as material assets,

capabilities, and markets (Chung et al. 2000, Gulati 1995, Rothaermel and Boeker 2008, Rowley et al.

2005, Vissa 2010). Post-alliance performance also indicates that complementarity increases match quality,

as alliances with complementary partners require lower investment (Mitsuhashi and Greve 2009) and

provide greater performance for the member firms (Lin et al. 2009).

Because complementarity can yield competitive advantages that are difficult to obtain through

other means, firms may make comparisons between their complementarity with their current partners in a

given alliance and their complementarity with firms that are outside of the alliance. Firms outside the

focal alliance constitute the outside option relative to remaining in the alliance, and a firm is more likely

to withdraw from the alliance if there are more firms outside the focal alliance that provide higher

complementarity than the firms inside the alliance. Thus, alliance withdrawal is a joint outcome of the

complementarity of the current partners and the ecology of outside options defined as the number of firms

outside the current alliance that would afford greater complementarity. The prediction is:

Hypothesis 1: Member firms are more likely to withdraw from alliances when there are a larger

number of outside options with higher complementarity than current partners.

We test Hypothesis 1 using a measure of access to geographical markets, so two firms are

complementary when each has access to markets in which the other has no or limited access. Market

10

complementarity is important for firms that seek to globalize or are competing in a global industry (as in

our sample of shipping firms). Other characteristics of alliances are also likely to matter, such as whether

the firm resources are sufficiently compatible to be used jointly (Greve et al., 2010) and whether the

partners are sufficiently similar in status (Chung et al. 2000). We emphasize the market complementarity

of outside options for two reasons. First, a role of alliance-partner complementarity in alliance initiation

and withdrawal has already been well established, making exploration of an outside-option effect a

natural extension. Second, while compatibility and status similarity are likely to act as minimal thresholds

to be satisfied, complementarity in market access has a potentially high variation that invites comparison

with potential new partners even when the firm is in an alliance.

Two-sided Matching

Support for Hypothesis 1 would make it interesting and important to explore the effect of outside options

further by considering which particular outside options are most likely to prompt alliance withdrawals

(Gulati et al. 2008). In developing such predictions, it is vital to keep in mind that a potential partner must

also be interested in entering an alliance with the focal firm. Matching is two-sided, and both sides require

a rationale for the match in order for it to occur. Hence, while Hypothesis 1 characterizes the match from

the point of view of the firm currently considering withdrawal, a more comprehensive test must also

consider the match from the viewpoint of the potential partner. Such analysis refines the model by

explicitly testing the availability criterion considered an unobservable source of variation in Hypothesis 1.

From the potential partner‟s point of view, the decision of whether to enter an alliance with the

focal firm depends on the benefit presented by the focal firm and the cost imposed by its own structural

constraints. The benefits of an alliance stem from the gain in complementarity that it affords its member

firms, while the structural constraints stem from prior alliances and market behaviors that produce

commitments and competitive relations that stand in the way of leaving existing alliances for new ones

(Rowley et al. 2005). These structural constraints can cause a potential alliance partner that seems

promising from the focal firm‟s point of view to be unavailable (Ahuja 2000). Hence, the hypotheses that

11

follow develop a finer-grained characterization of the ecology of outside opportunities that reduces the

original set of firms that have greater market complementarity than a firm‟s current alliance partners to a

smaller subset of firms with both greater market complementarity and a high likelihood of being available

as an alliance partner.

We examine three factors that influence the availability of a potential alliance partner: renewal,

structural embeddedness, and rivalry. We arrive at each of these factors by considering how a matching

criterion known from research on alliance matches from the viewpoint of the focal firm will affect the

attractiveness of the focal firm from the viewpoint of a potential partner. Each factor has distinct

consequences when applied to the question of whether potential partners are actually available for an

alliance with the focal firm. Renewal is related to research on how firms seek to improve their structural

position through alliances with new partners (Baum et al. 2003). The key concern is whether the

complementarity of the new alliance was previously unavailable. If two firms already have an alliance,

they should be able to leverage the complementarity of each other‟s resources, and hence the incremental

value of a new alliance is lower than that of alliances with a firm that offers an opportunity for network

renewal (Hite and Hesterly 2001). Structural embeddedness is related to research on how firms seek to

form alliances with partners that they have common third-party ties with (Gulati and Gargiulo 1999;

Podolny 1994). This means that a potential alliance partner can be reluctant to take on the focal firm as a

new alliance partner if it is currently embedded in a network structure that provides mutual trust and

enforcement of misbehavior (Krackhardt 1998, 1999). Rivalry is related to research on how firms form

alliances that reduce the options of their competitors (Silverman and Baum 2002). This influences choices

because a potential partner may avoid allying with the focal firm for fear that its current alliance partners

will react negatively. These are not a complete list of factors that affect matching, but as indicators of the

task value and social and competitive constraints of alliances, they are a good starting point for testing

matching theory.

Renewal. A firm acting based on trust only might be expected to choose the outside options with

12

which it is already allied in another market. Managers of firms that are already in alliances with each

other know each other‟s operating routines and trustworthiness, and have established personal

connections that help in negotiating agreements (Larson 1992). Indeed, when entering new alliances,

firms that already have alliances are likely to pursue additional ones (Gulati and Gargiulo 1999, Podolny

1994, Powell et al. 2005). However, this effect of having alliances is not contingent on the other firm

being a better outside option than another firm. None of the many studies showing that firms with prior

ties establish ties have controlled for the ecology of outside options (e.g., Hagedoorn and Frankort 2008,

Sorenson and Waguespack 2006), suggesting that the effect of prior ties is independent of the outside-

option effect. The reason for this is that the reduction of uncertainty and relationship initiation costs

favors such matches (Baum et al. 2005).

Matching theory gains leverage from considering the benefit of an outside option rather than only

its cost. Using benefit as a criterion suggests that firms with existing alliance relationships are less

promising as outside options than a pure focus on alliance initiation costs would indicate. An alliance

between two firms places them in a position to explore their current market complementarities. Additional

alliances can create additional market complementarities, but they can also increase trust and operational

knowledge, making exploitation of existing market complementarities easier. Thus the choice between an

additional alliance with an existing partner and one with a new partner is one of making a deeper

commitment to an existing network tie or renewing the network through a new tie. If the motive for the

alliance is to seek new market complementarities, as predicted in Hypothesis 1, renewal is likely to be

given higher priority. Indeed, R&D alliances, which often involve knowledge complementarities, have

been found to be more likely to involve renewal than other forms of alliances (Lavie and Rosenkopf

2006). Outside options that have no current alliance with the focal firm in different markets are thus likely

to have a stronger effect on the withdrawal likelihood because they offer the strongest potential for

renewal of the market position.

Extending this reasoning, some firms have no current alliance but do have a past alliance. At the

outset, these seem similar to firms with no current or prior alliance. However, when matching involves

13

both observed and unobserved characteristics, it is likely that firms that had an alliance but withdrew

discovered some mismatch that was not obvious from observable criteria. Hence, such firms should also

be less promising outside options. Based on this reasoning, we hypothesize:

Hypothesis 2: The effect of outside options is stronger for outside options with which a firm has no

current alliance in different markets.

Hypothesis 3: The effect of outside options is stronger for outside options with which a firm has

neither current nor prior alliances in different markets.

Structural embeddedness. The ecology of outside options is described both by the promise each

outside option holds from the viewpoint of the focal firm and by its availability for an alliance. A firm‟s

ability to establish an alliance with a potential partner depends on whether the potential partner is also

interested in and capable of entering an alliance (Ahuja 2000). Although member withdrawals occur as a

result of unilateral decisions by one of the participating members, alliances result from mutual agreement,

so both sides need to be attractive to each other and available for new alliance options to be feasible. One

factor that might influence the willingness of a potential partner to form a relationship with the focal firm

is the existence of mutual relations with other firms.

Structural embeddedness occurs when two firms both have ties to a common third firm. Structural

embeddedness stabilizes alliances because the indirect connection reduces uncertainty about partners‟

capabilities, reliability, and motives when alliances are formed (Burt and Knez 1995, Oxley 1997, Pfeffer

and Salancik 1978, Simmel 1950), and can facilitate sanctions against partners that misbehave (Burt and

Knez 1995, Krackhardt 1998, 1999, Walker et al. 1997). When two structurally embedded firms form an

alliance, they and their common partner form a closed triad in which all are tied to each other. There is

evidence that closed triads occur systematically in networks as a result of decision makers‟ preference for

extending existing relationships and forming new relationships with their partners‟ partners based on

referrals (Baker 1990, Gulati 1995, Gulati and Gargiulo 1999, Podolny 1994, Uzzi 1996). Closed triads

are the building blocks of larger cliques of closely embedded firms that monitor each other and enforce

14

norms of behaviors, promoting stable and rewarding exchanges.

A firm that participates in alliances characterized by high structural embeddedness should make a

poor outside option, the stability of its alliance relations translating into lower availability to new partners

because it has little incentive to break up its current alliances and rebuild them when new alliance

opportunities are offered. A promising outside option that is structurally embedded in closed triads should

thus be less likely to promote alliance withdrawal. Conversely, a promising outside option that is not

embedded in triads has greater autonomy to reshape its network (Burt 2000, 2002). Consequently, we

expect the outside options to have a greater effect on alliance withdrawal when they are less embedded in

closed triads.

Hypothesis 4: The effect of outside options is stronger for outside options that are less structurally

embedded in closed triads.

Rivalry. Firms engage in market rivalry when they seek to sell products or obtain scarce resources

in the same markets, and such rivalry relations can affect tie formation because firm actions in the

network are supplements to their market strategies. Through this mechanism, outside options become less

attractive when the potential partners are allied with the focal firm‟s rivals. When a firm‟s rival has an

alliance with a promising outside option, the firm and its rival are substitutable from the outside option‟s

perspective because they pursue similar resources, seek out similar markets or customers, or supply

similar services and products to markets (Baum and Mezias 1992). This potential substitutability

increases the outside option‟s bargaining power over both the firm and its rival by placing them in

competition with each other (Pfeffer and Salancik 1978). This competition may be to their disadvantage,

as seen through the higher failure rate among firms whose partners form alliances with rivals (Singh and

Mitchell 1996), and among firms whose rivals form alliances that foreclose their alliance options

(Silverman and Baum 2002).

Although firms that only meet in the market can do little to prevent exchange partners from seeking

alternatives, firms that join each other in alliances have the ability to react when an alliance partner

15

initiates alliances with their market rivals. They can reduce their exposure by collaborating less openly

with the firm that is now allied with their rivals, or even break off the relation entirely. This is a safer

option because an alliance partner who establishes an alliance with a market rival of the focal firm raises a

host of concerns such as leaks of knowledge about valuable operating procedures, technologies, or

strategies. Because outside options with links to rivals of the focal firm are likely to value these alliances

and consider the reaction of their alliance partners if they should establish an alliance with the focal firm,

they are less likely to be available to form an alliance than a firm with no alliance relation with rivals of

the focal firm. Therefore, we predict that:

Hypothesis 5: The effect of outside options is weaker for outside options that have alliances with a

firm’s rivals.

Data and Methodology

Liner Shipping

Liner shipping is the operation of regularly scheduled routes among commercial ports, as opposed to the

specially ordered journeys in bulk shipping. Most routes use ships that are made to exclusively carry

containers, but some have specialized ships such as car carriers or refrigerated cargo carriers. In order to

obtain maximum efficiency from each ship and meet the demands of increased world trade, shipping

firms have increasingly used large ships. Currently, container ships capable of transporting 8,000 twenty-

foot equivalent unit (TEUs) containers and costing USD100 million are common on the main routes.

Because customers value frequent departures, a single route has multiple ships. Capacity

requirements increase with the number of ports served by a route, but to be commercially viable, routes

need multiple port calls at each end. A trans-Pacific route typically has at least five ships, while a route

between Asia and Europe has at least eight. The risk and financial burden of such a resource commitment

may be too much for a single company, and it may not have a sufficient customer base to support a route

of the desired regularity. Alliances are thus formed to ensure sufficient operating resources and to feed

route traffic. Both are served by market complementarity because stationary resources (e.g., port facilities

16

and distribution facilities) are used most efficiently when the partners have complementary market

coverage, while inter-area traffic is more efficient when the market areas are complementary. For example,

in 2004 CMA-CGM, China Shipping, and P&O Nedloyd cooperated on a weekly South China Sea route,

each providing three vessels to serve the following rotation: Busan, Shanghai, Hong Kong, Savannah,

New York, and Norfolk. Advantages of these three operators forming an alliance lay in their

complementary port access, plus the combination of China Shipping's strong eastbound route network in

China with CMA-CGM's and P&O Nedloyd‟s strength in the east coast of the United States. As in the

airline industry, constellations such as the „Grand Alliance‟ formed by Hapag-Lloyd, MISC, NYK, and

OOCL have emerged. Unlike the airline industry, however, these constellations have been unstable, and

have not restricted members‟ ability to partner with nonmembers (Ryoo and Thanopoulou 1999). Hence a

single route is the meaningful unit for analyzing shipping alliances.

We analyze vessel sharing agreements, which are joint route operations in which the operators

pool vessels and have shared authority over their use. Such alliances involve coordination, information

transfer, and joint problem-solving. They create route economies of scale with less investment from each

alliance member, and pool alliance members‟ orders across their customer bases. Alliance members also

share access to local port facilities and services, negotiate jointly with third-party service providers, and

mutually adapt to variation in customer demand. These benefits are gained at the expense of more

complex decision rules as a result of the shared authority. The cost of member withdrawal is potentially

high because withdrawal may require reconsideration of shipping schedules and calling ports, capacity

adjustments, reassignment of vessels from other routes, vessel leases, and a search for new alliance

partners. These actions have a substantial impact on firms‟ operations, costs, and reliability of service.

The alliance network changes through formation of new alliances and breakup of or withdrawal

from old ones, but a graph of a single year is still informative regarding its overall structure. Figure 1

shows the year-2001 network, with circles indicating firms and lines indicating alliances. Both symbols

are scaled by their degree, so firms with many routes are larger, and lines connecting firms with more

alliances are thicker. The network exhibits some clustering, which could result from trust and joint

17

routines making repeated partnering attractive. Comparison of figures from adjacent years indicates that

the network retains its basic structure over time, although there is significant entry and exit of alliances.

Sample

The sample consisted of 666 alliances involving 171 shipping line operators originating from 46 nations

between 1991 and 2004. The data extend from 1988 through 2005, but the 1988-90 data are only used to

create independent variables and the 2005 data are only used to detect withdrawals from the 2004

alliances, so the analysis covers 14 years. Alliance data are from the International Transportation

Handbook 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 and all alliances among these firms during the study years are

included in the data. 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 with all operators

serving a Japanese port as the seed and all their contacts included. The data are highly reliable, as only

information original from line operators is compiled, and the data source has a comprehensive list of

routes served by line operators. The world‟s leading line operators have routes connected to Japan, one of

the world‟s largest economies and a major exporter of industrial goods. Roughly eight percent of world-

wide container traffic originated in or was bound for Japan in 1996 (Containerisation International

Yearbook, 1998), but routes connected to Japan usually visit the other main Asian manufacturing locales

as well. We used Containerisation International Yearbook to verify the route information and Lloyd’s

Registry Fairplay to obtain data about the firms‟ ships.

Our observations begin in 1988 following a major shift in industry alliance practices that occurred

during the mid-1980s. Prior to the observation period, collaborations were arranged through the

„conference system,‟ which operated as a form of cartel, collectively fixing tariffs and controlling

volumes of trades. The U.S. government, which is normally hostile to cartels, had tolerated the system to

foster the development of strong trade and commerce systems. In 1984, however, the U.S. Merchant

18

Shipping Act was passed, which excluded shipping conferences from anti-trust regulations but placed

strict limitations on price fixing. Together with the containerization revolution and globalization, the Act

weakened the ability of the conference system to enforce price agreements. In parallel with this U.S.

change, the European Commission also increased its scrutiny of freight rates and price agreements.

We constructed our alliance network by coding all routes operated jointly by multiple operators.

We regard joint operation of a route through a vessel sharing agreement 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 6.0

(Borgatti et al. 2002).

Dependent Variable and Models

Our dependent variable is member withdrawal from an alliance, coded as 1 when a given member of an

alliance operating a route at time t is no longer part of the alliance operating the route in t+1 and as 0

otherwise. Our unit of analysis is thus the firm-alliance-year. We impose the requirement that the route

still operates next year in order to distinguish member withdrawal from route failure. Theoretically the

determinants of route failure should be different from those of member withdrawal, with high competition

or low demand being prominent cases, so this distinction is important to isolate the mechanisms that lead

to withdrawal. However, the causes of route failure risk and member withdrawal may be correlated,

which we control for through a selectivity model with route failure as the first step and member

withdrawal as the second. The selectivity model is used to obtain the inverse Mill‟s ratio based on Lee‟s

(1983) generalization of Heckman‟s (1979) two-step method of selection bias correction, which in turn is

entered as a covariate in the withdrawal model. In order to obtain the best possible estimates of the

selectivity model, it was estimated on the failure of all routes, including routes with one operator. It had

the full set of variables of the withdrawal model plus a number of additional identifying variables. The

19

identifying variables were chosen to capture effects analogous to a population ecology model of firm

failure (applied to routes) and those that represent industry context and resources (ships) that affect

alliances. Thus we enter linear and squared terms for market density, which captures competitive intensity

in the same way as population ecology studies do, as well as route age to control for liability of newness.

We also control for market region fixed effects to capture demand heterogeneity across regions, and a set

of ship characteristics to control for resource quality of the alliance partners. As is usual when designing

selectivity corrections, we chose identifying variables that do not predict the second-stage outcome if

included in that regression. The estimates of the selectivity model presented in Table 1 show the expected

effects of density and liability of newness, consistent with the ecological prediction. They also show as a

greater tendency of partners with new ships to terminate routes, presumably because of better alternative

uses.

=====TABLE 1 ABOUT HERE=====

Withdrawals from alliances can occur in continuous time, but the annual measures of alliance

participation lead to a discrete time measurement of whether a member leaves a given alliance in a given

year. To model this process, we apply a complementary log-log specification, which is the equivalent of

an exponential hazard rate model. It is thus the most appropriate model for analyzing event history data

given in discrete time intervals, as these are (Allison 1982). To control for all annually varying influences

that are shared across the observations, we enter indicator variables for each year.

Variables

To perform the tests, it is necessary to characterize the ecology of outside options in terms of their market

complementarity with the focal firm. We measure market complementarity through a variable based on

the International Transportation Handbook classification of routes into 16 regions.2 We define

complementarity as a count of the number of complementary markets in which the firms are represented

2 The regions are Africa, Asia, Arabia, Australia, Caribbean, Central America, China, Europe, India, Korea,

Mediterranean, North America, South America, Russia, Transatlantic, and Worldwide. These regions are defined

from a China or Japan origin because of the tendency of routes to transport manufactured goods and parts from these

origins to destinations all over the world. The routes are rotations, so they also bring goods back to Asia.

20

divided by the union of the markets in which the firms are represented. More specifically:

Complementarity = BA

BABA

)()(

Thus, if firm i is in four markets (the set A) including two markets in which firm j is also represented, and

firm j is in five markets (the set B) including two markets that in which firm i is also represented, then the

union of their market presence is 4+5–2=7 markets. The complement is 4–2+5–2=5 markets. The

complementarity is then 5/7=.714.

We calculate the complementarity of the firm and its partner(s) in the focal alliance, and use this

as the threshold to determine whether a firm in the market but not in the focal alliance would be a

promising alliance partner. If there are multiple partners in the alliance, we set the threshold equal to the

lowest-complementarity firm in the alliance (using the highest does not alter the results). Next, we count

the number of firms in the market that are not in the focal alliance, and that have higher complementarity

than the threshold. This is the variable Outside Options that we use to test Hypothesis 1.

We test the other hypotheses using variables constructed by partitioning Outside Options into

subsets, and conducting tests for differences in coefficient estimates for each partition (e.g., Yip and Tsang

2007). For Hypothesis 2, we separate the outside options that are familiar to the firm because it is

partnered with them in a different market (Outside options, alliance in different market) from firms

without such a connection (Outside options, no alliance in different market). For Hypothesis 3, the latter

of these is further partitioned into firms that have a past alliance with the focal firm (Outside options, not

current alliance but past alliance) and those that do not (Outside options, neither current nor past alliance).

Hypothesis 4 predicts the effect of an outside option being embedded in closed triads, which we

capture using clustering coefficients. These coefficients are computed as the number of triads in which a

firm is embedded divided by the maximum number of triads that a firm can potentially form with its

partners. 3 Clustering coefficients were obtained for each firm in each year using the algorithm available

3 Watts (1999) provides a full rationale for this, but the intuition is that triads encode all the information needed in

order to understand network clustering because higher-order clusters can be reduced to a set of triads.

21

in Ucinet 6. To test Hypothesis 4, we created two variables, a firm‟s number of outside options with

clustering coefficients greater than zero (Outside options, clustered) and its number with clustering

coefficients equal to zero (Outside options, not clustered). Finally, for Hypothesis 5, we first identify the

focal firm‟s rivals, defined as firms that operate in any of the 16 geographical regions where the focal firm

also operates. After we first identify rivals by using the route data for all firms, we then partition each

firm‟s outside options into two groups: those that are allied with a rival of the focal firm (Outside options,

is in rival alliance) and those that are not (Outside options, is not in rival alliance).

We conduct separate rather than joint tests of Hypotheses 2-5 because two-criteria partitions (e.g.,

Outside option, is in rival alliance and is not clustered) become quite small in our sample, and hence do

not permit meaningful tests. Although this data limitation prevents us from estimating a full model that

separates the effect of each dimension of the outside option attractiveness from those of the others, the

analyses suffice to show that each has an effect on the alliance withdrawal likelihood.

Control variables. We use the same control variables as the models in Greve et al. (2010), which

also use these data, and include main-effects for the variables of theoretical interest as well.4 We include

controls for route, firm, alliance, and market characteristics. For routes, we control for the logarithm of

the number of days between sailings (frequency) and the presence of feeder routes, which are short-

distance branching paths from major ports to small ports that are inaccessible to large container ships or

uneconomical for them to serve. We control for the number of partners and the number of arm‟s length

ties in the form of container slot purchases and swaps, which are contractual arrangement in which one

firm purchases capacity from another, leaving operation of the route to the discretion of the firm that

operates it.

For alliance tenure, we enter the logarithm of the number of months since the alliance was

established, setting the tenure of new alliances to six months, and we enter an indicator variable set to one

4 Including the interaction terms estimated in Greve et al. (2010) does not alter the findings for our hypothesis.

These model estimates are available from the authors.

22

in the first year of an alliance.5 For the focal firm, we enter its fleet size (number of ships), an indicator

variable for owning no container ships, and capacity growth (TEUs) in the preceding year. For the market,

we enter the number of withdrawals from alliances operating in the area, as withdrawals may trigger

realignment in other alliances (Olk and Young 1997).

We also employ variables that measure the internal cohesion of an alliance (Olk and Young 1997).

The first is the number of alliance ties that a firm had with members of the focal alliance at time t-1. The

second is the number of arm‟s length ties (slot purchases or swaps) that the firm had with members of the

focal alliance at time t-1. We expect alliances with partners that also meet in other alliances to be more

stable. We consider both alliances and arm‟s length ties because any kind of interfirm relationships can be

a source of cohesion (Beckman and Haunschild 2002, Marsden and Campbell 1984).

We employ variables that measure friction resulting from low resource compatibility, which

occurs when the resources that allying firms have are not sufficiently interchangeable with each other to

produce uniform output quality and quantity (Greve et al. 2010). Accordingly, we compute the absolute

difference of each firm‟s resources from that of the mean of its partners in the focal alliance (Greve et al.

2010, Wang and Zajac 2007). We consider compatibility on three ship characteristics: age, maximum

speed (in knots), and capacity (in TEUs). Low resource compatibility complicates delivery of shipping

services and distribution of profits. Although firms do not enter alliances unless their partners can provide

resources over some threshold level of compatibility, variation in the level of resource compatibility can

still be a source of friction in alliances (Greve et al. 2010).

We also employ measures of the stability of the markets of the alliance firms. We calculate

multimarket contact among the members of the focal alliance in the market served by the alliance using

the proportional multimarket contact measure suggested by Baum and Korn (1996). This measure

captures the potential for strategic interactions among alliance members as the average proportion of

markets each alliance member serves that are also served by its partners in the alliance. Higher

5 When there is a change in membership through withdrawal or entry of members, alliance tenure is reset, so the

alliance tenure can be lower than the route tenure.

23

multimarket contact among firms in the alliance translates into greater ability to affect each other in

market competition. It will make alliances more cohesive if firms are reluctant to leave alliances because

their former partners can react against them through market competition. Like the outside options

measure, it uses information on the intersection of markets between the alliance members, but because it

does not use information on firms outside the alliance it is not correlated with the outside options measure.

We also enter each firm‟s market overlap with other firms in the industry (regardless of whether they are

members of the same alliance or not) (Baum and Korn 1996), as we expect firms with higher market

overlap to face greater competition and hence be more likely to make changes to their alliances. All

independent variables are lagged one year.

=====TABLE 2 ABOUT HERE=====

Table 2 shows the descriptive statistics and correlation coefficients for the analysis. The variables

measuring alternative partitions of the outside option set generally have high correlations, suggesting

some similarity between the different ways of partitioning the sets. Notably, the correlation of outside

options that have alliances with rivals and outside options that are in clusters is higher than any other pair

of correlation coefficients. This suggests that the competitive rivalry network and the alliance network are

more similar to each other than one would expect them to be if they varied independently of each other.

These correlations do not lead to multicollinearity in the analysis, however, because we enter only one

partition at a time. For clarity, variables we do not enter simultaneously are shaded in the correlation table.

Results

Table 3 shows the results of complementary log-log models that predict member withdrawals. Model 1 is

a baseline control variable model. Variables testing the hypotheses are entered in Models 2 through 6.

Overall fit is high in all models as indicated by the likelihood ratio test χ2 values (p < .01).

=====TABLE 3 ABOUT HERE=====

Model 2 tests Hypothesis 1, which predicts that alliance member withdrawal will be greater in the

presence of greater outside options with higher complementarity than current partners. The coefficient of

24

outside options is positive and significant, in support of this prediction. Alliance withdrawal is thus a

function of promising outside options, controlling for cohesion effects within the alliance.

Next we examine whether the effects of outside options vary with their characteristics.

Hypotheses 2 and 3 predict a greater effect of outside options that see the focal firm as a source of

network renewal because it offers access to complementarity not available through current partnerships.

Model 3 includes two variables that count a firm‟s outside options with which it does and does not have

alliances in different markets. The first variable is not significant. However, outside options to which a

firm is not allied in different markets increases its likelihood of withdrawal. The estimates thus support

Hypothesis 2, indicating that outside options that give access to new rather than existing market

complementarities are more likely to prompt withdrawal. However, a χ2 test indicates that the coefficient

for less familiar outside options is only marginally larger (p < .10, one sided test), so it difficult to

conclusively say they are different.

Model 4 includes variables that distinguish a firm‟s outside options with firms with which it has

allied in the past but does not currently have an alliance, from those with which it has never partnered. In

Model 4, the coefficient estimate for outside options with which a firm has neither current nor past

alliances is positive and significant, while the other outside option variables are insignificant. Additionally,

the coefficient for outside options with no prior connections is significantly greater than that of current

allies (p < .05). These estimates thus support Hypothesis 3. The convergence of results in Models 3 and

4 increases our confidence in the findings on alliance network renewal.

Model 5 tests Hypothesis 4, which predicted a stronger effect on alliance withdrawal of outside

options that are less embedded in closed triads. In Model 5, that prediction is supported by the coefficient

estimates, which show that only outside options that are not embedded in clusters affect alliance

withdrawals. The difference of coefficients is significant (p < .01). The result thus supports Hypothesis 4,

indicating that outside options are more likely to trigger withdrawals if they are not embedded in

networks that would raise the cost of reconfiguring their alliances to take advantage of an alliance with

the current firm.

25

Hypothesis 5 predicted that the outside options allied with a firm‟s rivals would not prompt a firm

to withdraw from its alliances. Supporting the hypothesis, the coefficient estimate for outside options not

in an alliance with rivals is significant and positive, whereas that of outside options that are allied with the

firm‟s rivals is insignificant. These coefficient estimates are consistent with Hypothesis 5, but they are not

significantly different from each other, so the results on Hypothesis 5 are inconclusive. Moreover, the

high correlation between the variables used in the tests of Hypotheses 4 and 5 suggests that these

explanations are difficult to separate in our data. If both had been significant, this would have complicated

the interpretation of Hypotheses 4 and 5, but the analysis shows better support for Hypothesis 4 despite

the correlation of the variables. This pattern of results suggests a greater effect of structural embeddedness

than market rivalries on which outside options have a greater effect on withdrawals by a focal firm.

Overall, the results provide evidence corroborating the significance of firms‟ outside options for

alliance withdrawal, as well as systematic differences in the tendency of firms to pursue these options. It

is thus of interest to consider the relative magnitudes of their effects. That is, which characteristics of

outside options are most likely to promote alliance withdrawals? Figure 2 shows estimated multiplier

effects as a function of all outside options, as well as for outside options characterized by the variables

testing Hypotheses 2 through 5. The estimated effects are computed based on coefficients from Models 2-

6 in Table 2, and for comparability at zero, mean, and mean plus 2.5 standard deviation values of the

variables. In Figure 2, a multiplier of 1.5 indicates a 50 percent increase in alliance withdrawals. For

clarity, Figure 3 shows the same multipliers computed relative to the effect of all outside options from

Model 2, with the all outside options effect becoming the constant unity (1). In Figure 3, a multiplier of

1.5 indicates that the subset effect is 50 percent higher than the effect of all outside options.

=====FIGURES 2 & 3 ABOUT HERE=====

As the figures show, outside options vary in their disruptiveness. Outside options with which a

firm has neither current nor past ties (Hypothesis 3) have the largest effect on withdrawal. Outside options

that have no alliance in different markets (Hypothesis 2) and are not structurally embedded in closed

triads (Hypothesis 4) are somewhat less disruptive. Outside options that ally with the focal firm‟s rivals

26

(Hypothesis 5) have the weakest disruptive effects. Thus outside-option driven alliance withdrawal

depends most importantly on the renewal potential of the outside options.

In a supplementary analysis, we examined the actions firms took in the year they withdrew from

alliances. This analysis is complex because firms often undertake multiple actions in a given year, so

connecting origin and destination is not straightforward. Moreover, some firms might leave alliances as a

result of a poor fit, and others as a result of outside opportunity. If both reasons for alliance withdrawal

are found in the data, we might expect some firms to do quite poorly (perhaps staying unmatched or

entering inferior alliances), while others will do very well. Indeed, that is what we find. There are 50

withdrawal events that are not matched by any form of entry event, suggesting that some firms have

difficulty finding good uses for the ships used in earlier alliances – a very negative outcome. There are

also 121 withdrawal events that are matched by an alliance entry, which can be either a good or a bad

outcome depending on the market complementarity in the new alliance. A plot (available on request) of

the change in complementarity against the complementarity in the alliance from which the firm withdrew

indicates that the majority of these events raise market complementarity, and the increase is particularly

large if the complementarity was originally low. This suggests that a majority of change events were

made by firms that were able to enter new alliances, and a majority of these firms entered new alliances

that improved their market complementarity.

We conducted several robustness checks. First, for firms in multi-partner alliances, the main

analysis (conservatively) set the threshold equal to the lowest complementarity with another firm in the

alliance. We also performed analyses using the highest complementarity as the threshold, and these did

not alter the findings. Second, we estimated all coefficients for outside options testing Hypotheses 2

through 5 in models with no other outside option variables present in order to check whether the results

were affected by correlations among these covariates. The only difference concerns Model 6, where

outside options in a rival alliance are marginally significant when entered without outside options not in a

rival alliance, while outside options not in a rival alliance have a higher (p < .01) significance level when

entered without outside options not in a rival alliance. Hence, collinearity appears to reduce significance

27

levels slightly in one model; in the others we found no differences. Finally, using Model 2, we considered

interactions of option-driven and failure-driven withdrawals to examine the possibility that outside

options were more salient to firms in alliances with a poor resource fit. Interactions of outside options and

the three resource compatibility variables were all insignificant, and the main effects of outside options

remained positive and significant. Hence, the effects of outside option and internal fit appear to operate

independently.

Discussion and Conclusion

This study sought to theorize and test firm withdrawal from alliances as a function of the ecology of

outside options. Alliance networks exert a strong influence on the performance and behavior of firms in

networks, but prior work disproportionately stresses the inertia of alliance networks that results from

embeddedness, limiting interest in research on firm withdrawals from alliances for instrumental reasons.

Using matching theory, we argue that a firm‟s alliance withdrawal is more likely when the density of

outside options that have better match quality than the current partners is higher. Our analysis of the

alliance data in global liner shipping industry supports this prediction.

The evidence thus supports our central claim that outside options matter for the stability of

interfirm relations. This is an important claim because it points toward a theory of alliance dynamics that

takes withdrawal as seriously as formation, and that views alliance withdrawals as resulting from either

successful identification of a superior partner or failure of the alliance to produce the intended results. At

the micro level, this suggests that information on firms‟ ecology of outside options helps in understanding

withdrawal decisions. At the macro level, this suggests that a highly dynamic network is not necessarily

one in which failed cooperation is rampant; it may instead be one in which outside options are readily

available. Movement into new alliances and out of old ones can be seen as steps in a firm‟s alliance career,

just as job moves are seen as steps in the careers of individual workers.

In order to do further testing of the matching theory that underlies our outside options hypotheses,

we also partitioned the outside options set by three different criteria, each of which represented one way

28

to examine the question of which matches would make sense from both firms instead of just from the

focal firm. For each of these tests, it would be possible to make the hypothesis also from the focal-firm

point of view and include an assumption that it would be able to assess the potential partner‟s preferences.

However, the hypotheses follow readily by examining what factors a potential partner would take into

account if it were approached by the focal firm and asked about its willingness to join a new alliance,

which is a plausible process that requires less foresight from the actors. From that point of view, we

derived theory on how newly gained complementarity would be more attractive than existing

complementarity (i.e., renewal), how structurally embedded firms would be less likely to break their

current alliances to join the firm (i.e., embeddedness), and how firms could take into account the reactions

of their current alliance partners (i.e., rivalry). Correlations between these three criteria, and especially

the last two, prevent a full analysis of which ones are most influential, but the models indicate a role of

alliance renewal as an attraction of the focal firm, while structural embeddedness makes potential alliance

partners unavailable. These findings are new, and the predictions are distinct to a matching theory of

alliance formation and withdrawal.

The theory and evidence presented here builds on current alliance research, but also departs from

it in important ways. First, by considering outside options as a driver of alliance withdrawal, it presents a

correction of the view that alliances are over-embedded due to a lack of concern for better outside options.

While embeddedness is supported by evidence and underpinned by the reasonable theoretical assumption

that familiar partners have advantages over unfamiliar ones (e.g., Chung et al. 2000, Goerzen 2007,

Sorenson and Waguespack 2006, Uzzi 1997), it does not imply that outside options are ignored. Indeed,

through the theoretical emphasis on a preference for known alliance partners and empirical focus on new

alliances, past research has not tested the ability of attractive outside opportunities to break the known

sources of inertia in alliance relations, leading to alliance withdrawals. For future research on alliance

changes it is important to consider both of these considerations: alliance partner familiarity may create a

threshold against seeking out new alliances and partners, but outside options that are attractive enough

will be chosen as new alliance partners.

29

Second, by considering a task-based source of outside option quality through market

complementarity, this work reinstates some of the balance between the task-based and socially based

origins of alliances that was present in early research (e.g., Gulati and Gargiulo 1999), but has lately

become overshadowed by the more socialized view of alliances seen in over-embeddedness research. A

merit of a more balanced view of task and social origins of alliances is a solution to the problem of

infinite regress posed by a purely social view of alliances. Knowing that network positions at time t-1

influence tie creation at time t does not help us understand how actors obtain positions in networks at time

t-1, so some other theory that does not resort to embeddedness is needed to understand the origins of

alliances (Stuart 2007). Scholars have just begun to work on this problem, but early efforts such as Hallen

(2008) also invoke embeddedness by examining how new startups‟ ties emerge from ties that their

founders had in their past businesses. Our approach to this problem is new: by extending matching theory

to alliance withdrawals, we present an explanation that decouples network dynamics from embeddedness.

While our finding on the alliances of potential outside options shows an effect of existing networks even

when outside options is the main motivation of withdrawal, complementarity of market positions has now

been established as an outside-network reason for alliance changes.

Moreover, and as a combination of the first two issues, we do not view alliance withdrawal as

necessarily resulting from a failure to put in place the social conditions needed for trustful exchange. Of

course, some alliances do break apart from internal frictions such as reduced individual attachment

(Broschak 2004, Seabright et al. 1992), competitive rivalry (Baker et al. 1998), power inequality (Rowley

et al. 2005), or resource incompatibility (Greve et al. 2010). This study controls for the internal cohesion

and friction of alliances, and also shows the role of outside options for alliance withdrawal and network

dynamics, thus demonstrating that alliance withdrawals can be a normal career progression of a firm

replacing current partners with better ones.

Our basic finding is that outside options with higher market complementarity than current

alliance partners increase firms‟ rates of alliance withdrawal. This finding has several managerial

implications. First, it shows the key role of broad market coverage in the liner shipping industry, and the

30

ability of firms‟ managers to assess potential partners on this criterion, despite their bounded rationality.

Given customer needs and the availability of information on firms‟ market coverage, this is a sensible

finding. Second, it is a very precise finding that not only explains when a given alliance is likely to

experience withdrawal, but also which firms are likely to leave the alliance – the ones with lower

complementarity in the alliance than with outsiders. The finding thus gives a useful metric for evaluating

the vulnerability of an alliance to withdrawals. This is especially critical to dyadic alliances, which are

the most common form in our data, because withdrawal is most consequential in dyadic alliances.

Assuming equal allocation of assets to an alliance, partner withdrawal from a dyadic alliance implies a

halving of the available ships, and similarly route frequency, unless the firm left behind finds a new

partner or allocates additional ships to the route. Such withdrawal may thus trigger route failure if the firm

left behind is unable to provide sufficiently frequent service with its own resources. In our analysis, such

an event would not be considered a withdrawal (which we distinguish from failures), but this is just a

conservative analytical strategy. In reality, partner withdrawal can lead to failure of dyadic alliances.

Our study has some limitations that need to be rectified through further work. First, the shipping

industry may have been especially well-suited for testing a theory of matching on observable

characteristics given the broad availability on information on market coverage. Other industries may have

more difficult access to information relevant to match quality, which would suggest a stronger need for

the theory of match uncertainty. Similarly, our findings may be less applicable to contexts where firms

exchange or pool intangible assets. In such alliances, the value that firms can derive from alliances is

determined by the commitments and resource allocations that partners are likely unable to specify prior to

alliance formation. The likelihood that prospective partners will actually provide the expected resources is

lower in such alliances unless cohesion has produced ability to evaluate each other and trust in

commitments. This speculation is consistent with insights from previous research that reports alliance

stability (e.g., Larson, 1992). Again, the theory of match uncertainty is helpful, and also the theory of

current-partner preference has greater predictive power in such contexts.

Shipping alliances that sell to industrial customers may also tend to be more dynamic than

31

alliances in industries that sell directly to consumers, and thus face reputational constraints. Although the

industrial customers of shipping firms are concerned with measurable outcomes such as speed and

breakage, they are less concerned with brands because, unlike airline passengers, for instance, the

container doesn‟t know or care about the livery of the ship that carries it. Nor will the customer

necessarily know, because the container is reloaded in port and delivered by a land transport contractor.

Hence, future research should explore effects of outside options in other industrial contexts in which the

different logics of competitive advantage exist. Still other contextual factors such as contracting practices

and norms may also affect withdrawals, further reinforcing a need for cross-industry comparison.

Second, while we provide insight into option-driven withdrawal, firms‟ post-withdrawal behavior

also requires investigation because it seems reasonable to expect firms‟ post-withdrawal behavior to differ

following failure- and option-driven withdrawal. A better understanding of factors shaping firms‟ post-

withdrawal choices seems critical to a comprehensive model of interfirm network dynamics.

Third, this study has assumed a fairly simple set of strategic moves for each firm. The predictions

assume that a firm leaves the alliance if it has more outside options, not that it may stay in current

alliances and instead leverage its greater bargaining power. Nor does the theory assume that the firm with

more outside options could drive its partners to withdraw in order to avoid being exploited, or that they

might cooperate to create a joint incentive for it to stay. The assumption of withdrawal in response to

better opportunities outside is more parsimonious than these complex strategic behaviors and appears to

fit the data well, but an investigation of how firms benefit from their alliances may need to consider

effects of outside options on the sharing of rewards.

Finally, although matching theory assumes opportunistic and well-informed decision makers, it is

important to acknowledge that they are also boundedly rational. It is more accurate to view a firm‟s

consideration set of outside options at a given point in time as an outcome of managers‟ search efforts

than to treat it as given. Although construction of the consideration set can be viewed as a rational process

based on the assessment of complementarities, cognitive biases may lead decision makers to construct

outside option sets that are incomplete, erroneous, or even superstitious (Levitt and March 1988). Little is

32

known about how boundedly rational managers choose criteria such as complementarity to find better

outside options and how cognitive factors constrain managers‟ constructions of consideration sets and

their reactions to changes in the availability of outside options in the consideration set. Understanding the

role of outside options in alliance withdrawal may thus require greater attention to factors influencing a

decision makers‟ view of those options.

The study opens the way for a number of promising lines of research. First, it is possible to

explore other dimensions of firm complementarity. Our use of market complementarity for the empirical

investigation matches our empirical context well and yielded good predictive power, but alternative

criteria such as technological complementarity also deserve consideration because of their demonstrated

effects in other industries (Rothaermel and Boeker 2008). Complementarity is a central reason for alliance

formation and deserves the bulk of research attention, but it can potentially be defined along a number of

dimensions.

Second, while complementarity is clearly crucial for the task-based rationale of alliances, a

matching theory of alliances is not complete without consideration of other task characteristics that

influence match quality. Groups of firms may also differ in the compatibility of resources that they can

bring to bear on the task solved by the alliance, and this in turn will affect the degree to which they can

exploit the complementarities. Thus, compatibility is also important from a matching perspective, as

shown earlier for alliance initiation (Mitsuhashi and Greve 2009), and it follows that compatibility has a

potential effect on withdrawal from alliances. As with complementarity, compatibility can be defined

along a number of dimensions ranging from production assets to firm cultures. We investigated

complementarity in this paper because we assumed that its potentially greater range would produce a

stronger effect, but this assumption would be worth testing in later work.

Third, alliance formation is also affected by a set of social dimensions that include network

embeddedness, interpersonal trust, and language and cultural compatibility, and status homophily

(Podolny 1994). Although we expect alliance withdrawal in response to outside opportunities to be

mainly driven by task-based rather social dimensions of the alliance, it is worthwhile examining the social

33

dimensions as well. It seems particularly important to investigate possible links between task based and

social dimensions, such as when status homophily is needed to preserve the competitive standing of each

firm in the alliance (Podolny 1994) or trust is needed for open exchange of information in technology

development (Powell et al. 2005).

While there is much to learn from this early study of option-driven alliance withdrawal, many

important questions remain. We hope our work, which emphasizes outside options over internal tensions,

inspires others engaged in the growing stream of work on alliance withdrawal to consider issues beyond

sources of cohesion and friction linked to failures in collaboration, and in particular the ecology of firms‟

outside options.

34

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39

Table 1 Selectivity Model of Route Failure

New route 0.840 (0.226) **

Route tenure -0.074 (0.047)

Route density in area -0.022 (0.007) **

Route density in area, squared 0.009 (0.002) **

Ship age (alliance) -0.038 (0.021) +

Ship speed (alliance) 0.018 (0.031)

Ship size, DWT (alliance) -5.330E-06 (3.130E-06) +

Container ship age (alliance) -0.073 (0.022) **

Container ship speed (alliance) 0.013 (0.033)

Container ship size, TEU (alliance) -5.100E-05 (7.370E-05)

N. of container ships (alliance) 0.016 (0.005) **

Log frequency 0.037 (0.118)

Number of members -0.085 (0.052) +

Feeder route 0.041 (0.103)

Proportion members with no container ships 0.903 (0.252) **

Number of alliance ties (alliance) -0.004 (0.004)

Number of arm's length ties (alliance) -0.011 (0.057)

N. of ships (alliance) -0.006 (0.002) *

New alliance 0.318 (0.327)

Alliance tenure 0.632 (0.179) **

N. of ships (firm) 1.149E-04 (0.001)

No container ship (firm) 0.061 (0.124)

Firm TEU growth -0.805 (0.536)

Withdrawals in area -0.043 (0.008) **

Number of alliance ties (firm) -0.002 (0.005)

Number of arm's length ties (firm) 0.017 (0.013)

Ship age difference -0.004 (0.018)

Ship speed difference -0.045 (0.031)

Ship TEU difference 0.054 (0.059)

Closed triads 0.170 (0.071) *

Constraint difference -0.037 (0.171)

Multimarket contact 0.134 (0.128)

Market overlap -0.001 (0.001)

Constant 142.391 (44.024) ***

Log likelihood -2311.756

LR χ2 572.50

Degrees of freedom 60

Note: + p<.10; *p< .05; **p< .01; standard errors in parentheses. Fixed effects for years and market areas

are included in the model but not displayed in the table. Note that route tenure starts when the route is

initiated, while alliance tenure starts when the current members of the alliance starts operating the route.

Alliance tenure can thus be less than route tenure if the alliance composition changes.

40

Table 2 Descriptive statistics and correlation coefficients

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

1 New alliance .40 .49 1

2 Alliance tenure 2.76 .87 -.90 1

3 Log frequency 2.10 .40 -.03 .02 1

4 Number of members 3.18 1.43 .16 .09 -.17 1

5 Number of arm's length ties .21 .61 .09 .00 -.06 .02 1

6 Feeder route .20 .40 .03 -.12 -.01 .02 .05 1

7 Firm fleet size 33.69 54.52 .02 -.07 -.02 .07 -.06 -.01 1

8 No container ships .30 .46 -.09 .11 .07 -.12 .01 -.05 -.38

9 Firm TEU growth .02 .07 .01 -.04 .01 .02 .01 .02 .07

10 Withdrawals in area 5.49 6.55 -.02 -.18 .03 -.17 -.02 -.09 .00

11 Number of alliance ties 14.12 13.97 .14 -.07 -.14 .51 -.09 -.13 .10

12 Number of arm's length ties 1.77 3.82 .05 -.06 -.07 .34 .03 -.01 .22

13 Ship age difference 2.39 2.77 .04 -.01 -.02 .06 -.05 .06 -.07

14 Ship speed difference 1.74 1.75 -.03 .03 .02 -.04 -.05 -.07 -.05

15 Ship TEU difference .89 .77 .03 .02 -.05 .08 -.06 -.05 -.07

16 Closed triads 1.57 1.21 .24 .13 -.23 .75 .00 -.04 .17

17 Constraint difference .00 .22 .00 .00 .00 .00 .00 .00 .12

18 Multimarket contact .49 .32 .01 -.16 -.02 .03 -.06 -.04 .17

19 Market overlap 57.69 37.21 -.08 -.23 .10 -.19 .01 -.01 -.06

20 Outside options 5.61 7.49 -.28 -.18 .31 .01 -.09 -.04 .07

21 ..alliance in different market .96 1.62 -.14 -.05 .19 .33 -.05 -.04 .14

22 ..no alliance in different market 4.64 6.68 -.28 -.19 .30 -.07 -.08 -.03 .04

23 ..not current alliance but past alliance .36 .85 -.10 -.12 .13 .04 -.06 -.04 .05

24 ..no current and past alliance 4.29 6.37 -.28 -.19 .30 -.08 -.08 -.03 .04

25 ..clustered -3.73 5.01 .25 .17 -.29 -.06 .07 .04 -.08

26 ..not clustered 9.33 12.21 -.27 -.18 .31 .03 -.08 -.04 .08

27 ..is in rival alliance 3.75 5.21 -.24 -.17 .28 .05 -.08 -.03 .10

28 ..is not in rival alliance 1.86 3.37 -.24 -.14 .26 -.06 -.07 -.03 .00

41

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

8 1

9 -.17 1

10 -.02 .02 1

11 -.23 .08 -.05 1

12 -.14 -.01 -.03 .16 1

13 .10 -.05 -.03 -.04 -.03 1

14 .34 -.08 .04 -.14 .01 .15 1

15 .20 -.09 .02 .03 .06 .19 .29 1

16 -.31 .06 -.13 .64 .32 -.02 -.12 .13 1

17 -.18 .04 .00 .02 .00 -.03 -.03 -.09 .05 1

18 -.25 .07 .05 .23 .10 -.10 -.07 .08 .17 .06 1

19 .11 -.04 .30 -.15 .03 -.07 .18 .02 -.27 -.10 -.06 1

20 -.06 .02 .19 .11 .09 -.03 .01 .03 .06 .02 .08 .25 1

21 -.19 .04 .00 .33 .17 .00 -.06 .04 .41 .06 .09 -.02 .58 1

22 -.02 .01 .21 .04 .06 -.03 .03 .02 -.03 .01 .07 .28 .98 .41 1

23 -.11 .03 -.03 .21 .08 -.04 -.04 .08 .14 -.01 .10 .01 .45 .39 .42 1

24 -.01 .00 .22 .02 .05 -.03 .03 .01 -.05 .01 .06 .29 .97 .38 .99 .30

25 .08 -.01 -.15 -.15 -.12 .01 -.02 -.02 -.10 -.01 -.10 -.29 -.91 -.66 -.86 -.50

26 -.07 .02 .18 .13 .10 -.02 .01 .03 .08 .02 .09 .27 .98 .63 .95 .48

27 -.10 .02 .18 .11 .11 -.01 .01 .03 .10 .02 .11 .26 .92 .64 .88 .48

28 .03 .00 .13 .08 .02 -.05 .01 .02 -.03 .02 .01 .14 .80 .30 .82 .27

24 25 26 27 28

24 1

25 -.83 1

26 .93 -.97 1

27 .85 -.97 .96 1

28 .83 -.51 .70 .50 1

Note: Cells marked in grey are correlations of variables that are not entered in the same model

because they are alternative partitions of the outside option set.

42

Table 3 Complementary Log-log Models of Member Withdrawals

Model (1) (2) (3) (4) (5) (6)

New alliance -0.346 -0.269 -0.228 -0.215 -0.200 -0.255

(0.290) (0.293) (0.294) (0.294) (0.293) (0.294)

Alliance tenure 0.320 0.345 0.341 0.331 0.381 0.370

(0.240) (0.241) (0.243) (0.243) (0.245) (0.243)

Log frequency -0.912+ -0.878+ -0.718 -0.714 -0.657 -0.819

(0.510) (0.507) (0.516) (0.516) (0.509) (0.510)

Number of members 0.260* 0.269* 0.287* 0.281* 0.289* 0.272*

(0.109) (0.111) (0.112) (0.112) (0.113) (0.111)

Number of arm‟s length ties -0.067 -0.057 -0.057 -0.051 -0.051 -0.060

(0.163) (0.164) (0.166) (0.165) (0.168) (0.166)

Feeder route 0.976** 0.959** 0.948** 0.947** 0.968** 0.950**

(0.223) (0.224) (0.224) (0.225) (0.226) (0.225)

Firm fleet size -0.010 -0.010 -0.010 -0.010 -0.012 -0.011

(0.009) (0.009) (0.009) (0.009) (0.009) (0.009)

No container ships 0.586 0.653 0.658 0.662 0.656 0.644

(0.444) (0.450) (0.454) (0.457) (0.469) (0.458)

Firm TEU growth -3.540* -3.469+ -3.564+ -3.528+ -3.974* -3.743*

(1.796) (1.827) (1.819) (1.834) (1.874) (1.830)

Withdrawals in area 0.009 0.002 -0.000 -0.001 -0.003 0.000

(0.016) (0.017) (0.017) (0.017) (0.018) (0.017)

Inverse Mills Ratio 1.336* 1.693** 1.878** 1.886** 1.958** 1.793**

(0.525) (0.552) (0.567) (0.567) (0.561) (0.556)

Number of alliance ties -0.023+ -0.023+ -0.026+ -0.025+ -0.024+ -0.025+

(0.013) (0.013) (0.014) (0.014) (0.014) (0.013)

Number of arm‟s length ties -0.126* -0.129* -0.135* -0.136* -0.131* -0.122*

(0.055) (0.054) (0.055) (0.056) (0.055) (0.054)

Ship age difference -0.013 -0.015 -0.012 -0.015 -0.012 -0.012

(0.043) (0.043) (0.043) (0.044) (0.044) (0.044)

Ship speed difference -0.182* -0.184* -0.184* -0.186* -0.199* -0.187*

(0.083) (0.083) (0.084) (0.084) (0.085) (0.083)

Ship TEU difference 0.468** 0.453** 0.453** 0.455** 0.421** 0.440**

(0.156) (0.158) (0.160) (0.160) (0.161) (0.159)

Closed triads 0.502* 0.427* 0.477* 0.484* 0.452* 0.442*

(0.199) (0.205) (0.207) (0.208) (0.207) (0.206)

Constraint difference 2.793** 2.771** 2.730** 2.692** 2.766** 2.767**

(0.549) (0.552) (0.556) (0.557) (0.560) (0.556)

Multimarket contact -0.716+ -0.716 -0.713 -0.713 -0.656 -0.672

(0.434) (0.438) (0.445) (0.446) (0.455) (0.445)

Market overlap 0.013** 0.013** 0.012** 0.012** 0.014** 0.013**

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

Outside options 0.037*

(0.017)

Outside options, alliance -0.088 -0.089

in different market (0.073) (0.073)

Outside options, no alliance 0.056**

in different market (0.019)

Outside options, not current -0.104

alliance but past alliance (0.147)

Outside options, no current 0.065**

43

and past alliance (0.020)

Outside options, clustered -0.043

(0.033)

Outside options, not 0.121**

clustered (0.031)

Outside options, is in rival -0.000

alliance (0.030)

Outside options, is not in 0.090*

rival alliance (0.037)

Constant -8.171** -9.015** -9.483** -9.492** -9.562** -9.111**

(1.616) (1.682) (1.720) (1.726) (1.717) (1.692)

Log likelihood -481.21 -478.82 -477.15 -476.51 -474.62 -477.67

LR χ2 against null 145.57** 146.60** 147.75** 147.54** 150.31** 148.35**

Degrees of freedom 31 32 33 34 33 33

LR χ2 against Model 2 3.34+ 4.62+ 8.40** 2.30

Degrees of freedom 1 2 1 1

Note: + p<.10; *p< .05; **p< .01; standard errors in parentheses; the sample includes 4,002 observations on 169 firms. Fixed effects for years are included in the model but not displayed in the table.

Figure 1 Alliance Network in 2001

Figure 2 Magnitude of Outside Option Effects

1

1.5

2

2.5

3

0 Mean Mean+2.5 SD

Mult

iplier

Outside options (H1)

... not in alliance with rival (H5)

... no alliance current and past (H3)

... no alliance in different market

(H2)

... not in cluster (H4)

46

46

Figure 3 Magnitude of Effects Relative to the Baseline Outside Option

0.9

1

1.1

1.2

1.3

1.4

1.5

0 Mean Mean+2.5 SD

Mult

iplier

rel

ativ

e to

the

bas

elin

e m

ain e

ffec

t

Outside options (H1)

...no alliance in different market (H2)

...no alliance current and past (H3)

...not in cluster (H4)

...not in alliance with rival (H5)