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Gravity for Domestic and International Alliances: A CAGE perspective

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The aim of this paper is to assess the relevance of the Gravity model to investigations of alliance flows within and between countries. We also aim to identify how different distance dimensions impact the choice of a partner. Inspired by economics research, we use the Gravity model to estimate and analyse bilateral alliance flows within countries and between country pairs. Our findings confirm the relevance of the Gravity model to investigations of alliance flows, and our results show that the richer the companies’ home countries, the more alliances are found between these two countries. Second, we reveal that too much geographical and cultural distance between two countries decreases the number of alliances signed between them.
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Pour citer cet article: E, J; C, P & Le R, F. (2018). Gravity for Domestic and International Alliances: A CAGE Perspective.
Management international, 22(spécial), 56-69.
Pour citer cet article: E, J; C, P & Le R, F. (2018). Gravity for Domestic and International Alliances: A CAGE Perspective.
Management international, 22(spécial), 57-69.
Pour citer cet article: E, J; C, P & Le R, F. (2018). Gravity for Domestic and International Alliances: A CAGE Perspective.
Management international, 22(spécial), 56-69.
Introduction
A
lliance formation is an i mportant component of companies’
strategy (Dunning, 1995) and is an ever-growing eld of
research in both the strategic management and the interna-
tional business literature (Teng & Das, 2008). More precisely,
alliances are seen as a critical way to develop act ivities on either
domestic or international markets such that dedicated research
in international business and strategic management is closely
related to economic theories (Beamish & Lupton, 2016). In this
paper, we build on economic theories regarding foreign direct
investment (FDI) and Grav ity models to understand the choice
of partners in alliances. More precisely, we assess the relevance
of the Gravity model to the invest igation of alliance ows within
and between countries, and we identify how dierent distance
dimensions impact the choice of a par tner. Gravity models are
most oen used to investigate FDI ows, but they have never
been used in an alliance context. is research thus allows us
to show how determinants of bilateral FDI ows can also be
used to understand alliance formation.
Research explains t hat alliances are considered to be one of
many types of development modes that ex ist alongside acquisi-
tion (and FDI abroad) or organic growth (Kogut, 1985). When
choosing to form an alliance, either locally or internationally,
the choice of the alliance partner is considered crucial, as it is
an important factor for the operation, performance and suc-
cess of alliances (Dacin, et al., 1997; Pangarkar & Klein, 2001;
Nielsen, 2003). It is therefore essential that decision makers are
able to choose the “right” partner (Beamish, 1985; Kauser &
Shaw, 2004). In the alliance literature, the choice of a partner is
explained by the task a nd/or partner characteristics (Geringer,
ABSTR ACT
This paper assesses the relevance of the
Gravity model to investigations of alliance
flows within and between countries.
Accordingly we aim to identify how dif-
ferent distance dimensions impact the
alliance partner choice. Inspired by eco-
nomics research, we use the Gravity model
to estimate and analyse bilateral alliance
flows w ithin countr ies and between country
pairs. Our results show that the richer the
companies’ home countries are, the more
alliances are found between firms of these
two countr ies. We also revea l that too much
geographic a nd cultura l distance bet ween two
countries decreases the number of a lliances
signed between firms of these countries.
Keywords: Domestic and international
alliances; Gravity model; Distances; CAGE
Distance Framework
RÉSU
Cet article évalue la pertinence du modèle
gravitaire pour comprendre les flux d’al-
liances bilatérales au sein d ’un pays et entre
différents pays. Inspirés par les recherches
en économie, nous uti lisons ce modèle pour
identif ier l’impact de s différentes dimensions
de la distance sur le choix du partenaire
d’alliance. Nos résultats montrent que plus
les pays sont riches, plus le nombre d’al-
liances entre les entreprises de ces pays est
importa nt. Nous révélons également que des
distances géographique et culturelle trop
importantes entre deux pays ont tendance
à diminuer le nombre d’alliances signées
entre les entreprises de ces pays.
Mots-Clés: Alliances nationales et interna-
tionale s; Modèle gravita ire; Distance s; CAGE
Distance Framework
RESUMEN
Este articulo evalúa la importancia sobre el
modelo de Gravity para las investigaciones
de flujos de alianzas locales y entre países.
Nuestro objetivo es identif icar cómo las dife-
rentes dimensiones en distancia afectan la
elección de un socio aliado. Inspirados en
la investigación económica, utilizamos el
modelo de Gravity para estimar y analizar
los flujos de la alianza bilateral dentro de
los países y entre pares de países. Nuestros
resultados d emuestra n que cuantos más ricos
son los países de origen de las empresa s, más
alia nzas se crean entre las empresas de estos
dos países. También demostramos que la
lejanía geográf ica y cultura l entre dos países
disminuye el número de alianzas.
Palabras clave: Las alianzas naciona-
les e internacionales; Modelo de Gravity;
Distancias; CAGE Distance Framework
Gravity for Domestic and International Alliances:
A CAGE Perspective*
Le modèle gravitaire pour les alliances domestiques
et internationales: une approche CAGE
Gravedad para las alianzas nacionales e internacionales:
una perspectiva CAGE
JULIANE ENGSIG
MRM – Montpellier Management,
University of Montpellier
PAUL CHIAMBARETTO
MRM – Montpellier Business School /
Ecole Polytechnique
FRÉDÉRIC LE ROY
MRM – Montpellier Management,
University of Montpellier / Montpellier
Business School
*Acknowledgement: We wo uld like to t hank the ed itors Nadine Tournois and Ph ilippe Very for their helpf ul and constructive comments. We also w ish to thank
the two anonymous reviewers for their high-quality comments and discussions, which have helped to improve the quality of the article. We are also grateful to
Pierre-Xavier Mesc hi for comments on an e arlier version of th e research. Fin ally, we would lik e to thank LA BEX Entreprend re for the fina ncial support for t his work.
Pour citer cet article: E, J; C, P & Le R, F. (2018). Gravity for Domestic and International Alliances: A CAGE Perspective.
Management international, 22(spécial), 57-69.
Pour citer cet article: E, J; C, P & Le R, F. (2018). Gravity for Domestic and International Alliances: A CAGE Perspective.
Management international, 22(spécial), 56-69.
Gravity for Domestic and International Alliances: A CAGE Perspective 57
1991; Glaister & Buckley, 1996). is choice may become even
more dicult in an international context because of the natio-
nal dierences between potential partners (Luo, 2002, 2007).
Indeed, within the literature on international alliances, many
studies highlight and analyse country dierences between
partners (Mayrhofer, 2004; Nielsen, 20 07; Meschi & Riccio, 20 08;
Zaheer & Hernandez, 2011). ese dierences can be classied
as dierent types of distance (Berry et a l. 2010). A consensus has
emerged that the dierent types of distance genera lly negatively
aect both the likelihood and level of cooperation between
partners. Consequently, rms tend to choose a local or regional
business partner whenever possible (Rugman & Verbeke, 2004;
Oh & Rugman, 2014; Ghemawat, 2016). For this reason, even
if the question of distance is traditionally addressed within
the context of international alliances, we argue that for most
alliances (R&D, production, marketing etc.), domestic partners
can be seen as substitutes to international partners, such that
it is necessary to investigate both domestic and international
alliances to understand the impact of the dierent types of
distance on partner selection.
Building on the CAGE Distance Framework (Ghemawat,
2001), we aim to reveal how the dierent types of distance
(divided into cultural, administrative, geographic and econo-
mic distance) impact the choice of a partner for a domestic or
international alliance at the country level. e originality of
this study lies in the fact that only a few studies have empiri-
cally investigated several distance dimensions simultaneously
in an alliance context (Moalla, 2015; Choi and Contractor,
2016). Furthermore, so far, most distance-related studies have
been performed at the rm level, even though research argues
that to obtain a more holistic understanding of a phenomenon,
one should also consider other levels of analysis (individuals,
groups, enterprises, countries) (Felin & Foss 2009; Rousseau,
2011). In this study, we are interested in how the micro-level
strategic decisions made at a company level are aggregated at
the country level. rough this analysis, we hope to arrive at
new insight into how dierent distance dimensions impact
partner selection at a global level. Because the determinants
might dier according to the type of entry mode or alliance,
it is important to understand whether, at the aggregated level,
some distance dimensions are more important than others.
To understand rms’ choices of alliance partners in an
international context and using a country-level approach, we
draw from the economic literature on bilateral trade and FDI.
At rst glance, alliances appear dierent from FDI because
they require a lower investment of resources, as well as shared
risk between the partners and access to the partner’s network
resources (Gulati, 1998). However, alliances also have several
commonalities with FDI, including location-based advantages
(Kogut, 1985) and the challenge of entering unknown territory.
Furthermore, some alliances (equity a lliances such as joint ven-
tures) are traditionally categorized as FDI. Consequently, we
can state that in both FDI and alliances, companies face some
common risks in regard to country dierences, even if they
can react dierently to these risks (Canabal & White, 2008).
To investigate the role of the dierent types of distance, we
use the Gravity model. e Gravity model shows that there is
a positive relationship between countries’ economic sizes and
trade, but there is a negative relationship between the distance
separating the countries and trade (Buch et al., 2004; Combes
et al. 2006). e Gravity model has been used to explain several
other phenomena in addition to trade and FDI ows (Kleinert
& Toubal, 2010). Consequently, we want to assess its relevance
for predicting alliance ows between country pairs. To the best
of our knowledge, this has never been done in the literature on
domestic or international al liances. Based on t he existing litera-
ture on Gravit y models for FDI and on the literature on alliances,
we propose ve hypotheses. Our results show that that there
is a positive relation between the number of alliances between
country pairs and the economic size of the countries involved.
In other words, the richer the partneri ng countries are, the more
alliances we nd between them. Secondly, we nd that t here is a
negative relat ion between the geog raphic distance (measured i n
kilometres) between the partners and the number of alliances
signed between the countries. is means that t he farther away
the partnering countries are physically, the fewer the alliances
between them. Our ndings also reveal that too much cultural
distance negatively impacts the number of alliances between
country pairs. Finally, we nd that ad ministrative and economic
distances do not aect the number of alliances.
Our study contributes to research on strategic management,
international business and economic theories. We contribute to
the existing alliance literature, as well the international business
literature, by testing several distance dimensions at a country
level, which helps to broaden our understanding of the choice
of alliance partner in an international context. We have found
that it is relevant to divide distance into dierent dimensions
and that each dimension impacts the choice of a partner die-
rently. Our analysis therefore streng thens the results of several
other studies that have highlighted the importance of country
dierences between companies (Mayrhofer, 2004; Kaufmann
& O’Neil, 2007; Meschi & Riccio, 2008; Lavie et al., 2012). We
also conclude that alliances, and therefore choices of par tners,
are more oen made locally or between neighbouring countries,
where geographical and cultural dierences are reduced. is
paper also contributes to the economic literature, more precisely,
the gravity literature, as we show that t he Gravity model is also
useful in an allia nce context and may help to bet ter understand
the choice of partner in an international context.
Literature review and hypotheses
T G :    
FDI 
e Gravity model is inspired by Newton’s gravity equation in
physics, which states that the gravitational forces between two
bodies depend on their mass and the distance between them
(Zwinkels & Beugelsdijk, 2010). In the 1960s, the logic of New-
ton’s gravity equation was applied to the eld of international
trade by Tinbergen (1962) and Linnemann (1966). Applied in
this context, the Gravity model postulates that the magnitude
of international trade ows between two countries depends
on the same two types of factors: 1) “Mass” factors, such as
the economic size of the countries or their level of economic
development (measured in GDP), which increase trade ows
and 2) “distance” factors, such as the geographic distance or
58 Management international / International Management / Gestión Internacional
other barriers between the countries, which reduce t rade ows
(Fratianni et al., 2011).
e rst contributions based on the Gravity model mainly
aimed to describe bilateral trade ows (see Head & Mayer (2013)
for a synthesis of empirical a nd theoretical contributions based
on the gravity approach). In the last twenty years, a new stream
of research, especially in economics, has applied the Gravity
model to a dierent type of ows: Foreign Direct Investments
(Kleinert & Toubal, 2010; Fratianni et al., 2011). is approach
improved the existing theory of FDI, and it has been arg ued that
the Gravity model is “…the most successful empirical specica-
tion for bilateral FDI” (Paniagua et al., 2015). However, Li and
Vashchilko (2010) remark that even if the Gravit y model is a solid
empirical tool to explain FDI ows, its theoretical foundations
are still limited. Some theoretical attempts have been proposed
by authors such as Brainard (1997), Egger and Pfaermayr
(2004) and Kleinert and Toubal (2010), who develop models in
which multinational rms face a trade-o between exporting
and setting up a rm or plant in a foreign country using FDI.
Nevertheless, the largest body of research concerning FDI
remains empirical, and it is structured around two streams of
research. A rst set of contributions aims at understanding the
determinants of FDI bilateral ows. ese contributions iden-
tify dierent forms of distance beyond traditional geographic
distance, such as the impact of cultural or psychic distance
(Dow & Ferencikova, 2010); they also consider ot her factors that
reduce FDI ows, such as the level of political risk in the host
country (Bevan & Estrin, 2004). In contrast, other researchers
identify factors that mitigate the negative impact of distance,
such as bilateral trade and investment agreements (Berger et
al., 2013) or institutional factors (Benassy-Quéré et al., 2007).
In parallel, a second set of contributions investigates more
technica l issues related to FDI bilateral ows. Some articles use
the Gravity model to investigate the dynamics of FDI bilateral
ows and to understand the impact of external shocks on their
variations (Zwinkels & Beugelsdijk, 2010; Kahouli & Maktouf,
2015), while others analyse the impact of concentrating FDI on
a limited number of rms on the value of the gravity coecients
(Paniagua et al., 2015).
A    G  
 
When considering research on FDI ows, all the articles men-
tioned above focus their attention on FDI as a whole. However,
FDI can take many forms. According to the OECD, “…FDI is
dened as a cross-border investment by a resident entity in one
economy with the objective of obtaining a lasting interest in an
enterpr ise resident in another economy
1
. In other words, FDI is
an investment made by a company or entity based in one countr y
in a company or entity based in another countr y. Firms making
foreign direct investments typically have a signicant degree
of inuence and control over the foreign company into which
the investment is made. us, FDI may take dierent forms,
such as direct acquisition of a foreign rm, construction of a
facility in the foreign country, or investment in a joint venture
with a foreign partner. However, cooperative entry modes (e.g.,
1. www.oecd-ilibrar y.org/sites/factbook-2013-en/04/02/01/index.html?itemId=/content/chapter/factbook-2013-34-en
alliances and joint ventures) are rarely treated specically in
the FDI literature.
In the present paper, we focus our attention on cooperative
entry modes such as alliances for three reasons. First, coope-
rative entry modes are rarely treated specically in the FDI
literature, although their grow th rate is twice as la rge as that of
other ty pes of FDI (Owen & Yawson, 2013). According to Greve
et al. (2014), more than 4,000 alliances are created each year.
Among these alliances, for the year 2015, the SDC (Securities
Data Company) Platinum database reveals that joint ventures
account for 80% of all strategic alliances created. Second,
according to Ra et al. (2009), joint ventures account for 40%
of FDI ows, but they have never been studied alone using the
Gravity model. Consequently, studying FDI as a whole does
not provide us with a clear picture of the potential specicities
of joint ventures in the context of the Gravity model, and, in
particular, it does not consider non-equity alliances. Finally,
focusing on FDI does not allow us to take into account strategic
alliances made nat ionally (which accounted for more than 49%
of all alliances made in 2015).
Studies have found that the cultural dierences between
partners (Calantone & Zhao, 2001), as well as the partners’
political and economic dierences (Meschi et al. 2017) and
the structural complexity of their alliance (Shenkar, 1990),
inuence the success of the joint venture. us, within the
research on FDI and cooperative entry modes, studies have
already emphasized the importance of country d ierences. is
is paradoxical because several researchers have highlighted that
strategic alliances (including joint ventures) and wholly owned
subsidiaries are impacted dierently by the various forms of
distance (Morschett et al., 2010; Brouthers, 2013, Moalla, 2015).
For the reasons mentioned here, we are interested in knowing
the extent to which FDI and alliances are similarly aected by
country dierences and how the Gravity model may help to
explain these similarities.
No previous studies have investigated alliances as a whole
(including not only domestic and international alliances but
also equity and non-equity alliances) while simultaneously
measuring country dierences. us, we nd that although
a sizeable eld of research has investigated FDI from a gra-
vity perspective, almost no research has studied the bilateral
ows of strategic alliances (i.e., joint ventures and non-equity
agreements, which are not accounted for in FDI statistics). e
only contribution to the study of strategic alliances that uses a
gravity approach is that of Owen and Yawson (2013), but they
use the gravity approach as a control model to test its impact
on the information costs of cross-border strategic alliances.
We thus nd that it crucial to apply the Gravity model to both
domestic and international alliances (joint ventures and non-
equity ag reements). Analysing the ow of alliances may help us
broaden our understanding of rms’ choices of al liance partners.
T    :   
 
When looking for a new alliance partner, rms can employ
task-related as well as partner-related selection criteria (Shah
Gravity for Domestic and International Alliances: A CAGE Perspective 59
& Swaminathan, 2008). e task-related criteria are associated
with the partner’s skills a nd capabilities (Dussauge et al., 20 07),
whereas partner-related criteria are related to the cha racteristics
of the partner, such as its national and corporate cultures (Luo,
1998; 2002). In the present paper, we aim to contribute to the
literature that focuses on partner-related criteria by looking at
how country dierences aect the choice of partner in strategic
alliances.
From the gravity literature on international trade and FDI
bilateral ows, we know that countries’ sizes (measured by GDP)
have a positive eect on the volume of the ow, while distance
between two countries has a negative eect (Kleinert & Toubal,
2010; Head & Mayer, 2013). Limiting the concept of distance to
the geographic distance between t he two countries is, however,
too restrictive if we seek to understand the ow of alliances. In
fact, as research has progressed, other forms of distance have
been integrated into the Gravity model, such as cultural dis-
tance (Dow & Ferencikova, 2010; Felbermayr & Toubal, 2010)
and psychic and institutional distance (Benassy-Quéré et al.,
2007). is is referred to as the “spirit of gravity,” wherein other
estimations correlate with the geographical distance and the
countries’ GDP (Head & Mayer, 2013).
Similarly, in the alliance literature, we nd several contri-
butions that have underlined the importance of country dif-
ferences between the involved partners (Mayrhofer, 2004;
Meschi & Riccio, 2008; Zaheer & Hernandez, 2011, Le Roy et
al., 2016). ese dierences are signicant, positively or nega-
tively, for the results of the cooperation, and they convince a
company to choose either a global partner or a local partner.
A consensus has emerged – and most contributions have
shown – that country dierences aect business relations
negatively, which is why companies tend to choose a “close”
partner, physically, legally and mentally (Rugman & Verbeke,
2004). As shown in the FDI literature, these dierences can be
identied as dierent types of distances between the partners
(Ghemawat, 2001).
Despite the large number of contributions to the subject,
no consensus has yet been reached regarding how to specify
and measure the concept of distance in the international
business literature (Ambos & Håkanson, 2014). us, there
seems to be a lack of clarity concerning the dimensions of
distance as well as its measurement (Hutzschenreuter et
al., 2015). When considering all types of distance found
in the literature, the majority can be regrouped according
to the division of cultural, administrative, geographic and
economic distance, which constitutes the CAGE Distance
Framework (Ghemawat, 2016). Most studies tend to analyse
these dimensions separately, but Ghemawat oers a holistic
framework that is based on the research by Johanson and
Vahlne (1977). e industry and the countries involved in the
dierent dimensions may aect the uncertainty of coopera-
tion in dierent ways. e CAGE perspective is particularly
relevant to gravity studies, as Ghemawat (2016) himself uses
Gravity models to assess the impact of the dierent types of
distance on several international ows (but not on alliances).
e CAGE Distance Framework has already been tested in
an alliance context (Moalla, 2015), but to the best of our
knowledge, without applying the Gravity model.
H
Following the structure of the CAGE Distance Framework
mentioned above, we take a closer look at the four distance
dimensions to propose four hy potheses related to their impact
on the bilateral ows of alliances. In addition, according to the
basic variables in the Gravity model, we include a hypothesis
regarding the importance of the countries’ GDP levels.
Cultural distance
Cultural distance is most oen recognized as stemming from
informal institutions, such as national habits, beliefs, social
norms and values. ese are representative of their country a nd/
or their organization and determine how the individuals and
the organizations interac t with others (Porter, 1990; Ghemawat,
2001; Hofstede, 2001). Too much cultural distance between the
companies’ countries of origin is associated with a high degree
of uncertainty and with diculties in cooperation (Shenkar
et al., 2008; Håkanson & Ambos, 2010; Trompenaars, 2010). It
can create mistrust, misunderstandings, miscommunication
and individual conicts, which make the management of the
alliance dicult (Parkhe, 1998; Ambos & Ambos, 2009; Kim
& Parkhe, 2009). ese factors can aect both the probability
of market selection and the entry mode (Pedersen & Petersen,
2004; Chiambaretto & Wassmer, in press).
Out of the four types of distance in the CAGE Distance
Framework, cultural distance seems to be the most blurred
and has proven itself dicult to conceptualize and measure
(Shenkar, 2012; Christoersen, 2013). However, one example of
an oen-used measurement in distance research is Hofstede’s
(1980) cultural values, which were later redened by Kogut and
Singh (1988): (1) Power distance refers to a country’s acceptance
of inequality in social systems; (2) Individualism/collectivism
is the degree to which people look aer themselves and their
families (individua lism) versus the degree to which they identif y
with social groupings (collectivism); (3) Masculinity–femininity
refers to the preference for achievement versus aliation, as
well as to traditional role distinctions between the sexes; (4)
Uncertainty avoidance refers to the general level of discom
-
fort with unstructured or unusual circumstances within a
society. Later, Hofstede added two more dimensions: (5) Long
term orientation, which refers to how each society maintains
links with its own past while dealing with the challenges of
the present and the future; and (6) Indulgence, which refers to
the extent to which a society permits its members to enjoy life
and have fun. When measuring cultural distance, these six
cultural values are transformed into cultural scores that may
help to determine cultural distance. Despite the rich use of this
measure in international business research, Hofestede’s values
have been criticized for relying on narrow and outdated data as
well as for relying too much on Western values (Kogut & Singh,
1988; Shenkar, 2001). For these reasons, several frameworks
that encompass other cultural dimensions have been proposed
by researchers such as Trompenaars and Hampden-Turner
(1998) and Shalom Schwartz (2014). e best-known alter-
native is the Global Leadership and Organizational Behavior
Eectiveness (GLOBE) framework (House et al., 2004). Despite
the concerns raised and the alternative cultural measures
proposed, Hofestede’s index remains a dominant research
60 Management international / International Management / Gestión Internacional
instrument within culture-related studies (Dow, 2014), which
is why we use this measurement in our study.
When using Hofstede’s cultural values or other measures
in empirical studies, it is most oen found that a strong
level of cultural distance between the companies’ countries
of origin can generate a high level of uncertainty (Shenkar,
2012; Hutzschenreuter et al., 2015). Consequently, fewer
alliances should be observed between countries with a large
cultural distance. Accordingly, more alliances should be
found when there is a low level of cultural distance (Tung &
Verbeke, 2010). Similarly, the gravity literature on trade and
FDI ows has shown that cultural distance tends to reduce
the ows between countries (De Groot et al., 2004; Benassy-
Quéré et al., 2007).
We therefore propose the following hypotheses:
Hypothesis 1: e greater the level of cultural distance between
two countries, the fewer the number of alliances signed between
rms of these two countries.
Administrative distance
e creation of international alliances can be impacted by ins-
titutional arrangements in a given country because companies
have to adapt and make choices from a dened set of legitimate
options (Dong & Glaister, 2006). is determines the bounda ries
of opportunity and the constraints that companies encounter
when creating an international alliance (Frankel & Rose, 2002;
Chiambaretto, 2015). Legitimate options can be tra nslated into
national laws and policies, trading blocs, common currency
and political hostility (Ghemawat, 2007), and the dierences
between countries’ institutional settings generate a particular
level of administrative distance.
Local government policies are oen observed as the most
common barriers to cross-border cooperation. ese barriers
can be established either by the company’s home country or
by an international organization (Prévot & Meschi, 2006;
Lehiany & Chiambaretto, 2014; Estrin et al., 2016). If the legal
barriers are important, or if the administrative standards are
signicantly dierent between the companies’ countries of
origin, the administrative distance is considered to be large.
According to Ghemawat (2016), rms are less likely to develop
international interactions with countries that are adminis
-
tratively distant.
Furthermore, other studies arg ue that the presence of simi-
lar legal jurisdictions (i.e., low administrative distance) in
countries can facilitate market entries and favour cross-border
alliances (Berger et al., 2013; Brouthers, 2013). Research shows,
for example, that European cross-border alliances are asso-
ciated with a lower degree of legal uncertainty than are other
international alliances (Mayrhofer, 2004). When they have
similar levels of administration, rms from dierent countries
tend to present a higher level of t and can foster cooperation
(Mitsuhashi & Greve, 2009; Greve et al., 2014). We therefore
propose the following hypothesis:
Hypothesis 2: e greater the level of administrative distance
between two countries, the fewer the number of alliances signed
between rms of these two countries.
Geographical distance
Geographical distance, measured in kilometres between the
companies’ countries of origin, naturally plays a role when a
rm is choosing a partner abroad. e number of kilometres
is more or less important depending on the industry and the
sector of the partners, as it aects their transportation and
communication costs (Ghemawat, 2001; Berry et a l., 2010; Meyer
et al., 2011). In general, both tangible and intangible products,
as well as ser vices, are aected by geographic distance (Brewer,
2007). However, it is also important to take into account the
size and shape of each country (distance to borders, access to
waterways, t ransport and communications infrastructu re, etc.)
(Ghemawat, 2007). All these parameters have an eect on the
costs of the allia nce and must be ta ken into consideration when
choosing an international partner.
Research states that when look ing at the ows of internatio-
nal trade or FDI, national and local factors are more signicant
than previously assumed and that distance and borders still
play an important role in international business (Krugman
1997, Combes et al., 2005; Kleinert & Toubal, 2010). e num-
ber of trade ows decreases as the kilometres of transport
increase, which Combes et al. (2006) refer to as the “distance
tyranny (p.116).
e same trends are found in the international alliance
literature, where it is argued that the farther away a country is
from a home country, the harder it is for the two partners to do
business (Ganesa n et al., 2005; Kraus et al., 2010). Despite a few
contributions showing that geographical distance has a positive
inuence on international cooperation (Zaheer & Hernandez,
2011; Le Roy et al., 2016), the more general assumption is that
the farther away a company seeks a partner, the harder it will
be to conduct business with that partner (Kleinert & Toubal,
2010). is leads us to the following hypothesis:
Hypothesis 3: e greater the level of geographic distance between
two countries, the fewer the number of alliances signed between
rms of these two countries.
Economic distance
e less recognized dimension of distance is the economic
dimension, as it is dicult to derive theoretically (Hutzschen-
reuter et al., 2015). However, it is oen integrated into multidi-
mensional measures (Head & Mayer, 2013) and is, for example,
captured as dierences between the wea lth and sizes of countr ies,
as measured by GDP, per-capita incomes, human development
indices and dierences in resources (nancial, human, natura l,
infrastr ucture, information and knowledge) (Tinbergen, 1962;
Combes et al. 2006; Kleinert & Toubal, 2010).
Economic distance between the companies’ countries of ori-
gin may help shed some light on the choice of an international
partner, as economic distance is considered to have a negative
eect on internat ional business if the distance becomes too large
(Brewer, 2007; Berry et al., 2010). In addition, more alliances can
be found when the countries of the partnering rms a re simila r
in their levels of development, so that rms from rich countries
will partner more with rms from equally rich countries than
with rms from poorer countries (Ghemawat, 2007; Meschi
and Riccio, 2008). We therefore assume:
Gravity for Domestic and International Alliances: A CAGE Perspective 61
Hypothesis 4: e greater the level of economic distance between
two countries, the fewer the number of alliances signed between
rms of these two countries.
Market attractiveness
In addition to testing the four standard types of distance in the
CAGE Distance Framework, we also set a standard hypothesis
that is usually integrated into Gravity models.
It is shown in the alliance literature that rich countries
engage in more cross-border activities and alliances compa-
red with poorer countries because rich countries represent
attractive markets for foreign rms (Meschi & Riccio, 2008;
Tung & Verbeke, 2010). e concept of market attractiveness
is essential in the gravity literature, whether it is for trade or
FDI ows (Head & Mayer, 2013; Kleinert & Toubal, 2010).
Indeed, the larger the country, the larger the number of rms
and consumers, and consequently, the higher the likelihood of
observing interactions that involve rms from this country. We
thus state our nal hypothesis:
Hypothesis 5: e greater the economic size of two countries,
the greater the number of alliances signed between rms of these
two countries.
Methods
S 
To estimate our gravity equations for the ow of alliances, we
relied on the SDC (Securities Data Company) Platinum database,
which lists all public alliances arou nd the world (Schill ing, 2009).
It contains a large amount of information, compiled since 1970,
about alliances and joint ventures on a global scale. Alliances
are mostly concentrated in a limited number of countries. To
prevent our sample from having too many country pairs with
no alliances between them (which would imply a large num-
ber of zeros in our analyses), we focused our attention on the
bilateral ows of alliances among OECD countries and their
associate members (China, India, South Africa, Indonesia and
Brazil). Hereaer, the sample includes 39 countries. We have
paired the countries without considering the “direction” of the
alliances, yielding 741 country pairs (=39x38/2). In addition,
we have added the domestic alliance ow for each of the 39
countries (39 + 741). is gives us a sample of 780 observations,
which are detailed in Table 1. For the period of September 2014
to September 2015, 652 international and domestic alliances
(equity and non-equity agreements) have been created among
OECD and aliate members. Rega rding our sa mple, 49% of the
alliances are domestic and 51% are international. It is important
to stress that with regard to domestic alliances, we will expe-
rience a large number of zeros concerning the dierent types
of distance, but this is no less interesting for our analyses. As
shown by previous alliance research, a low distance (or even
a distance equal to zero) may be seen as an advantage when
looking for a partner and may thus have an impact on the
total number of alliances. In fact, any type of distance (from 0
upward) is important when estimating gravity models (Head &
2. Because of the many zeros that are not defined in t he logarithm, we have calcu lated the log values as log(1+x)
3. www.geert-hofstede.com/countries.html
Mayer, 2013). Table 1 below describes the number of alliances
between the country pairs in the database. It shows that most
country pairs (being the same or dierent countries) formed
no alliances during the period. When alliances are created
between two countries, the total number of alliances remains
limited. Only a few country pairs (less than 3%) formed more
than 10 alliances during the period.
TABLE 1
Descriptive statistics for the number
of alliances between country pairs
Number of alliances
Number of
countr y pairs
% of all country
pairs
0634 81.28%
176 9.74%
From 2 to 5 alliances 48 6.16%
From 6 to 10 alliances 9 1.15%
From 11 to 20 alliances 8 1.03%
From 21 to 30 alliances 3 0.38%
From 31 to 100 alliances 1 0.13 %
More than 100 alliances 10 .13%
V  
Dependent variable
e dependent variable, ALLij, measures the number of alliances
between country i and country j. As we will explain in more
detail below, we have used two methods to estimate the ow
of alliances: an OLS with log-linear regrwession (OLS) and a
Poisson Pseudo Maximum Likelihood (PPML)2. For the OLS
regression, we have taken the log of the dependent variable.
ere are, therefore, two types of dependent variables: LNALLij
(for the OLS) and ALLij (for the PPML).
Independent variables
Five independent variables are used in our models. e rst
four variables are related to the dierent types of distances
highlighted in the CAGE Distance Framework.
(1) e rst independent variable is cultural distance. It is
measured as t he log of the cultural distance between countries
i and j (LNCult_distij). To create this variable, we collected data
from the Hofstede Center’s website,3 where we identied each
country in the Hofstede Index. To merge these measures into
one cultural value, we have used the Euclidian index and the
Pythagorean eorem (Drogendijk & Slangen, 2006). e
distance has been calculated with the following formula:
a
1
b
1
( )
2+
a
2
b
2
( )
2+!+
an
bn
( )
2. e aggregated sum
represents the cultural distance between two countries.
62 Management international / International Management / Gestión Internacional
(2) e second independent variable assesses the ad ministrative
distance between two countries and is also measured as the
log of the administrative distance (LNAdm_distij) between
the two countries. For this variable, we relied on a construct
similar to that used for cultural distance but based on the data
available in the Doing Business Reports of the World Bank4.
From the Doing Business Reports, we used the Distance to
Frontier Score (DTF) from 2014 for each partnering country
to create the variable.
(3) e third distance-related variable is t he geographical distance
between the countries. It is measured as t he log of the distance
between countries i and j in kilometres (LNGeo_distij). e
geographic distance is measured between the most important
cities (in terms of population and economic activity) of the
two countries, which are – for the most part – also the ocial
capitals5. e data are provided by the CEPII’s GeoDist data-
base, which contains bilateral distances and country-specic
information on approximately 225 countries (Mayer & Zignago,
2011). e database also provides geographical distance mea-
sures for within-country ows based on measures computed
by weighted city population data from the principal cities in
a country. is information is helpful for our analysis when
dealing with domestic alliances for which we would like to
avoid assuming that the geographical distance between the
companies is equal to zero.
(4) e last distance-related variable is the economic distance
between the countries. It is measured as the log of dierences
4. www.doingbusiness.org/rankings
5. For 13 of the 225 countries, t he CEPII database considers that the capital is not the “economic center” of the country but another city. For these cases, the
dista nce data are computed from both the capital and the economic center city (Mayer & Zignago, 2011: 9).
6. www.hdr.undp.org/fr/composite/HDI
7. www.stats.oecd.org/index.aspx?queryid=350
between scores on t he Human Development Indices for countries
i and j (LNEco_distij) in 2014, which are available on the website
of the United Nations Development Program6.
In addition, to estimate the impact of market attractiveness
(5), and as a basic element of the Gravity model, we integrated
the log of the GDP in 2014 for country i and country j using
data from the OECD7 and created two variables: LNGDPi and
LNGDPj.
All the variables are summarized in Table 2 below.
D 
Historica lly, because the gravity model is a multiplicative model,
gravity equations used to be estimated using an OLS with
log-linear specication (Fratianni et al., 2011; Head & Mayer,
2013). We therefore apply this met hod to our study. However, a
log-linear specication raises several issues from an est imation
point of view, and nonlinear specications have been used
more extensively in recent years. Indeed, Silva and Tenreyro
(2006) show that in the presence of heteroscedasticity in the
error term, the log-linearization can cause the OLS estimator
to be biased. Furthermore, the log-linearization is incompa-
tible with the presence of zeros for the dependent variables
because several countries do not have any alliances between
them. Omitting these zero-valued observations would create a
biased sample that could lead to biased results (Helpman et al.,
2008). Silva and Tenreyro (2006) instead suggest using a Poisson
Pseudo Maximum Likelihood (PPML) model to estimate the
TABLE 2
Variable presentation
Theoretical variables Operationalized variables Name of variables Value Source
Dependent variable
Number of alliances The number of alliances between countries i
and j
LNALLij (OLS);
ALLij (PPML)
> 0 SDC Platinum
Independent variables
Cultural distance Log of Euclidian Index based on Hofstede’s
cultural dimensions
LNCult_distij > 0 The Hofstede Center
Administrative distance Log of the Euclidian Index based on the ease of
doing business in both countries
LNAdm_distij > 0 Doing Business
(The World Bank)
Geographic distance Log of the number of kilometres LNGeo_distij > 0 CEPII
Economic distance Log of Euclidian Index based on the Human
Development Indices in both countries
LNEco_distij > 0 Human Development
Indices
(United Nations
Develop Program)
Gravity for Domestic and International Alliances: A CAGE Perspective 63
parameters. PPML models are more robust in the presence of
heteroscedasticity of error terms and are able to address data
containing important zero-valued observations (Kleinert &
Toubal, 2010; Fratianni et al., 2011; Head & Mayer, 2013).
Nevertheless, despite a stronger robustness of the PPML
estimation, most articles using Gravity models combine OLS
and PPML estimators to look for potential di erences. Followi ng
this approach, we estimate the coecients for two equations.
e rst equation (1) is based on the OLS model, while the
second one (2) is based on the PPML model:
(1) LnAllij = α + β1LNCult_distij + βLNAdm_distij
+ β3LNGeo_distij + β4LNEco_distij + β5LNGDPi
+ β6LNGDPj + εij
(2) Allij = α + β1LNCult_distij + βLNAdm_distij
+ β3LNGeo_distij + β4LNEco_distij + β5LNGDPi
+ β6LNGDPj + εij
Results
D 
We provide descript ive statistics and the correlation matri x for
the variables in Table 3 below.
First, we nd a high correlation between the constant and t he
market attractiveness variables: LNGDPi (0.320) and LNGDPj
(0.378). We see that there is a negative correlation between the
dependent variable and the geographical distance variable
(-0.048). Together with t he variable of economic distance (0.008),
this is t he only correlation where the constant is not signicant.
e correlations with both cultural distance (-0.286) and ad mi-
nistrative distance (-0.095) are signicantly negative. ere is
no sign of multicollinearity (r > 9). is is conrmed by the
VIF (Variance Ination Factor), as we nd no values higher
than 2 (Neter et al., 1985).
L   PPML 
Table 4 helps us to analyse our two models, in which we test
the impact of dierent distance measures on the number of
alliances between country pairs. e table includes the results
from the OLS with log-linear regression (OLS) as well as from
the Poisson Pseudo Maximum Likelihood (PPML). We nd that
that all variables are highly signicant except administrative
distance and economic distance. We note that the signicance of
the variables remains the same for all estimation models used.
Nevertheless, because the for the PPML analysis is stronger
than that for the OLS, which indicates that this model better
explains alliance ows; below, we report only the results of the
PPML analysis (Silva & Tenreyro, 2006).
When looking into the results of the PPML analysis in grea-
ter depth, we nd, consistent with our hypothesis, that there
is a negative and signicant relation between our dependent
variable and the variable for cultural distance (β = -0.345,
p < 0.05). e larger the cultural distance between country i
and country j, the lower the number of alliances between the
countries. erefore, our results support hypothesis 1.
Second, we nd no signicant relation between the number
of alliances created and the administrative distance (
β
= 0.039,
p = 0.796). We therefore reject hypothesis 2.
ird, the geographic distance is found to have a negative
and signicant impact (β = -0.295, p < 0.05) on the dependent
variable. So, the larger the geographical distance between
country i and country j, the lower the number of alliances
between companies belonging to these countries. e results
are thus in line with our hypothesis 3.
Also, we nd that there is no signicant relation between
the dependent variable and the economic distance (β = 2.967,
p = 0.139). Based on the results, we reject hypothesis 4.
We further nd that there is a positive and signicant rela-
tion between the dependent variable Allij and the market
attractiveness of the countries: LNGDPi (β = 0.628, p < 0.001)
TABLE 3
Descriptive statistics and correlations
Mean Std. Dev. 1234567
Allij 0.2195 0 .561 1.000
LNCult_distij 3.993 0.984 -0.286* 1.000
LNAdm_distij 1.873 0.881 -0.095* 0.532* 1.000
LNGeo_distij 8.118 1.312 -0.048 0.516* 0.423* 1.000
LNEco_distij 0.071 0.070 0.008 0.284* 0.503* 0.421* 1.000
LNGDPi13.427 1.636 0.320* 0.017 0 .113 * 0.274* 0.219* 1.000
LNGDPj13.4 34 1.526 0.378* -0.008 0.038 0.178* 0.126* 0.025 1.000
64 Management international / International Management / Gestión Internacional
and LNGDPj (
β
= 0.924, p < 0.001). In other words, if the GDP
of country i or j increases, more alliances are expected to be
formed between rms from country i and country j. Based on
these results, we conrm hypothesis 5.
A 
Because our sample includes two dierent types of alliances
– domestic and international – one could question whether
the eect of the distances varies within each type of alliance.
For that reason, we have compared the results of two PPML
analyses: a sample including all alliances (domestic and inter-
national) and a sample including only international alliances.
As expected, we found some similarities and dierences in the
coecients. e signs and signicance of the coecients are
the same for four variables (LNGDPi, LNGDPj, LNAdm _distij
and LNGeo_distij); however, they dier for the variables that
measure economic and cultural distance. We explain these
dierences based on the fact that when dividing the sample,
we removed (or emphasized) some distance eects that play a
key role in the choice between a domestic or an international
partner. Because we wish to contribute to the international
business literature, where the choice between doing business
locally or internationally is central, we chose to discuss the
results based on the full sample in order to provide the most
complete picture of the distance eects.
Discussion and concluding remarks
T    G  
    
First, our analysis shows that there is a positive relation between
the number of alliances between country pairs and the eco-
nomic size of the countries involved in the alliance. Second,
we nd that there is a negative relation between the number
of alliances between a pair of countries and the geographical
distance between the countries. ese two results conrm the
theory behind the Gravity model, which, in our study, has also
proven to be valid in an alliance setting.
ird, the statistical results showed that other distance
dimensions from the CAGE Distance Framework might also
help explain the number of alliances between country pairs. We
found that too much cultural distance decreases the number
of alliances between countries. is is interesting in a global
world where rms can potentially work with anybody they
wish to but where cultural dierences apparently still create
obstacles to cooperation. is point conrms t he results of the
“allia nce” contributions in the literature that have highlighted
this relationship (Kaufmann & O’Neil, 2007; Meschi & Riccio,
2008; Beugelsdijk et al., 2014; Li & Parboteeah, 2015). It could
also indicate that the success of alliances depends heavily on
the people involved in t hem (Herrmann & Datta, 2002; Prévot
& Meschi, 2006; Ambos & Ambos, 2010), particularly where
cultural dierences can be considered a challenge.
Lastly, we found no relation between administrat ive distance
and economic distance and the number of alliances between
country pairs. We suspect that this is related to our sample
consisting of OECD countries and their partners. Regarding
administrative distance, we note that the composition of our
sample may lead to the inclusion of countries that are quite
close from an administrative point of view. Consequently, this
criterion does not explain most of t he variation in alliance ows
between country pairs, and thus it is not surprising to have a
non-signicant coecient.
Quite similar reasoning could be applied to economic dis-
tance. e OECD countries mainly consist of Western countries
and thus advanced economies; however, some of their key
partners are Brazil, India a nd China. ese emerging economies
have undergone explosive growth in recent years; they have
become global markets that many advanced economies seek
to enter using alliances (Ghemawat, 2016). For these reasons,
one could expect to nd many alliances between companies
from advanced and emerging economies, a nding that is not
congruent with the high level of economic distance between
these actors. is trend could be one explanation for the non-
validation of our hypothesis.
Overal l, we nd that the Gravit y model has proven itself usefu l
in a strategic alliance setting and, when integrating dierent
distance dimensions from the CAGE Distance Fra mework, it has
helped us to understand the structu re of alliance ows between
country pairs. Our results indicate that the distance dimensions
impact the number of alliances between countries dierently.
is discovery can be used to broaden our understanding of
which factors impact the choice of an alliance partner.
C    
e starting point of this paper was t he use of the Gravity model,
as applied in research on FDI. Beyond the FDI context, the gra-
vity literature has also been used in many other contexts, such
as trade ows (Head & Mayer, 2013), nancial ows (Kleinert
TABLE 4
Results of the linear regressions
Variables
Model OLS Model PPML
βSig. βSig.
(Constant) - ****
(0.216) 0.000 - ****
(0.292) 0.000
LNCult_distij -0.149****
(0.022) 0.000 -0.345**
(0.151) 0.022
LNAdm_distij 0.024
(0.025) 0.980 0.039
(0.151) 0.796
LNGeo_distij -0.034**
(0.017) 0.040 -0.295**
(0.12 6) 0.019
LNEco_distij -0.207
(0.289) 0.476 2.967
(2.007) 0.13 9
LNGDPi 0.116****
(0.011) 0.000 0.628****
(0.091) 0.000
LNGDPj 0.141****
(0.011) 0.000 0.924****
(0.10 7 ) 0.000
(OLS: R 2= 0.33; PPML: R2= 0. 65; *p < 0.1; **p < 0.05; ***p < 0. 01; ****p < 0.001).
Numbers in parentheses are standard errors.
Gravity for Domestic and International Alliances: A CAGE Perspective 65
& Toubal, 2010), immigration ows (Lewer & Van den Berg,
2008) and social networks (Combes et al., 2005). However, to
our knowledge, the gravity approach has never been used in a
strategic alliance setting. is is surprising because we know
that joint ventures account for 40% of FDI; therefore, there
seems to be much to learn about how the Gravity model can
be used to explain the formation of strategic alliances both
nationally and internationally.
With this study, we have conrmed that the Gravity model
is a useful tool when looking at alliance ows. We found that
the basic elements of the Gravity model (the negative eect
of geographic distance and the positive eect of the level of
economic development of the countries) are also valid in an
alliance context. Furthermore, we found that the Gravity model
is very well adapted to including other types of distances that
also aect the alliance ow, such as cultural distance.
We found it fruitful to divide distance between partners
into the dierent distance dimensions and to test these simul-
taneously. Using the Gravity model to test our hypotheses,
we found that the dierent distance dimensions impact the
alliance ow dierently. Our analysis contributes to the results
of several other studies that have highlighted the importance
of partner-related criteria and country dierences between
partners (Geringer, 1991; Mayrhofer, 2004; Kaufmann & O’Neil,
2007; Meschi & Riccio, 2008; Lavie et al., 2012). By testing the
distance dimensions simultaneously, we join the small group
of authors who have treated several distance dimensions at
the same time to obtain a more holistic understanding of the
impact of distance on international cooperation (Angué &
Mayrhofer, 2010; Berry et al., 2010; Moalla, 2015; Choi and
Contractor, 2016).
However, our results concerning administrative distance
and economic distance are dierent from the results of most
other studies in the ex isting literature (Nielsen, 2003; Majocchi
et al., 2013). We found that these types of distance do not
have a signicant impact on the number of alliances between
countries. We suspect that this result is related to the content
of our sample: OECD countries and their partners.
M 
is study aims at informing both academics and practitioners
by giving them insights into the eects of dierent distance
dimensions on partner selection. We also provide tools with
which to analyse and approach these dierences. ese tools
include the awareness of country dierences and the use of the
CAGE Distance Framework, as well as the use of the Gravity
model in a partner selection context. If managers do not take
this into account, they take the risk that country dierences
will inhibit – rather than reinforce – their strategies. We thus
hope that our ndings will serve as guidelines for practitioners
and be a useful complement to economic reports and analyses
produced by companies when looking for new partners for
alliances. Our results encourage decision makers to integrate
all of the distance dimensions into their analyses to obtain a
more complete picture of potential risks and advantages when
choosing an alliance partner.
L     
ese conclusions cannot be accepted without considering their
limitations, which oer interesting opportunities for further
studies. eoretically, the concept of distance has been treated
by other research disciplines but has been dened dierently,
as seen in the concept of proximity in economics (Porter,
1998) and in the concepts of embeddedness and syndication
networks in sociology (Sorenson & Stuart, 2001; Carrinca-
zeaux et al., 2008). It would be interesting to combine these
denitions and theories to expand our understanding of the
concept of distance.
Furthermore, we have not looked into the “direction” of the
alliances. Integrating an inward/outward perspective could
aect our ndings, as the objectives of these two types of
alliances are dierent (Welch & Luostarinen, 1993) and could
deepen our understanding of partner choices. Empirically, we
are aware that other explanatory factors may explain the choice
of an alliance partner. Other explanatory factors could include
company size, network dynamics, technology or production
complementarity, and norms and contract regulations, all
of which might be sector-dependent (Greve et al., 2014). e
dierent distance dimensions should have dierent eects
depending on the industry, as certain industries are more
distance-sensitive than others (Ghemawat, 2016). e same
could be said about the location of an alliance, as we found
some dierences between the eect of distances in domestic
and international alliances in our robustness analysis of the
results. It would be interesting to integrate a larger research
context by testing how the type and location of alliances, the
alliance industry and the object of the alliance are aected by
the dierent distance dimensions.
In addition, our approach was mainly static and did not
consider the impact of international experience or previous
alliances on the ows of alliances between countries. e dif-
ferent types of distance are in fact dynamic and change over
time. International experience allows companies to accumulate
skills and capabilities, and it is oen seen as a way to reduce
the liability of foreignness (Prévot & Meschi, 2006; Kaufmann
& O’Neil, 2007; Chakrabarti & Mitchell, 2013; Christoersen,
2013; Hutzschenreuter et al., 2015). Continuous contact increases
the level of trust between t he partners and thus facilitates their
cooperative relationship (Zhou & Guillén, 2015). Research also
shows that it is advantageous for the alliance managers in a
company to have cosmopolitan proles, which can mitigate
the eects of cultural dierences (Nielsen & Nielsen, 2011).
Along with the positive eect of international experience comes
the longevity of the alliance. It is shown that, for example,
longevity moderates the negative eect of cultural distance
(Meschi & Riccio, 2008). Extending our results to include the
above-mentioned perspectives could be a promising avenue
for future research.
66 Management international / International Management / Gestión Internacional
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... Finalement, notre recherche contribue à la littérature appréciant le poids des différents déterminants étudiés dans le choix des modes d'entrée sur les marchés étrangers (Angué et Mayrhofer, 2011;Engsig, Chiambaretto et Le Roy, 2018). Elle révèle que les quatre dimensions analysées n'ont pas le même impact sur la décision de réaliser des fusions-acquisitions internationales. ...
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