PARTNER SELECTION IN INTERNATIONAL TECHNOLOGICAL ALLIANCES:

THE ROLE OF INSTITUTIONAL DISTANCE, COLONIAL AND ECONOMIC TIES

Sorin M.S. Krammer

University of Groningen

Faculty of Economics and Business

Department of Global Economics and Management

Nettelbosje 2, 9747 AE Groningen

The Netherlands

Email: M.S.S.Krammer@rug.nl

Tel: +31 (0) 50 363 3713

Fax: +31 (0) 50 363 7337

Web: http://www.rug.nl/staff/m.s.s.krammer

ABSTRACT

Careful selection of partners is a prerequisite for a successful alliance. I posit that institutional distance

influences negatively partner selection in international technological alliances beyond firm-specifics.

Furthermore, I argue that the effect of institutional distance on partner selection is moderated by the

extent of colonial and economic ties between home-countries of firms. Empirical results based on a

longitudinal dataset of firms in the global tire industry confirm that MNEs prefer alliance partners

from closer cognitive-normative environments, and from similar or stronger regulatory ones.

Moreover, colonial and economic ties between nations moderate positively this relationship. These

findings offer a more comprehensive view of partner selection decisions in an international context,

attesting the role of institutions and their interplay with historical and economic contexts of countries.

Keywords: Technological alliances; Cultural values; Managerial norms; Intellectual Property

Rights; Colonial ties; Economic ties.

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INTRODUCTION

Technological alliances have become a popular strategy to cope with competitive pressures, short

product life‐cycles, high R&D costs, and entry barriers (Teece, 1986; Gulati, 1995a; Anand and

Khanna, 2000; Rothaermel and Boeker, 2008). As a result, firms balance explorative and

exploitative alliances (Lavie and Rosenkopf, 2006) to access complementary technologies

(Prahalad and Hamel, 1990), avoid uncertainty (Burgers, Hill and Kim, 1993), acquire knowledge

(Hoang and Rothaermel, 2010), enter new markets (García-Canal, Valdés-Llaneza, and Sánchez-

Lorda, 2008), improve performance (Yamakawa, Yang and Lin, 2011), and preserve leadership

(Mortehan, 2004). However, as firms rush to leverage these benefits, they often ignore potential

losses from alliance mismanagement (Ireland, Hitt and Vaidyanath, 2002) that ultimately result in

high failure rates (Park and Ungson, 1997; Kale, Dyer and Singh, 2002). To avoid such

outcomes, firms must carefully select their partners (Hitt et al., 2000; Shah and Swaminathan,

2008), especially in international settings (Dacin, Hitt and Levitas, 1997; Dong and Glaister,

2006).

Combining elements from transaction costs economics and resource-based theory, prior

studies show that successful selection of alliance partners depends on the complementarity

between them in terms of characteristics and resources (Hitt et al., 2000; Hitt et al., 2004;

Rothaermel and Boeker, 2008). Others suggest that, despite this need for complementarity,

partners must share compatible skills, routines, and strategies for the alliance to function well

(Dacin et al., 1997; Glaister, 1996). Besides the individual characteristics of partnering firms,

alliances are also subject to agency problems arising from misalignment of partners’ goals

(Einsenhardt, 1989), separation of ownership and control mechanisms (Reuer and Ragozzino,

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2006) and project-specific behavior of partners (Shah and Swaminathan, 2008). Thus, the

uncertainty firms face (Beckman, Haunschild and Philips, 2004), level of mutual trust (Gulati,

1995a; Anand and Khanna, 2000), social and strategic interdependence (Gulati, 1995b) and prior

commitments (Mohr and Spekman, 1994) have important implications for the selections of

partners. However, among all the above aspects, complementarity, compatibility, and

commitment of partners have been most consistently associated with positive selection outcomes

and successful alliances (Kale and Singh, 2009).

In addition to these firm-specific factors identified by previous studies, the selection of

alliance partners in an international context needs to overcome idiosyncratic differences between

countries stemming from economic, political, legislative, and social factors (Hitt et al., 2000;

Parkhe, 2003). Firm behavior does not occur in an organizational vacuum (Dacin, Ventresca and

Beal, 1999) but is rather nested in the institutional environment in which it operates (Brouthers

and Brouthers, 2000; Meyer et al., 2009). As a result, institutional distance between home and

host country plays an important role in determining different elements of MNE strategy, such as

entry modes (Lu, 2002), staffing (Gaur, Delios and Singh, 2007), inter-firm collaborations (Park

and Ungson, 1997) or export activities (He, Brouthers and Filatotchev, 2013). Overall, these

studies suggest that institutional distance between countries is an important determinant of MNE

strategy and subsequently, its performance (Kostova and Zaheer, 1999).

Given the existing institutional heterogeneity worldwide (Hitt et al., 2004; Meyer et al.,

2009), it is important to understand how institutional distance affects MNE’s selection of

strategic partners (Hitt et al., 2005). With few notable exceptions, this issue has received little

attention in the literature, and existing research has been confined to examination of a few

countries or single institutional dimensions. For example, Hitt et al. (2000) identify significant

differences in competences sought from foreign partners between firms from emerging (i.e.,

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financial resources, assets, technologies) and developed economies (i.e., unique competences,

local market knowledge). Moreover, Hitt et al. (2004) point out that institutional differences are

important even among emerging economies themselves, and contrast their effects on the

partnering preferences of Chinese versus Russian managers. In a related vein, Roy and Oliver

(2009) show that the selection of foreign partners is contingent on regulatory aspects and

perceptions of the host-countries’ environments, such as rule of law or corruption. Finally, Shi et

al. (2012) suggest that sub-national institutions are equally important in the quest for international

partners.

In this study, I explore alliance partner selection through the lens of institutional theory

(Kostova, 1999) supplemented with elements from transaction-costs economics (Gulati and

Singh, 1998) and make several contributions to the extant literature. First, I develop theoretical

arguments regarding the role of institutional distance on partner selection in technological

alliances, complementing the role of firm‐specifics which have been detailed by previous studies

(Shah and Swaminathan, 2008). Once a firm identifies a pool of possible international partners

that are committed, compatible and complementary, it must decide which one(s) to actually

partner with (Gulati and Singh, 1998). Such decisions involve transfers of technology and are

sensitive to differences in normative (Steensma et al., 2000), regulatory (Oxley, 1999) and

cognitive (Kelly, Schaan and Joncas, 2002) traits of partners, which entail additional monitoring,

adaptation and appropriability costs. Building on institutional and transaction-costs arguments, I

argue that MNEs will prefer partners from closer cognitive-normative environments, and from

similar or stronger regulatory ones.

Second, I propose that colonial and economic ties between countries provide important

insights on the global historical (Makino and Tsang, 2010) and economic context (Alhorr et al.,

2008) with important consequences for all international interactions, including inter-firm

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technological alliances. Apart from their perennial ramifications on economic performance

(Acemoglu, Johnson and Robinson, 2001), trade flows (Head, Mayer and Ries, 2010),

development levels (Feyrer and Sacerdote, 2009) and inward FDI (Makino and Tsang, 2010),

colonial ties also affect MNE activities by shaping competitive advantage (Frynas, Mellahi and

Pigman, 2006), supporting legitimacy claims (Jones, 1996) and prompting different ownership

strategies (Chakrabarty, 2009). Likewise, bilateral (e.g., EIAs- economic integration agreements)

and multilateral (e.g., membership in the WTO- the World Trade Organization, or its precursor

the GATT) economic ties between countries stimulate trade and FDI flows (Baier et al., 2008),

and influence international strategies of MNEs (Frantianni and Oh, 2009). I expand this literature

and argue that colonial and economic ties between nations will positively moderate the effect of

institutional distance in the process of partner selection, by reducing indirectly MNE coordination

and appropriation concerns regarding institutionally-distant partners.

These hypotheses are tested using a hand‐collected dataset that covers all firms in the

global tire industry between 1985 and 2003. I examine the selection of international partners for

technological alliances with both exploration and exploitation rationales. Following previous

studies, I focus on horizontal dimension of these agreements (Mowery et al., 2002; Lavie and

Rosenkopf, 2006) for greater theoretical clarity (Phelps, 2010) and consistency with the

particularities of the tire industry (Acha and Brusoni, 2005), in which technological alliances are

occurring almost exclusively between tire producers. My results indicate that institutional

distance (across cognitive, normative, and regulatory elements) between home countries of the

MNE and its potential partners has a negative effect on the propensity of being selected as a

partner for a technological alliance. In terms of moderation, the extent of historical colonial ties

moderates the negative effects of cognitive and normative distances highlighting the role of

colonial relationships in reducing uncertainty and risks of prospective partners from these

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countries. Likewise, the existence of bilateral (i.e., EIAs) and multilateral (i.e., membership in

GATT/WTO) economic ties moderate positively the effects of institutional distance between

partners through profound knowledge of each other cognitive and normative backgrounds.

Following these findings, I discuss their implications for institutional theory and the alliance

literature in general, as well as practical ramifications for managers and policymakers.

THEORY AND HYPOTHESES

Institutional distance and selection of international partners

A central issue for alliance formation is the quest for a suitable partner (Gulati, 1995a; Gulati,

1995b; Hitt et al., 2000). Surveys confirm that most managers consider partner selection as the

most important factor for alliance success, and one that should be continuously perfected by firms

to improve the success of their alliances (Glaister, 1996). While a good selection procedure

involves a careful screening and commitment of substantial time and resources by the firm, it

pays significant dividends by shaping knowledge, resource and skills available in the alliance,

and firms’ ability to meet their strategic objectives (Geringer, 1991).

While firm-specific factors (e.g., complementarity, compatibility, trust, strategic

interdependence) are some the most salient firm-specific explanations for the successful selection

of alliance partners, in international interactions, country-specific factors (e.g., differences in

culture, economic development, governmental policies) exacerbate any mismatch between

potential partners (Dacin et al., 1997; Parkhe, 2003; Dong and Glaister, 2006). However, among

all these attributes, institutional factors have been found to be particularly relevant for MNE

strategies (Brouthers and Brouthers, 2000; Kostova and Zaheer, 1999; Meyer et al., 2009).

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Differences in institutional arrangements across countries can both benefit and impede

MNE activities abroad. Often these differences present opportunities for institutional arbitrage,

encouraging exploitation of different capabilities across countries (Gaur and Lu, 2007). On the

other hand, institutional differences contribute to the liability of foreignness faced by the MNEs

in the form of unfamiliarity, relational and discriminatory hazards that require additional costs of

adaptation to host-country environments (Eden and Miller, 2004). As a result, institutional

distance between home and host countries is indicative of MNE’s strategies in different host

markets (Henisz and Swaminathan, 2008).

Concerning partner selection, the impact of institutional distance has received relatively

little attention, and existing contributions have been confined to few countries and single

institutional dimensions (Hitt et al. 2000; Hitt et al. 2004; Roy and Oliver 2009; Shi et al 2012).

Nevertheless, institutional complexity requires a complete examination of all dimensions (i.e.,

cognitive, normative and regulatory) given their potentially different effects and magnitudes on

various MNE activities. Therefore, by focusing on the three pillars of institutional distance

proposed by Kostova (1999) –i.e., cognitive, normative and regulatory-, I am able to examine

comprehensively how different institutional aspects affect partner selection, and which one is the

most salient for MNEs seeking international partners for technological alliances.

The normative and cognitive elements of the institutions are tacit and informal aspects that

describe how societal actors operate in a certain environment (Gaur et al., 2007). Normative

aspects describe legitimate ways to achieve goals (Scott, 2001), while cognitive aspects reflect

different schemas, frames, beliefs and inferences on how the world operates (Kostova, 1999).

Both represent institutional aspects that are embedded in the normal functioning of a society.

Thus, firms’ actions are highly representative of their home normative-cognitive environments

since they often abide and implement these rules unconsciously (He et al., 2013).

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However, given their similarity and overlap, distinguishing between cognitive and

normative aspects of institutions in practice is a challenging task (Scott 2001). The cognitive

dimension is commonly identified with cultural values and beliefs (Estrin et al., 2009; Bae and

Salomon, 2010; He et al., 2013). Defined as the “collective programming of the mind” that

identifies different groups of people, national culture provides a common frame for individuals to

relate to organizations, the environment, and their peers (Hofstede, 1980). In turn, the normative

dimension is closely linked to the development of professional standards and educational

curricula, and previous studies have conceptualized it using measures of managerial abilities,

norms and practice (Delios and Beamish, 1999; Xu et al., 2004; He et al., 2013). Thus, I focus on

the potential mismatch in terms of managerial norms and cultural values as two cognitive-

normative elements that are relevant for the selection of alliance partners. Moreover, I consider

cognitive (cultural) and normative (managerial) distance between partners to be symmetric and

dyadic in nature (Zaheer, Schomaker and Nachum, 2012).

I argue that MNEs will prefer international partners from closer cultural and managerial

backgrounds as partners for technological alliances for several reasons. First, managers face

significant coordination concerns upon establishing new alliances. These concerns stem from

increased interdependence between partners as a result of logistics of alliance activities (Gulati

and Singh, 1998) and include communication and decision costs that are contingent upon the

cognitive-normative mismatch of partners (Delerue and Simon 2009). Second, cultural and

managerial proximity is associated with intrinsic attractiveness and trust, which facilitate the

transfer of technology between alliance partners (Michailova and Hutchings, 2006). Technical

knowledge has a major tacit component and is often embedded in people and organizations

(Volkof, Strong and Elmes, 2007), thus trustful and attractive potential partners stand a better

chance of being selected as technological allies. Finally, successful assimilation of technology

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demands certain absorptive capabilities (i.e., trained employees, knowledgeable managers,

efficient routines, etc.) from potential collaborators (Mowery et al., 2002). Firms from closer

normative-cognitive backgrounds have similar cognitive schemas and managerial dispositions

regarding the use and share of technical knowledge, which will be easier to understand and adopt

by the MNE (Xu et al., 2004) facilitating also the absorption of technology by partnering firms

(Pisano, 1990). Therefore, they will be more appealing as alliance partners.

In contrast, firms from a distant managerial and cultural environment will be less

attractive as alliance partners, since the MNE will need to devote additional resources to

understand and deal effectively with these cognitive-normative differences (Chan and Makino,

2007). When dealing with distant managerial and cultural partners, the MNE needs to commit

additional resources to further develop their alliance capabilities (Kale and Sigh, 2002) and

bridge these differences (e.g., via new routines, work ethics, management training programs,

upgrades of technological skills, etc.) for the alliance to succeed. As a result, coordination costs

rise and impose greater uncertainty on both partners, which makes these alliances seem riskier

and costlier for the MNE (Gulati and Singh, 1998). Furthermore, cultural and managerial distance

leads to less cooperation and knowledge stickiness (Szulanski, 1996) impeding the proper flow of

technical knowledge between partners. Familiarity with local norms and behaviors is tacit and

requires additional efforts from the MNE to secure full cooperation from its partners and fluidize

the flows of knowledge in the alliance via common organizational routines (Kostova, 1999).

Finally, cognitive-normative differences reduce the absorptive capability of partners to transfer

and implement technology from the MNE successfully, given their different ways (e.g., cognitive

schemas and managerial dispositions) of sharing and using technical knowledge. Such normative-

cognitive differences can impose significant constraints on firms’ other activities and are a

leading cause of alliance failures (Park and Ungson, 1997; Mohr and Spekman, 1994). Therefore,

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when engaging a distant cognitive (cultural) and normative (managerial) partner, the MNE will

face higher adjustment costs and greater uncertainty regarding the alliance. Based on these

arguments, I expect a negative effect of the normative-cognitive distance on partnering decisions:

H1: All else equal, the MNE will prefer partners from closer normative and cognitive

environments when forming a technological alliance.

The regulatory component of the institutional environment includes codified rules that

govern economic interactions (North, 1990). These formal institutional components (i.e. laws,

regulations, policies) vary significantly across countries (Nachum, Zaheer and Gross, 2008), and

affect all inter-firm international interactions (Xu and Shenkar, 2002). For this paper, I focus on

one domain‐specific regulatory element that is particularly salient for technology and innovation,

namely intellectual property rights (IPR). IPR are exclusive rights given to a creator or inventor

over the use of it over a limited period of time. As a result, firms increasingly capitalize on their

technological assets via licensing in global technology markets that require strong and uniform

IPR regimes (Arora, Fosfuri and Gambardella, 2001). Thus, given their pivotal role in the

creation and exploitation of technology, I theorize that regulatory differences in terms of IPR will

influence partner selection in technological alliances.

Unlike the cognitive and normative elements of the institutional environment, regulatory

distance between home and MNE host country is asymmetric and the direction matters (Zaheer et

al., 2012). Albeit globalization and increased economic integration have contributed to the

convergence of regulations worldwide, countries still exhibit significant heterogeneity in terms of

IPR (Park, 2008). Some of these differences involve important IPR aspects such as legal

protection, actual enforcement, and coverage of these laws. By considering both the magnitude

and the direction of this relationship between countries, I emphasize both how, and how much

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they differ in terms of IPR regulations. In turn, the magnitude and direction of these IPR

differences may favor or hinder alliance activities across borders. Hence, upon considering a set

of potential alliance partners, the MNE can assess each of them as either having stronger (i.e.

positive distance) or weaker (i.e. negative distance) regulatory institutions vis-à-vis its home-

country environment.

An MNE seeking international partners for an exploitative technological alliance will

prefer firms from countries with similar or stronger IPR regulations for several reasons. First,

similar or stronger IPR regulations reduce MNE’s appropriation concerns, thereby improving

firm’s ability to capture greater rents from the alliance (Gulati and Singh, 1998) and benefit from

co-specialization of partners (Gimeno, 2004). Second, IPR facilitates the transfer of technology

between firms via specialized markets for technologies and employ licensing as a commercial

tool to exploit their knowledge-intensive assets (Arora et al., 2001). Finally, countries with

similar or superior IPR regimes than that of the MNE will allow a more efficient transfer of

technology given their compatible IPR standards (Gans, Hsu and Stern, 2008), which in turn will

increase MNE’s opportunities for exploitation and exploration via technological alliances (Lavie

and Rosenkopft, 2006).

In contrast, the MNE will avoid partners from countries with weaker IPR regulations

when forming a technological alliance, given the high appropriation concerns (Gulati and Singh,

1998), reduced benefits from co-specialization (Gimeno, 2004), and difficulties in the transfer

and commercialization of technologies (Branstetter et al., 2006). Within an alliance, the scope of

technical knowledge transferred to partnering firms is difficult to limit and monitor by the MNE,

demanding the development of costly alliance functions (Kale and Singh, 2002) and raising

concerns about free riders and technology leakages (Pisano, 1990). These concerns are further

inflated by weaker IPR regulations that facilitate technology leakages to domestic firms (Teece,

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1986) and lower MNE ability to capture rents from exploitation of its technological assets

(Khoury and Peng, 2011)1. Furthermore, worse IPR regulations than the home-country ones will

interfere with the MNE ability to transfer technology to its partners. In such cases, lower IPR

standards increase MNE’s perceived costs of sharing technology by raising incompatibility issues

in terms of dealing with intellectual property (Gans et al., 2008), and exposing them to threats of

imitation or expropriation (Martinez‐Noya and Garcia‐Canal, 2011). As a result, firms from

countries with worse regulatory environments compared to the MNE’s home institutions will be

less attractive as partners for a technological alliance (Contractor and Ra, 2002). Based on all

these arguments, my second hypothesis is:

H2: All else equal, the MNE will prefer partners from similar or stronger regulatory

environments when forming a technological alliance.

Moderating effects of colonial and economic ties

Colonial links between countries have been consistently linked with cross-border

economic activities. At the country-level, colonial ties are identified as an important predictor for

economic development (Feyrer and Sacerdote, 2009), international trade (Head et al., 2010) and

foreign direct investments (Makino and Tsang, 2010). Moreover, colonial ties are also important

from a firm perspective. They reduce the risks of investments, support MNE legitimacy claims in

host-countries (Jones, 1996), and create abiding first-mover advantages with important

consequences for market success (Frynas et al., 2006) and ownership strategies (Chakrabarty,

2009). As a result, colonial links may provide important “omitted insights” on international firm

1 For example, in 2009 the US International Trade Commission has estimated that American firms in China lost

around $48.2 billion because of such IPR violations.

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interactions, beyond the “usual suspects” discussed in the literature such as economic, geographic

or cultural factors (Makino and Tsang, 2010).

Subscribing to this idea, I propose that the extent of colonial ties will moderate the

negative effects of institutional distance on MNE’s selection of alliance partners for the following

reasons. First, the process of colonization creates a deep mutual knowledge of the cognitive and

normative characteristics between colonizer-colony pairs of otherwise dissimilar countries. This

knowledge of each other’s values and norms is mostly tacit and gets accrued over time (Makino

and Tsang, 2010) at different societal levels, including individuals, firms and governments

(Jones, 1996). Second, colonial ties stimulate economic exchanges through lower uncertainty,

coordination and transaction costs. In-depth knowledge of cognitive and normative elements of a

country, acquired through lengthy colonial relations, results in less uncertainty and clearer

expectations from the MNE regarding partnering firms, thereby facilitating the selection of

appropriate partners (Rangan and Segul, 2009). Finally, colonial ties moderate the effects of

institutional distance between countries through shared regulatory elements resulting from the

countries’ common legal traditions (Watson, 1974). Although countries with the same legal

traditions can still be very different in terms of institutions, lengthy colonial ties between them

ensure that they have a common regulatory base (La Porta et al., 2008). Such common base

provides familiarity and confidence to the MNE in the regulatory environment of this partner,

lowering the perceived appropriation concerns regarding a potential alliance. Therefore, in view

of all these arguments, I propose that:

H3: The extent of colonial ties between the home countries of the MNE and its prospective

partners will positively moderate the effect of institutional distance on partner selection for

technological alliances.

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Besides informal ties between countries (e.g., colonization, migration) that occur as a result

of random factors such as geographical proximity or historical events, formal ties are also

developed, as countries adopt intentionally certain preferential relationships (i.e., agreements,

treaties) to promote mutual interests (Makino and Tsang, 2010). These relations vary widely in

terms of focus (e.g., economic -European Union-, political -United Nations-, environmental -

Kyoto protocol-, or military – North Atlantic Treaty Organization-) and scope (i.e., bilateral or

multilateral), but they all share a codified structure and clear enforcement mechanisms that

warrant their uniform implementation across all signatory parties. In this study I focus on the

effects of formal ties of economic nature on the relationship between institutional distance and

selection of partners for technological alliances. Specifically I will examine the effect of

multilateral economic agreements (i.e., the World Trade Organization –WTO and its predecessor

GATT) and bilateral ones (i.e., economic integration agreements EIAs).

I argue that the extent of such economic ties between countries will moderate the effects

of institutional differences on alliance partner selection through several mechanisms. First,

economic ties will lower MNE’s appropriation concerns in an alliance through their formal

nature and their strict enforcement mechanisms. Formalized economic agreements between home

countries of prospective partners provide a significant buffer for appropriation concerns, as they

signal irreversible commitments at the national level to adopt and uphold international regulatory

standards. Moreover, breaches of these provisions are subject to severe penalties from economic

partners (in the case of bilateral EIAs) or the rest of the world (GATT/WTO). Second, the

codified nature of these agreements provides the MNE with better information on the regulatory

standards in home countries of partners, thereby reducing the informational asymmetry and

uncertainty it faces in these markets. For instance, both the GATT/WTO and many EIAs cover

aspects related to IPR (Kohl, 2013), implicitly reducing the gap between the legislative treatment

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(“de jure”) and the actual enforcement (“de facto”) of IPR laws in an international context. Third,

appropriation and coordination concerns are further escalated by nationalist views and security

constraints, which can interfere with otherwise sensible market transactions (Nigh, 1985).

Economic ties contribute towards forming favorable public opinions and sentiments towards

foreign governments and MNEs (Feasel and Kanazawa, 2013), thus moderating the effects of

cognitive-normative dissonances between home countries of firms, and increasing the mutual

attractiveness of prospective partners. Finally, economic ties trigger spillovers to contingent

institutional domains, which are not explicitly covered by these agreements, thus lowering

MNE’s coordination costs and appropriation concerns regarding an alliance partner. For instance,

although a bilateral FDI treaty between two countries is likely to focus on key investment

provisions such as protection of investments, dispute settlement, etc., it will also stimulate

convergence in terms of other regulatory aspects such as labor or IPR regulations that are

important for foreign investors. This translates into lower relational risks for firms coming from

signatory countries, which provides additional motivation for the MNE to select them as partners

for an alliance. Based on all these arguments, I hypothesize that:

H4: The extent of economic ties between the home country of the MNE and its prospective

partners will positively moderate the effect of institutional distance on partner selection for

technological alliances.

METHOD

Setting, sample and data

To test these hypotheses I use data on firms from the global tire industry. I focus on this industry

for a number of reasons. First, tire producers have formed over time many horizontal strategic

alliances, providing a rich environment for studying partner selection. Secondly, this is a truly

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global industry, with firms in more than 80 countries, thus capturing greater institutional

heterogeneity than the "usual suspects" in the alliance literature (i.e., high‐tech sectors, confined

to few developed nations). Finally, technology has always played an important role in this

industry, especially at the top where significant R&D efforts take place (see Table 1).

I compile a dataset of all tire producers worldwide, manually collected from various

issues of the European Rubber Journal (ERJ)2. Since partnering in a technological alliance is my

dependent variable, I limit the sample to the years where this information is available from these

journals, namely the period 1985 to 20033. To estimate partnering choices I consider all possible

dyads between firms in the industry over this period4. I match this data with other firm

characteristics (size, age and ownership) for both members of the dyad from ERJ. Firms for

which this information is not available are excluded from the sample.

In addition, I collect manually data on tire-related patents assigned to these firms from

Derwent Innovation Index (ISI Thomson), which has wide international coverage from multiple

national patent offices. To assemble the patent data, I identify all patents assigned to tire

producers and their affiliates that refer to tire products or processes. Using this information, I

compute firm knowledge stocks in the “tires” domain, assuming an annual depreciation rate of 15

percent to discount their commercial and scientific relevance over time (Griliches, 1990). For

firms that do not hold patents, I assume a null stock of technological knowledge. Analyzing the

announcement details of firms’ alliances activities (ERJ) I identify whether an MNE (i.e., the

focal firm) selects a partner in a dyad to form either an exploitative or an explorative

2 These agreements have been cross-checked with alliance and joint-venture data from Thomson’s SDC Platinum,

however the ERJ data is much richer in documenting all technological interactions.

3 After 2003 ERJ has stopped providing information on technological alliances.

4 This excludes data from the first three years (1986-1988) used to compute the previous alliance experience of

partners (described in the subsection “Controls”). Thus, there are on average about 220 active firms in the tire

industry each year, yielding around 385,000 potential undirected dyads (220 times 219, divided by 2 then multiplied

by 16 years), contingent on further data availability.

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technological alliance (coded as 1, and 0 otherwise). Following the text of these announcements, I

identify the focal firm in a dyad to be the one that provides technology to its partner for

exploitation, and respectively the firm taking the lead role in exploration alliances. For dyads

with firms that do not form a technological alliance (i.e., no alliance announcement available), I

consider the focal firm to be the one with the larger knowledge stock (i.e., granted patents) of the

two. In case both firms have the same number of patents, the first firm listed in the dyad is

considered to be the focal one. Lastly, I match the firm-level data with country characteristics

such as gross domestic product (GDP), GDP growth, geographic distance, institutional distances,

colonial ties, and economic ties. Firms from countries where these data are not available are

dropped from the sample.

Dependent variable. Data on technological alliances between tire producers worldwide

comes from various issues of the Global Tire Report published in the European Rubber Journal

(ERJ). Following prior work on technological alliances, I focus on horizontal dimension of these

agreements (Mowery et al., 2002; Lavie and Rosenkopf, 2006; Hoang and Rothaermel, 2010).

This is consistent with both theoretical arguments for clarity (Phelps, 2010) that favor horizontal

alliances as an avenue for technological exploitation and exploration, the unavailability of

complete data on the population of firms in other industries, and the high concentration of tire

technologies among top tire producers without a significant presence from other industries (Acha

and Brusoni, 2005). Hence, I examine the horizontal selection of international partners for

technological alliances, either exploitative or explorative in nature, as described in various issues

of ERJ. Following the literature (Lavie and Rosenkopft, 2006; Yamakawa et al., 2011), I capture

both exploitative alliances, which involve "the use and development of things [i.e., technologies]

already known" (March, 1991) and explorative ones, which aim to create new-to- the-firm

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resources and competences in a desire the seize new strategic opportunities (Koza and Lewin,

1998). I consider several types of exploitative alliances (i.e., long-term agreements involving

joint marketing, service, OEM, licensing, supply and joint-production deals) in which the focal

firm (i.e., the MNE) provides existing technologies to its partners in exchange for certain benefits

(e.g., access to production facilities, services, etc.) and explorative alliances (i.e., R&D alliances,

R&D joint-ventures, and long-term cross-licensing deals), which are commonly forged for

explorative purposes such development of technology standards, solving of technical issues or

technological collaboration. Thus, my dependent variable (Partner selection) as a binary

dependent variable that equals 1 if the two firms in a dyad form a cross-border technological

alliance in a year, and zero otherwise. The number of active alliances in the industry has

increased steadily from 1985 (75 alliances) to 2003 (113), and most of them (81%) are forged for

exploitative reasons. In terms of geographic distribution, 61% of them involve a firm from the

Triad (i.e., USA, Western Europe, and Japan) and one from a developing economy, 29% have

both partners from the Triad, and only 10% of technological alliances are formed exclusively

between firms from developing countries.

Independent variables. Following previous studies (Gaur et al., 2007; Estrin et al., 2009;

He et al., 2013), my measure of the cognitive environment focuses on cross-country cultural

differences as an important determinant of alliance decisions (Steensma et al., 2000, Delerue and

Simon, 2009). I compute a cultural distance index using Hofstede’s five dimensions of culture

(Hofstede et al., 2010) extracted from the website: http://www.geert‐hofstede.nl. These

dimensions capture potential cognitive conflicts between alliance partners that stem from cross-

country differences in obedience and respect for authority (power distance), trust and job security

20

(uncertainty avoidance), independence and the role of government (individualism), importance of

family and work (masculinity), as well as future expectations (long‐term orientation).

My measure for the normative environment focuses on international management

practices and reflects how inter-firm transactions are conducted across countries. Similar

measures employing managerial attitudes and norms were used by other studies examining

ownership (Xu et al., 2004) and export strategies of MNEs (He et al., 2013). Following them, I

consider nine managerial norms (Cronbach’s alpha = 0.91) retrieved from IMD’s World

Competitiveness Yearbook, which are relevant for partnering decisions in an international

context: competence, credibility, efficiency of corporate boards, employee training, flexibility

and adaptability, international experience, social responsibility and worker motivation5. Given the

potential overlap between these variables, I perform exploratory factor analysis to determine my

normative dimension (Table 2). Following it, variables with uniqueness above 0.6 are removed,

and from the remaining seven items (alpha =0.93) I derive a principal component (Eigenvalue=

4.53) for the latent variable that captures average managerial norms in a country6.

Technology‐specific regulatory elements are measured using global data on intellectual

property rights (IPR). IPR data comes from Park’s (2008) and covers five crucial aspects of IP:

extent of coverage, membership in international agreements, provisions for loss of protection,

enforcement options, and duration. This IPR index is computed as a weighted average of these

five dimensions and covers 122 countries over the period 1960‐20057. The frequency of

5 For example, in terms of managerial competence, Israel, USA and Switzerland score the highest, while Croatia,

Bulgaria and Peru are the lowest.

6 Using command factortest in Stata I perform additional tests (Bartlett’s sphericity, Kaiser-Meyer-Oklin sample

adequacy) to assess the appropriateness of factor analysis, all with satisfactory results.

7 USA, Netherlands and Japan have the strongest IPR provisions, while Burma, Angola and Guyana have the

weakest.

21

observations is every five years, and values have been linearly interpolated under the assumption

that these laws do not vary drastically within this interval.

Data on cognitive and normative institutions are used to compute dyadic (symmetric)

distances between two countries using the Mahalanobis formula, which is scale‐invariant and

accounts for the variance‐covariance matrix of components (Berry et al., 2010). Regulatory

distance between countries is computed as a simple difference between the IPR score of the focal

firm (i.e., the MNE) and its prospective partner in a dyad. This distance is asymmetric and in this

case direction counts, hence, the partner can be “stronger” (positive value) or “weaker” (negative

value) vis-à-vis the MNE (Zaheer et al., 2012). Both regulatory and normative distances vary

over time to accommodate potential institutional dynamics (Frantianni and Oh, 2009).

The extent of colonial ties is calculated using the duration (number of years) of colonial

rule between home countries of firms in a dyad using colonial links from CEPII database

(http://www.cepii.fr/) and their duration (Olsson, 2009). I define a colony as a new, long‐lasting

political organization created outside Europe by Western countries (countries in Europe

excluding Russia but including the so‐called "Western offshoots", namely the United States,

Australia, New Zeeland and Canada) between 15th and 20th century through invasion, conquest

or settlement colonization (Acemoglu et al., 2001). Finally, economic ties are captured using two

variables derived from Kohl (2013): (1) the GATT/WTO membership within a dyad, which can

take the value of 2 if both countries in the dyad are members in GATT (prior to 1995) or WTO

(after 1995), 1 if one of them is, and 0 if none of them are members; and (2) the Economic

Integration Agreements (EIA), coded as 1 if there is an EIA between the two nations in the dyad,

and 0 otherwise.

Controls. I include several other variables that are closely related to alliance partner

selection. Firm size and age (market experience) are two important determinants of its strategy

22

(Kale and Singh, 2009). Firm size differential is computed using the production capacity of firms

within a dyad, while firms’ age differential is determined using the opening year of their first

plants. To capture the strategic interdependence between firms (Gulati 1995b) in terms of

technologies I calculate the firm knowledge differential using firms’ annual patent stocks

computed from Derwent Innovation Index with a 15% annual depreciation rate (Griliches, 1990).

To control for benefits from prior or current equity links between firms in a dyad, several

dummies are included for cases in which partners are majority, minority ownerships or joint‐

venture projects. Another important motivation for international exploitative alliances is to access

new, dynamic markets (Glaister, 1996). At the country level, market size differential and market

growth differential are computed using GDP figures for both countries involved, extracted from

the World Penn Tables 6.1. Furthermore, a common proxy for external uncertainty in

international business is geographic distance (Berry et al., 2010). To account for this I use a

measure of geographic distance based on bilateral distances between the biggest cities of the two

countries in a dyad, weighted by population (CEPII). Furthermore, to controls for “border

effects” known to affect economic exchanges between countries (Schulze and Wolf, 2009) I also

employ a dummy variable for geographic contiguity (CEPII). Finally, a critical ingredient for

alliances is the existence of certain dedicated capabilities (Kale and Singh, 2002). I proxy these

capabilities using previous alliance experience (calculated as the cumulative number of alliances

for both firms in a dyad in the 3 previous years) and a partner-specific experience dummy (prior

interactions), which captures whether the two firms in a dyad have been engaged previously in

other alliances. Finally, time dummies are added to all estimations.

23

Estimation technique

The unit of analysis is the dyad, and I consider all undirected pairs of firms in the tire industry

where data is available. Table 3 presents descriptive statistics for all variables. Analyzing firm

dyads increases significantly the dimension of the dataset, but also introduces an additional

problem: given the very low (only 0.17 percent) number of 1s in the data, running a regular probit

or logit estimation will underestimate the probability of selecting a partner for an exploitative

technological alliance. Thus, I employ a rare-event logit model that relies on maximum likelihood

estimation and generates coefficients with lower mean square errors than the standard model by

correcting for rare events (King and Zeng, 2001). Moreover, since data contains all possible

dyads among these firms across time, it faces potential problems of unobserved heterogeneity and

autocorrelation. To deal with this, I follow Wang and Zajac (2007) and use robust standard errors

clustered on each dyad in all estimations.

A second major concern regarding the partner selection process in alliances refers to

endogeneity. I assume that the causal sequence I study exhibits two steps: first, firms make a

decision whether to engage in a technological alliance or not, and second, whom to partner with.

Both decisions are not random: the former is based on firm and home market characteristics such

as range of available technologies, competitive pressure, international strategy, etc., while the

latter is a function of potential partners’ characteristics, including institutional aspects. To deal

with these endogeneity concerns I use a two‐stage correction (Heckman, 1979). Using probit

analysis, the first stage estimates the probability of engaging in technological alliances for the

focal firm, as a function of its size, age, patent stocks, home market size and dynamics. In this

first stage the unit of analysis is the firm. In the second stage, using a rare-event logit estimator, I

analyze whether firms choose partners systematically based on different firm and country

24

specifics. To correct for the self‐selection issue, I include an Inverse Mills Ratio derived from the

probit regression of the first stage8. In the second stage the unit of analysis is the dyad.

RESULTS

Table 4 presents the pairwise correlation matrix. Most correlations are within acceptable limits,

indicating that there are no severe collinearity issues. The main regression results are reported in

Table 5. Model 1 confirms the important effect of our chosen control variables on the propensity

to select a partner: the degree of strategic interdependence (i.e., firm knowledge differential),

market dynamism (growth) differentials, as firms seek to establish themselves in new and

important markets (Glaister, 1996), existing formal ties (i.e., JVs, minority or majority holdings,

with the omitted category being "no relationship"), the endogeneity correction factor (i.e., the

Inverse Mills ratio), and alliance capability factors such as previous alliance experience and prior

interactions between firms in a dyad.

Models 2, 3 and 4 test the effects of cognitive, normative and regulatory institutional

differences (standardized values) on partner selection in international technological alliances. The

results strongly support my first two hypotheses with negative and highly significant coefficients

in all cases, indicating that MNEs prefer partners from closer cognitive-normative institutional

environments, and from similar or stronger regulatory ones. In terms of magnitudes, one standard

deviation increase in cognitive distance between two potential partners will reduce the log odds

of selection for a technological alliance by 0.34, compared to 0.23 (normative), and respectively

0.13 (regulatory distance). These relative effects are stable when all three institutional dimensions

are considered concomitantly (Model 5), suggesting that cognitive (cultural) differences are the

8 IMR is computed as the ratio of the probability density function over the cumulative distribution function of a

distribution.

25

most important criteria for partner selection, followed by normative (managerial) and regulatory

(IPR) aspects. Overall, the effects of all three institutional distances appear to be additive and

robust (Model 5), as postulated in the theory (Kostova, 1999).

Model 6 tests the effect of colonial ties on partner selection. This is positive but not

statistically significant, implying that, ceteris paribus, having colonial links does not warrant a

greater chance of forming a technological alliance with a MNE. Models 7, 8, and 9 test the

moderating effect of colonial ties on the relationship between institutional distance and alliance

partner selection and find significant positive effects only in the case of normative distance. This

suggests that the moderation occurs through bridging of cultural-cognitive elements, as well as

managerial norms and practices (Model 8).

Table 6 presents the results in the case of two types of economic ties. First, I examine

bilateral ties in the form of EIAs between home countries of MNEs and their potential partners.

Interestingly, the effect of EIAs on partner selection appears negative but not statistically

significant (Model 10), the moderating effects are in accordance with our expectations (Models

11-13), suggesting that moderation occurs via cognitive (cultural) and normative (managerial)

distance (so the log ratio of an alliance increases by 2.43 and 3.01 as the cognitive and

respectively, normative distances between two countries that have an EIA increases by one

standard deviation). In the case of regulatory dimension the results do not indicate moderation.

Second, I examine the indirect effect of GATT/WTO on partner selection. Although

membership of countries in GATT/WTO has positive, yet weak, statistically significant effects on

partner selection (Model 14), the results indicate that moderation occurs again through the

cognitive and normative elements of the institutional environment (Models 15 and 16) but not

through the regulatory one (Model 17). Thus, a shift from a case in which none or only one

country in a given dyad is a member of the GATT/WTO, to both being GATT/WTO members

26

will moderate the negative effects of a standard deviation increase in cognitive distance by 0.79,

and respectively 1.22 for normative distance.

Robustness checks

First, I test the robustness of the results by including several other cross-country distances (i.e.,

connectedness, knowledge, economic, and political) proposed by Berry et al. (2010). Despite a

significant reduction in terms of sample size, the coefficients of the institutional distances

considered remain negative and highly statistically significant, except for normative distance, re‐

enforcing my previous conclusions. Of all these additional distance measures considered, only

economic distance is statistically significant, implying that partner selection is negatively affected

by great differences in the macro-economic conditions of host-nations.

Second, alliance experience is commonly computed in the literature using the number of

alliances a firm forms in the past 5 or 10 years. Given the length of this panel (19 years) and the

strong correlation across time between the number of alliances firms form in a year, I have opted

for a shorter time window of 3 years. However, as a robustness check I have re-run all regressions

using more restrictive windows of 5 and respectively 10 years for computing the alliance

experience variable. As expected, the magnitude of these coefficients is smaller (on average, 0.44

for 5-year and 0.34 for 10-year window), but the main results hold despite the severe reductions

in sample size (counting around 115,000 and respectively 64,000 observations for the 5- and 10-

year window). These results of these robustness checks are not reported here due to space

constraints but are available upon request.

27

DISCUSSION, LIMITATIONS AND CONCLUSIONS

In this study, I set out to examine two issues: (1) the impact of institutional distance on partner

selection in international technological alliances; and (2) whether colonial and economic ties

between home countries of prospective partners moderate this relationship. Technological

alliances have become a popular tool among firms to secure competitive advantage (Anand and

Khanna, 2000), and their success depends largely on a proper selection process (Shah and

Swaminathan, 2008), which involves dealing with institutional differences of international

partners (Hitt et al., 2004). Combining insights from institutional theory and transaction costs

economics, I subscribe to the idea of a multidimensional institutional distance (i.e., cognitive,

normative and regulatory) and propose several mechanisms through which it affects MNE’s

selection of alliance partners in international settings (Xu and Shenkar, 2002; Eden and Miller,

2004). Therefore, by distinguishing the relative influence of different institutional components

and allowing for both symmetric and asymmetric effects, I offer a more nuanced understanding of

partner selection decisions in international alliances.

In doing so, I contribute to the existing literature in two major ways. First, I find strong

support for my main conjecture that, besides the “fit” in terms of firm characteristics (i.e.,

strategic interdependence, capabilities, experience) documented in previous studies, the selection

of partners depends also on institutional features (Kostova, 1999). This provides an important

convergence between transaction-costs economics arguments, which emphasizes

complementarity and interdependence rationales for partner selection, and those from institutional

theory, which motivate, shape and regulate the behavior of firms in a certain environment (Di

Maggio and Powell, 1983; Scott, 2001). Furthermore, different from prior work in this area,

which has commonly focused on single institutional dimensions and few countries (Hitt et al.,

28

2000; Hitt et al., 2004; Roy and Oliver, 2009), this study provides a comprehensive view on the

relative effect of different institutional elements on selection of international partners. Thus,

normative and cognitive differences appear to be more important criteria for partner selection

than regulatory ones, at least in the context of the global tire industry. This is consistent with

prior qualitative findings, which suggest that in the case of competitive, non-strategic industries,

normative-cognitive differences are more likely to govern inter-firm alliances given their

emphasis on uniform practices, HR management, and leadership values between alliance partners

(Olie, 1994). Likewise, it resonates with the arguments developed by Zhao (2006), regarding

MNE’s capacity to overcome regulatory differences via internal dedicated mechanisms, which

compensate for legislative differences across countries.

Second, I augment the existing literature by advancing and testing the hypothesis that

historical and economic links between countries have the potential to moderate the effects of

institutional distance on selection of international partners. Hence, lengthy colonial relationships

between countries provide prospective partners with deep knowledge of each other’s institutions

and norms and common legal traditions, which will reduce MNE’s uncertainty regarding the

behavior of partners, thereby lowering the perceived costs of an alliance. By focusing on the role

of colonial links in contemporary international business (Makino and Tsang, 2010), this work

answers recent calls in the literature (Jonnes and Khanna, 2006; Cantwell, Dunning and Lundan,

2009) to bring back history into the field, given its deep roots in our cultural, societal and

legislative systems. Furthermore, in addition to the role of historical ties, I also examine the

moderating effect of current economic links between countries, captured by bilateral and

multilateral agreements between nations. This offers insights on a relatively novel, yet central,

issue for the broader management agenda, namely the role of economic integration in

international business (Alhorr et al., 2008).

29

From a practitioner perspective, my findings are informative for both managers and policy

makers, especially those in developing countries. They draw attention to the importance of

cognitive (cultural) and normative (managerial) differences that lead to divergent behaviors

(Kelly et al., 2000), resulting in different perceptions regarding potential partners (Delerue and

Simon, 2009). On one hand, normative divergence in terms of management practices remains an

important deterrent for international collaborations, and a drag on alliance performance. Thus, the

development of managerial skills and adherence to international standards should become

priorities in these markets, achievable through both country- (i.e., education) and firm-specific

(i.e., in-house training) policies. On the other hand, cognitive distance is difficult to overcome,

and will remain an important component of the liability of foreignness due to its tacit nature and

great inertia that prompts changes with a centennial frequency (Hofstede et al., 2010). Besides

cognitive-normative aspects, a weaker regulatory (i.e., IPR) environment of a prospective partner

vis-a-vis the home country of the MNE will reduce its appeal as an alliance partner. This calls for

governments to step up existing IPR standards to reduce MNE’s appropriability concerns

(Khoury and Peng, 2011) and should prompt managers from these countries to lobby for reforms

or develop alternative safeguarding mechanisms to attract MNEs as technological partners.

Moreover, the effects of history and geo-politics on international technological alliances appear to

be powerful. These results suggest that national policies that support economic integration (e.g.,

joining of the GATT/WTO, signing of bilateral EIAs) can alter MNEs’ perception of risks

regarding institutionally distant partners, increasing their appeal as technological partners.

Considering the vital role of technologies in spurring international competitiveness and

development, governments in these countries should actively engage in such agreements.

The present study has several limitations that could serve as starting points for future

research. First, given the single-industry data used in this study, the generalizability of these

30

results should be examined across other contexts. My theoretical predictions did not rely on any

idiosyncratic feature of the tire industry, and therefore are generalizable to other empirical

settings. However, the features of technological alliances in other (say, high-tech, fast growing)

industries are very different from those within the tire domain, and may result in different ranking

for MNE priorities in terms of institutional barriers (Olie, 1994). Furthermore, this work focuses

exclusively on the technological and horizontal (i.e., within-industry) dimensions of alliances,

given the concentration of technology production and assets (i.e., patents) among top tire

producers (Acha and Brusoni, 2005) and the special feature of the tire industry, which has been

historically dominated by horizontal rather than vertical technological exchanges (ERJ).

However, firms in high-tech industries (e.g., pharmaceuticals, biotechnology) characterized by

complex and dynamic technological environments are often adopting a more mixed partnering

strategy, which includes both horizontal and vertical alliances for exploration and exploitation

(Rothaermel and Boeker, 2008). Hence, the generalizability of these results could be further

tested by examining the vertical dimension of technological alliances, especially in the case of

high-tech industries. Complementary, subsequent work along these lines could make the

distinction between the effects of certain institutional features on the selection of international

partners for technological versus non-technological alliances.

31

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Table 1: Top global tire producers

Rank Company Country Average sales Percent Tires No. Plants No. Countries R&D spending Employees Investments

1 Michelin France 10,892 93 56 14 735 128,165 1,290 2 Bridgestone Japan 9,703 75 44 19 354 102,165 1,573 3 Goodyear USA 9,616 86 45 22 449 106,724 805 4 Continental Germany 3,762 43 21 12 401 63,832 619 5 Sumitomo Japan 2,903 74 6 3 174 15,000 375 6 Pirelli ltaly 2,712 45 18 10 216 41,954 500 7 Yokohama Japan 1,978 72 8 3 104 5,401 175 8 Toyo Japan 1,140 64 5 3 74 5,848 97 9 Cooper USA 1,138 53 5 2 40 21,185 150 10 Kumho South Korea 914 100 4 2 42 5,510 154 11 Hankook South Korea 826 95 4 2 51 4,152 128

Notes: (1) Average sales and percent of sales from tires are computed between 1984-2003 in million US$; R&D spending and investments (in million US$), number of plants, number of countries in which these companies are present, and the number of employees, are all for 2003; (2) The top three tire producers worldwide account for almost half, while the top 11 producers account for about two thirds of the total R&D expenditures in the industry. Source: various issues of the European Rubber Journal.

37

Table 2: Factor loadings of managerial values items fom IMD WCY*

Variable Factor1 Uniqueness Managerial Values

Competence of managers 0.69 0.52 Credibility of managers 0.89 0.20 Corporate boards effectiveness 0.81 0.35 Employee training 0.77 0.40 Flexibility and adaptability 0.48 0.77 International experience of management 0.72 0.49 Remuneration of management 0.20 0.96 Social responsibility 0.83 0.31 Worker's motivation 0.89 0.21

Eigenvalue 4.53 Alpha 0.93

Note: * Bold type indicates best factored items

Table 3: Descriptive statistics

Variable Obs. Mean Std. Dev. Min Max

Partner selection 204,070 0.002 0.046 0.00 1.00 Firm size differential* 204,070 10.614 1.756 0.00 14.82 Firm age differential* 204,070 3.022 0.951 0.00 4.77 Firm knowledge differential* 204,070 0.759 1.607 0.00 6.86 Minority 204,070 0.000 0.019 0.00 1.00 Majority 204,070 0.001 0.024 0.00 1.00 JV 204,070 0.001 0.027 0.00 1.00 Market size differential* 204,070 1.622 1.104 0.00 6.51 Market growth differential 204,070 0.041 0.035 0.00 0.24 Geographic contiguity 204,070 0.065 0.247 0.00 1.00 Geographic distance* 204,070 8.890 0.700 5.09 9.88 Inverse Mills ratio 204,070 3.565 0.377 1.95 3.90 Previous alliance experience 204,070 0.162 0.933 0.00 10.00 Prior interactions 204,070 0.002 0.044 0.00 1.00 Cognitive distance 204,070 0.020 1.046 -2.80 2.09 Normative distance 204,070 -0.082 0.850 -1.25 4.45 Regulatory distance 204,070 0.252 1.017 -2.33 2.90 Colonial ties* 204,070 0.299 1.236 0.00 6.10 EIA 204,070 0.122 0.328 0.00 1.00 GATT/WTO 204,070 1.857 0.351 0.00 2.00

Note: Variables marked with an * have followed a logarithmic transformation.

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Table 4: Paired correlations

No Variable 1 2 3 4 5 6 7 8 9 10

1 Partner selection 1.00

2 Firm size differential 0.03* 1.00

3 Firm age differential 0.03* 0.09* 1.00

4 Firm knowledge differential 0.09* 0.33* 0.10* 1.00

5 Minority 0.35* 0.01* 0.01* 0.04* 1.00

6 Majority 0.44* 0.02* 0.01* 0.06* 0.01* 1.00

7 JV 0.43* 0.03* 0.01* 0.08* 0.04* 0.17* 1.00

8 Market size differential 0.00 0.10* 0.01* 0.12* -0.00 0.00 0.00 1.00

9 Market growth differential 0.00 -0.04* 0.04* -0.06* 0.01* -0.01* -0.00 -0.02* 1.00 10 Geographic contiguity -0.01* 0.00 -0.06* -0.05* 0.00 -0.01* -0.01* -0.01* -0.09* 1.00 11 Geographic distance 0.00 0.01* 0.04* 0.00 0.00 0.00 0.01* 0.07* 0.12* -0.41* 12 Inverse Mills ratio -0.12* -0.25* -0.18* -0.74* -0.04* -0.08* -0.09* -0.08* 0.06* 0.06* 13 Previous alliance experience 0.07* 0.07* 0.05* 0.29* 0.01* 0.01* 0.01* 0.02* -0.02* -0.024* 14 Prior interactions 0.80* 0.03* 0.01* 0.09* 0.30* 0.38* 0.45* 0.00 0.01* -0.01* 15 Cognitive distance -0.01* -0.05* -0.06* 0.01* -0.00 0.01* -0.00 0.01* -0.09* -0.00 16 Normative distance -0.01* 0.05* 0.02* 0.02* -0.01* 0.00 -0.00 0.08* -0.01* -0.08* 17 Regulatory distance -0.01* 0.00 0.03* 0.20* 0.00 0.01* 0.01* 0.10* -0.07* -0.07* 18 Colonial ties 0.00 0.04* 0.04* 0.01* -0.00 -0.01* -0.00 0.00 -0.13* -0.06* 19 EIA -0.02* -0.05* -0.07* -0.08* -0.01* -0.01* -0.01* -0.07* -0.13* 0.37* 20 GATT/WTO 0.00 -0.01* -0.06* 0.08* 0.00 0.01* 0.01* 0.01* -0.24* -0.10*

No Variable 11 12 13 14 15 16 17 18 19 20

11 Geographic distance 1.00

12 Inverse Mills ratio 0.03* 1.00

13 Previous alliance experience -0.00 -0.38* 1.00

14 Prior interactions 0.00 -0.12* 0.06* 1.00

15 Cognitive distance 0.02* 0.12* -0.00 -0.01* 1.00

16 Normative distance 0.002 -0.03* -0.03* -0.01* -0.01* 1.00

17 Regulatory distance 0.10* -0.06* -0.00 0.01* 0.16* 0.03* 1.00

18 Colonial ties 0.04* 0.00 0.01* 0.00 0.06* -0.05* 0.12* 1.00

19 EIA -0.59* 0.04* -0.03* -0.02* 0.00 -0.03* -0.12* -0.08* 1.00 20 GATT/WTO 0.01* -0.09* 0.04* 0.00 0.10* 0.06* 0.10* 0.10* 0.14* 1.00

Note: * denotes correlations significant at 5 % or better.

39

Variables / Models Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9

Firm size differential 0.07 0.05 0.07 0.07 0.04 0.06 0.04 0.05 0.06 [0.09] [0.09] [0.08] [0.09] [0.08] [0.09] [0.09] [0.09] [0.09] Firm age differential -0.17 -0.22 -0.17 -0.17 -0.22 -0.20 -0.26 -0.20 -0.20

[0.18] [0.17] [0.18] [0.18] [0.17] [0.18] [0.17] [0.18] [0.17] Firm knowledge differential -0.55** -0.61*** -0.55*** -0.55*** -0.61*** -0.50*** -0.59*** -0.55*** -0.51***

[0.18] [0.15] [0.14] [0.14] [0.15] [0.15] [0.17] [0.14] [0.15] Minority 8.52*** 8.51*** 8.56*** 8.52*** 8.56*** 8.62*** 8.63*** 8.70*** 8.63***

[0.79] [0.77] [0.77] [0.80] [0.77] [0.80] [0.78] [0.82] [0.81] Majority 7.46*** 7.30*** 7.61*** 7.47*** 7.37*** 7.55*** 7.40*** 7.55*** 7.56***

[0.77] [0.60] [0.88] [0.80] [0.70] [0.79] [0.65] [0.81] [0.82] JV 5.60*** 6.30*** 6.08*** 5.99*** 6.36*** 6.03*** 6.27*** 6.11*** 6.03***

[0.61] [0.78] [0.60] [0.62] [0.77] [0.60] [0.76] [0.62] [0.61] Market size differential 0.03 0.02 0.00 0.03 -0.01 0.03 0.01 0.09 0.03

[0.16] [0.16] [0.18] [0.16] [0.17] [0.16] [0.16] [0.15] [0.16] Market growth differential 12.47*** 9.51*** 12.77*** 12.49*** 9.92*** 13.47*** 10.01*** 13.73*** 13.40***

[3.46] [3.35] [3.31] [3.41] [3.02] [3.40] [3.32] [3.54] [3.36] Geographic contiguity 0.32 0.16 0.35 0.31 0.18 0.41 0.20 0.38 0.40

[0.67] [0.63] [0.66] [0.66] [0.60] [0.68] [0.65] [0.69] [0.66] Geographic distance -0.08 -0.11 -0.06 -0.08 -0.09 -0.10 -0.14 -0.14 -0.10

[0.20] [0.21] [0.20] [0.19] [0.20] [0.20] [0.20] [0.20] [0.20] Inverse Mills ratio -4.42*** -4.66*** -4.46*** -4.41*** -4.68*** -4.32*** -4.72*** -4.57*** -4.36***

[0.58] [0.63] [0.56] [0.59] [0.62] [0.62] [0.69] [0.58] [0.64] Previous alliance experience 0.05*** 0.05*** 0.05*** 0.05*** 0.05*** 0.05*** 0.05*** 0.05*** 0.05***

[0.02] [0.02] [0.02] [0.02] [0.02] [0.02] [0.02] [0.02] [0.02] Prior interactions 9.01*** 9.12*** 9.10*** 9.01*** 9.21*** 8.94*** 9.10*** 9.16*** 8.94***

[0.43] [0.46] [0.47] [0.43] [0.49] [0.49] [0.52] [0.52] [0.49] Cognitive distance -0.34** -0.32** -0.30**

[0.14] [0.13] [0.15]

Normative distance -0.23** -0.20*** -0.17 [0.06] [0.07] [0.11] Regulatory distance -0.13*** -0.09** -0.08

[0.05] [0.04] [0.08] Colonial ties 0.19 0.15 0.24 0.23+

[0.15] [0.13] [0.17] [0.13] Colonial ties * cognitive distance 0.17***

[0.06]

Colonial ties * normative distance 0.34*** [0.09] Colonial ties * regulatory distance -0.07

[0.07]

N 204,070 204,070 204,070 204,070 204,070 204,070 204,070 204,070 204,070

Table 5: Institutional determinants of partner selection and the moderating effects of colonial ties on institutional distance. Rare-event logistic regression

Notes: The dependent variable equals 1 if the potential partner in the dyad is selected for a technological alliance, and 0 otherwise; All models include time dummies and an intercept, not reported given space constraints; +, ** and *** indicate variables that are significant at the 10%, 5% and respectively 1%.

40

Variables / Models Model 10 Model 11 Model 12 Model 13 Model 14 Model 15 Model 16 Model 17

Firm size differential 0.07 0.03 0.06 0.07 0.06 0.05 0.05 0.06 [0.09] [0.09] [0.08] [0.09] [0.08] [0.09] [0.08] [0.09] Firm age differential -0.18 -0.23 -0.17 -0.17 -0.18 -0.21 -0.16 -0.18

[0.18] [0.17] [0.18] [0.18] [0.18] [0.17] [0.18] [0.18] Firm knowledge differential -0.57*** -0.61*** -0.55*** -0.58*** -0.56*** -0.58*** -0.57*** -0.55***

[0.14] [0.15] [0.14] [0.14] [0.14] [0.14] [0.14] [0.14] Minority 8.41*** 8.41*** 8.45*** 8.43*** 8.61*** 8.60*** 8.69*** 8.63***

[0.81] [0.78] [0.78] [0.82] [0.83] [0.79] [0.82] [0.84] Majority 7.43*** 7.24*** 7.59*** 7.47*** 7.17*** 7.013*** 6.82*** 7.08***

[0.80] [0.61] [0.90] [0.83] [0.64] [0.56] [0.57] [0.67] JV 6.02*** 6.34*** 6.09*** 6.02*** 6.04*** 6.17*** 6.29*** 6.02***

[0.60] [0.77] [0.59] [0.61] [0.64] [0.73] [0.62] [0.65] Market size differential 0.02 0.01 -0.00 0.02 0.08 0.08 0.08 0.07

[0.16] [0.15] [0.18] [0.16] [0.17] [0.17] [0.18] [0.17] Market growth differential 11.75*** 8.69** 12.17*** 11.81*** 9.94*** 9.32*** 9.67*** 10.14***

[3.55] [3.42] [3.39] [3.53] [3.19] [3.37] [3.19] [3.03] Geographic contiguity 0.72 0.49 0.78 0.75 0.31 0.22 0.30 0.31

[0.69] [0.66] [0.67] [0.64] [0.67] [0.66] [0.64] [0.65] Geographic distance -0.29 -0.29 -0.25 -0.29 -0.07 -0.17 -0.06 -0.07

[0.20] [0.21] [0.20] [0.20] [0.20] [0.21] [0.20] [0.20] Inverse Mills ratio -4.46*** -4.68*** -4.44*** -4.51*** -4.52*** -4.55*** -4.61*** -4.49***

[0.58] [0.63] [0.563 [0.59] [0.60] [0.56] [0.58] [0.62] Previous alliance experience 0.05*** 0.05*** 0.05*** 0.05*** 0.05*** 0.05*** 0.05*** 0.05***

[0.02] [0.02] [0.02] [0.02] [0.02] [0.02] [0.02] [0.02] Prior interactions 8.93*** 9.05*** 9.01*** 8.95*** 9.12*** 9.09*** 9.30*** 9.13***

[0.41] [0.44] [0.46] [0.41] [0.43] [0.42] [0.50] [0.42] EIA -1.63+ -2.41** -3.37*** -1.54+

[0.96] [1.04] [1.28] [0.90]

Cognitive distance -0.36*** -1.73**

[0.13] [0.69]

Normative distance -0.24** -2.06** [0.12] [0.89] Regulatory distance -0.12 -0.34

[0.19] [0.88] EIA * cognitive distance 2.49***

[0.24]

EIA * normative distance 3.01***

[0.58]

EIA * regulatory distance -0.50

[0.64]

GATT/WTO 0.71+ 0.80 0.39 0.73+ [0.39] [0.76] [0.48] [0.39] GATT/WTO * cognitive distance 0.79**

[0.38]

GATT/WTO * normative distance 1.22** [0.49] GATT/WTO * regulatory distance 0.19

[0.47] N 204,070 204,070 204,070 204,070 204,070 204,070 204,070 204,070

Table 6: Moderating effects of economic ties (EIAs and GATT/WTO membership) on institutional distance. Rare-event logistic regression

Notes: The dependent variable equals 1 if the potential partner in the dyad is selected for a technological alliance, and 0 otherwise; All models include time dummies and an intercept, which not reported here due to space constraints; +, ** and *** indicate variables that are significant at the 10%, 5% and respectively 1%.