DOCUMENT DE TRAVAIL 2009-016 - FSA ULaval · point to the importance of cliques and structural holes for the explanation of interorganizational collaboration in a network setting

Publié par : Published by: Publicación de la:

Faculté des sciences de l’administration 2325, rue de la Terrasse Pavillon Palasis-Prince, Université Laval Québec (Québec) Canada G1V 0A6 Tél. Ph. Tel. : (418) 656-3644 Télec. Fax : (418) 656-7047

Édition électronique : Electronic publishing: Edición electrónica:

Aline Guimont Vice-décanat - Recherche et affaires académiques Faculté des sciences de l’administration

Disponible sur Internet : Available on Internet Disponible por Internet :

http://www5.fsa.ulaval.ca/sgc/documentsdetravail [email protected]

DOCUMENT DE TRAVAIL 2009-016

HOW NETWORKS MATTER: INSIGHTS FROM ARMED FORCES COLLABORATIVE ACTIVITY Yan CIMON Louis HÉBERT

Version originale : Original manuscript: Version original:

ISBN – 978-2-89524-342-7

Série électronique mise à jour : On-line publication updated : Seria electrónica, puesta al dia

09-2009

HOW NETWORKS MATTER: INSIGHTS FROM ARMED FORCES

COLLABORATIVE ACTIVITY

Yan Cimon,

Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Faculty of Business Administration, Université Laval Pavillon Palasis-Prince, Local 1513, Québec (Québec), Canada G1V 0A6 [email protected] , (418) 656-2131 x5675

Louis Hébert,

Department of Management, HEC Montréal, 3000, chemin de la Côte-Ste-Catherine, Montréal (Québec), Canada H3T 2A7 [email protected] , (514) 340-6334

ABSTRACT

What network characteristics drive organizations to collaborate with one another within a

social network setting? We put forth the importance of various perspectives on

embeddedness. Our paper draws on social network perspectives and the resource-based

view. We test our hypotheses on a sample of 37 armed forces that have collaborated

through large-scale international exercises from 1991 to 2001. We find that being a

member of many cliques has a positive and significant impact on collaboration between

armed forces. Furthermore, various structural hole measures yield mixed results. These

results provide evidence for the reconciliation of the closure and structural hole

perspectives of social networks.

Keywords

Social networks, embeddedness, collaboration, armed forces

Cimon & Hébert (2009) How networks matter

2

INTRODUCTION

At the time of writing these lines, it seems that social networks are all the rage. McKinsey

& Company, a consulting firm, advocates that employee social networks are a potential

value driver (Bryan, Matson, & Weiss, 2007). Facebook, MySpace and other social

networking sites allow individuals to “befriend” legions of others online (Bialik, 2007).

Research in corporate settings found that individual managers embedded in social

networks may derive benefits from their level of embededdness (e.g. Uzzi & Lancaster,

2003). But what does this mean for interorganizational collaboration? More to the point:

What network characteristics drive organizations to collaborate with one another within a

social network setting? In this article, we study the influence of network embeddedness

on collaboration.

Social networks constitute a set of relationships between actors (e.g. Inkpen & Tsang,

2005). Kogut (2000) adds:

“Networks are more than just relationships that govern the diffusion of

innovation and norms, or explain the variability of access to information

across competing firms. Networks constitute capabilities that augment

the value of firms (Kogut, 2000: 423)”

Past research on interorganizational collaboration in networks has focused on repeated

interactions or on the characteristics of ties per se. The collaboration-as-interactions view

puts an emphasis on repeated ties (see Goyal, 2003; Gulati, 1995a). Furthermore, these

repeated ties may be a key to the joint development of new products as they entail access

to new sets of capabilities (Bangens & Araujo, 2002).

The characteristics-of-ties view focuses on the type(s) of collaboration that takes place.

For example, collaborative efforts around R&D projects may be measured: as the

participation in R&D joint ventures (Benfratello & Sembenelli, 2002); through joint

R&D activities (Tether, 2002); R&D partnerships (Hagedoorn & Duysters, 2002); or joint

publication efforts by researchers from different organizations (Liebeskind, Oliver,

Zucker, & Brewer, 1996). Another way of framing this view comes from the

categorization of ties along a weak tie/strong tie dichotomy (Keister, 1999; Rowley,

Behrens, & Krackhardt, 2000). Finally, various other perspectives compete within this

view. While some researchers focus on evaluating the satisfaction with a given alliance


3

(Norman, 2004), others examine the motives behind ties such as knowledge sharing

routines (Dyer & Nobeoka, 2000) or licensing agreements for the establishment of a

dominant design (Khazam & Mowery, 1994).

It then follows that notwithstanding the view of collaboration espoused in previous

research, the influence of the architecture of interorganizational social networks on

collaboration between member organizations has been the subject of increasing attention

in the literature. Foundational work took their roots in structural sociology, from the

study of problems like job hunting (Granovetter, 1973) at an individual level to the

allocation of resources such as money and authority (Benson, 1975) on an

interorganizational level. Strategy scholars situated networks between markets and

hierarchies (Thorelli, 1986) that could reveal themselves to be “strategic” (Jarillo, 1988)

in the sense that they could constitute a source of competitive advantage for embedded

firms (see also Gulati, 1998; Uzzi, 1996). Networks are also important sources of value

for organizations. They represent a shared context where they are still able to differentiate

themselves from others (e.g. Huemer, 2004; Loveridge, 2002).

In this article, we examine the effect of embeddedness on collaboration in an

interorganizational network setting. We put forth a perspective on network embeddedness

anchored in the resource-based view (RBV) and in network research in strategy. We

point to the importance of cliques and structural holes for the explanation of

interorganizational collaboration in a network setting. We test our hypotheses on a

sample of armed forces. We find that cliques have a significant link with collaboration

while various structural hole measures yield mixed results. Our findings contribute to a

better understanding of the effects of embeddedness on collaboration in a social network

setting.

THEORY AND HYPOTHESES

The study of the relationship between collaboration and embeddedness may be traced

back to the origins of the resource-based view (RBV). According to classical

understandings of RBV, the organization is a portfolio of resources (Penrose, 1959;

Wernerfelt, 1984) that are heterogeneous if they are to yield a competitive advantage. But

in order to become a driver for s sustainable competitive advantage, a resource need to be


4

valuable, rare, imperfectly imitable, and non-substitutable (Barney, 1991). But as Nonaka

and Takeuchi (1995: 34-35) underline, quoting Penrose (1959: 25), resources are

interesting for what they become, thus for the capabilities they entail. Therefore the

heterogeneity in resource endowments will drive interorganizational collaboration as

organizations seek to access resources they don’t possess (Véry & Arrègle, 1996). As

such, the architecture of these collaborative endeavors thus takes place in networks

(Ahuja, 2000a; Ahuja, 2000b) where knowledge is combined and transferred (Inkpen et

al., 2005) and where firms learn to manage interdependencies (see Brusoni, Prencipe, &

Pavitt, 2001). Networks then become vehicles for diffusing innovation (Colombo &

Mosconi, 1995; Deroïan, 2002; Dosi, 1997; Jovanovic & MacDonald, 1994) as they hold

and create knowledge (Knight, 2002; Kogut, 2000) through embedded routines

(Andersen, 2003) since “social interaction in a group facilitates not only communication

and coordination, but also learning (Kogut & Zander, 1996: 510).”

Even though network structure is relatively persistent (Walker, Kogut, & Shan, 1997),

embeddedness nevertheless constitutes an advantage for organizations since it allows

them to access, and benefit from, resources that lie outside their boundaries. Rowley et al.

(2000) have shown through the study of horizontal ties, similar to the scale type in other

streams of research, that weak ties are positively associated to performance while strong

ties aren’t. Gulati (1999) finds that firms do access resources in interfirm networks and

that it influences their decisions about whether to form alliances or not. Kogut (2000)

pushes the argument one step further by putting forth the idea of “networks as capabilities

that augment the value of firms (Kogut, 2000: 423)”. Dyer and Nobeoka (2000) found

evidence that a firm’s learning capability may reside outside its boundaries. Indeed,

organizational routines are often embedded beyond their boundaries (see Sobrero &

Roberts, 2002). This explains in part that interorganizational ties have a positive effect on

the adoption of new organizational practices (Erickson & Jacoby, 2003) and by extension

underline the role and importance of embeddedness for collaboration. We thus further

examine the role of two modes of actor embeddedness: clique membership and structural

holes.


5

Clique membership

The density of interactions is positively related to network performance (Reagans &

Zuckerman, 2001). Also, being embedded in a network has a positive influence on the

trust between members if the exchange of information is reciprocal (Buskens & Weesie,

2000). Moody and White (2003), studying the relationship between structural cohesion

and nestedness through the comparison of two networks, found that the latter is positively

associated with the former and that this is favorable to the transmission of information

within the network. In fact, when looking at the configuration of R&D activities, it was

found that it differed between Japanese and US firms, the latter adopting an integrated

configuration while the former preferring hubs (Lam, 2003). Along the same lines, Pyka

and Saviotti (2001) show that network characteristics matter for innovation. Therefore,

the structure of relationships around a given actor matters to its collaborative efforts. The

direct implication is that clique membership (e.g. Gulati, 1999), i.e. belonging to tightly

connected subgroups of actors in a network, has a particular importance to collaborative

activity. Thus:

H1: Simultaneous membership in many cliques is positively associated

to collaboration in an interorganizational social network setting.

Structural holes

Structural holes (Burt, 1995) are another tool to understand the role of embeddedness in

the collaborative endeavors that take hold within social networks. This because “network

structure and network position affect how network collaboration will occur and between

which network actors’ collaboration will take place (Batt & Purchase, 2004: 170).” In

such a context, an organization may benefit from a central position, from the

entrepreneurial possibilities conferred by their ties with others, or from their hierarchical

status.

Centrality. Alliance blocks take place between complementary firms that compete against

other alliance blocks for the establishment of technological standards, the architecture of

individual blocks being contingent on the strength of a focal firm’s position

(Vanhaverbeke & Noorderhaven, 2001). Madhavan et al. (1998) confirmed the


6

importance of a firm’s centrality when events could cause shocks that would potentially

disrupt the networks’ configuration. Within a multinational firm’s internal network, the

centrality of a business unit or the headquarters allows more possibilities for resources

brokerage behavior an confers it some power (Ghoshal & Bartlett, 1990). It leads to the

following hypothesis:

H2: Organizational centrality is positively associated to collaboration

in an interorganizational social network setting.

Entrepreneurial possibilities. The competitive advantage of the firm depends on its

absorptive capacity (Cohen & Levinthal, 1990) for knowledge and on the creation of

capabilities and their recombination into innovative routines (Grant, 1996; Leonard-

Barton, 1995; Nonaka et al., 1995; Pavitt, 2002) that are embedded and replicated in

boundary-spanning networks (Andersen, 2003; Araujo, Dubois, & Gadde, 2003).

However, interorganizational collaboration within a social network is often constrained

by the number of ties along which organizations interact. Such constraint represents the

possibility for an organization to be entrepreneurial and is an element that is to be

considered as part of a structural hole maximization strategy that would confer actors

more value in a social network (Burt, 1995). If an organization is very constrained, then it

is forced to interact with only a limited number of others and should have sustained

collaborative activity with them. This implies that:

H3: An organization’s constraint is negatively associated to

collaboration in an interorganizational social network setting.

Hierarchical status. An organization’s hierarchical status in a network may influence its

access to a range of network resources. Hierarchy confers value to an actor in a social

network because of the possibility of informational arbitrage it enables (Burt, 1995). This

is coherent with Ahuja’s (2000a) results that direct ties have a direct and positive impact

on a firm’s innovation output. Along similar lines, Gulati (1995b), while studying

alliance formation, found that indirect ties are a good vehicle for information. Afuah

(2000) determined that the ties between a firm and its suppliers could constitute a source

of competitive advantage. Moreover, the type of tie, whether it be arm’s length (purely


7

transactional) or embedded (a reflection of sustained interaction) has a bearing on the

public or private nature of the information exchanged (Uzzi et al., 2003) and is probably a

reflection of status. We can then hypothesize that:

H4: The hierarchical status of an organization is positively associated

to collaboration in an interorganizational social network setting.

METHODS

Sample and Data

We examine how an organization’s own network characteristics drive its collaborative

activity. Our sample comprises 37 armed forces that collaborated through 58 large-scale

military exercises from 1991 to 2001. Of these 37 armed forces, 18 are NATO members

while 19 are not. The data we use originates from the Stockholm International Peace

Research Institute’s Facts on International Security Trends database (SIPRI, 2004). This

is a meta-database that collates data from universities, government agencies, international

organizations and leading think tanks on security-related international affairs.

Our sample offers many methodological advantages. Because they are not subject to

market pressures, armed forces are an ideal unit of analysis to study the network

characteristics of collaboration free of the noise induced by profit-generation or market-

related activities. Furthermore, since the collaborative activity recorded here involves the

participation in large-scale exercises, the data presents a more faithful (e.g. valid) picture

of partner desirability than datasets on the collaborative activity of for-profit firms or, in

the case of armed forces, on collaborative activities in military operations carried out in

armed conflicts notwithstanding their level of intensity. On one hand, the collaborative

endeavors of for-profit firms are subject to market or competitive pressures that render

difficult the isolation of their impetus to collaborate. On the other hand, the collaboration

of armed forces in armed conflicts is subject to a heavily politicized decision-making

process. However, in the case of large-scale military exercises, since they are operations

other than war, the decision to participate is made by high ranking officers generally free

of, or subject to very limited, political pressure.


8

Another advantage is that the primary goal of these exercises is for armed forces to learn

to work/fight together (ABCA, 2005: 8-1) through interactions (Darling, Parry, & Moore,

2005) of a situated nature (Knight, 2002) that eventually have an impact on operations

(Lin, Luby, & Wang, 2004). Few real-world contexts are so well delineated. The large-

scale exercises discussed here comprise between 3000 and 50 000 military personnel.

This variation in scale does not affect our study since the scope of these exercises is

similar. In fact, these events resemble corps-level exercises and are typically commanded

by high ranking generals.

Measures

Dependent variable. Our dependent variable is collaboration. It consists in the joint

participation of armed forces in large-scale international exercises on an annual basis.

While discrete repeated interactions have been hallmarks in the study of cooperation (e.g.

Beckman & Haunschild, 2002; Medlin, 2004), collaboration in these military exercises

are all but arm’s length. They require sustained and continuous liaison efforts prior to,

during, and after these events. They also provide a vehicle for exchanges that contribute

to learning (Liebeskind et al., 1996) if only because rubbing shoulders together helps

organizations improve their cooperative processes (Doz, 1996).

Independent variable. Our first independent variable is organizational clique

membership. Cliques are a measure of structural embeddedness. They consist in network

subsets of non-redundant contacts and therefore represent subsets of tightly linked actors

(Scott, 1991: 114). Furthermore, all actors a clique are in a dyadic relationship and

cliques cannot be contained in another clique (Wasserman & Faust, 1999: 254). This

measure was used in previous research to capture the network effects of firms engaging

in alliances (Gulati, 1999).

Our second independent variable is organizational centrality (closeness) (Freeman, 1979).

This measure refers to the geodesic distance between an ego actor and other nodes (e.g.

Marsden, 2002), i.e. other organizations, in our case other armed forces. It has been used

in strategy-related social network research (Gulati, 1999; Madhavan et al., 1998).


9

The third independent variable is the aggregated constraint on individual armed forces.

This structural hole measure put forth by Burt (1995) allows us to measure the limits on

an organization’s potential “entrepreneurial behaviour”. For example, if an armed force

has ties with only one other, its constraint is deemed maximal. In the case where it has

many ties, it may then be more entrepreneurial since it may shift its collaborative efforts

toward many other actors should it elect to do so.

Fourth, the hierarchy of an armed force is also included as an independent variable.

Hierarchy refers to an organization’s status in the network. Like Burt (1995: 70-71), we

calculate it using a Coleman-Theil index.

Control variables. We controlled for a variety of potential influences on our results. We

controlled for: the impact of intraorganizational capabilities on collaboration, the

characteristics of events, membership in an alliance, and the use of information

technologies (IT).

The impact of intraorganizational capabilities was measured using variables that capture

the technical abilities of armed forces and their collaborative experience. The technical

abilities are measured 1) by the ratio of heavy weapons to military personnel within a

given armed force and 2) by the relative importance of a country’s military spending. The

ratio of heavy weapons to personnel is the inverse of the one used by Benfratello and

Sembellini (2002) and is conceptually analogous to the R&D over sales ratio used

extensively in the strategy literature (Afuah, 1998; Bierly & Chakrabarti, 1996; Cohen et

al., 1990). It takes its roots in the two modes of resource allocation for defense spending

(Treddenick, 1998) : arm-the-man vs. man-the-arms; in other words labor intensity vs.

technical intensity. The relative importance of a country’s military spending is measured

using the ratio of military expenditures to gross domestic product (GDP). This measure

helps measuring the effect of the relative effort of each country to enhance its military

capabilities. Capital budgeting data could not be obtained for most of the armed forces in

this sample, thus the ratio of military expenditure was the most robust and accepted

method to circumvent this.

The collaborative experience of armed forces is another measure of intraorganizational

capabilities. Gulati (1995b), for example, found that dyads whose partners had more


10

collaborative experience had better probabilities of collaborating together or with others.

We model it as the total collaborations in past periods up to t-1, t referring to the current

year.

We also took in consideration the impact of the characteristics of the events (i.e. the

exercises). We created this variable by multiplying the size of the exercises (in number of

military personnel on the ground) and their length (in number of days). The size of an

exercise helps in checking if a critical mass is necessary for effective collaboration not

unlike what happens for the adoption of standards (Rothaermel, 2001). The duration of

exercises gives an indication with regards to the time it may take for collaboration to be

efficient. This idea comes from research on alliance duration in the context of learning

(Chen & Chen, 2002; Gulati, 1998). We also controlled for whether or not an armed force

belonged to an alliance by adding a dichotomous variable. We aim to check for the

existence of differentiated behaviour between alliance members and non-members.

Finally, we controlled for the use of IT at the national level using the ratio of internet

users per PC as a simple but robust measure of orientation toward technology.

Descriptive statistics and correlations are presented in table 1.

------------------------------------------

Insert table 1 about here

------------------------------------------

Statistical techniques and Data Management

We computed all of the social network data using NETMINER II (Cyram, 2003). We

performed a preliminary analysis, as suggested by Scott (1991), by mapping the

interorganizational social network that resulted from armed forces collaboration over the

10 year period covered by the sample. The network proved to be visualizable and did not

possess any discontinuity that may have prevented further analysis (see McGrath,

Krackhardt, & Blythe, 2003).

After making sure the network would be analyzable, we made sure that our missing data

was random and proceeded to input it by regressing it so we wouldn’t negatively impact

variance (Hair, Anderson, Tatham, & Black, 1998). We also were confronted with a

small number of outliers and decided to keep them in the sample (Green & Salkind, 2003;


11

Hair et al., 1998). Our tests for interactions between independent variables were not

conclusive.

We innovate from the previous literature that makes abundant use of regression

techniques (e.g. Benner & Tushman, 2002; Chung, Singh, & Lee, 2000; Powell, Koput,

& Smith-Doerr, 1996) by concurrently using panel data analysis (Hsiao, 2003) and

hierarchical linear modeling (Luke, 2004; Raudenbush & Bryk, 2002) to measure the

impact of network characteristics on collaboration. We use panel data analysis because

our data consists in time series. This technique is also very robust (Hsiao, 2003). It may

not however help us to fully take into account the presence of multilevel effects in our

data. Therefore, hierarchical linear modeling also proves to be particularly well suited for

this research since our time series may be construed as multiple measures on the same

individuals. It helps dealing with the limitations regarding degrees of freedom present in

more conventional regression techniques (Luke, 2004).

After obtaining our results, we then conducted in-depth semi-structured confirmatory

interviews with high-ranking officers. They were selected using the “reasoned choice

technique” (Royer & Zarlowski, 1999) on the basis of their international field experience,

in the context of multinational operations, exercises, and staff assignments.

Validity and reliability

We took the necessary steps to ensure our results were valid and reliable (Carmines &

Zeller, 1979). On one hand, the validity is increased by our careful use of recognized

statistical techniques that are gaining ground in our field. Also, the data covers the period

immediately following the fall of communism until, but not including, 9-11. This implies

that these two major world events are not disrupting our data. While no collaborative

activity was recorded for the year 1994, most probably because of the war in the Balkans,

we found no evidence of a statistically significant shift in the data.

On the other hand, the reliability of our results is ensured by our concurrent use of two

statistical techniques that yield similar results. Our research covering a 10-year period,

we did not account for tie decay: Burt’s suggestions did not apply very well to the context

and data of this research; and past empirical research either arbitrarily set the timeframe

for decay (Gulati, 1995b) or found that there wasn’t much bias induced by ignoring this


12

phenomenon (Hayward, 2002). We also verified that we did not “double count” ties

(Fafchamps & Minten, 2002).

RESULTS AND DISCUSSION

Panel models

Models 1 to 4 are two-way fixed effects panel models, results are shown in table 2. We

performed our analyses with the SAS 8 software. We used the TSCSREG procedure on

our balanced panel. We used two-way models in order to control for potential

heterogeneity bias (Hsiao, 2003). Selectivity bias was controlled by using the Hausman

test (Hausman, 1978) which significantly rejected the use of random effects for all

models.

Models 1, 2 and 3 test the effect of embeddedness without the controls associated to

intraorganizational capabilities. Model 4 is the full model.

------------------------------------------


------------------------------------------

Models 1, 3 and 4 show that cliques are strongly related to collaboration (p < 0.001)

anytime they are included in a model. Centrality, for its part, is only significant in model

2 (p < 0.001) where the clique variable is not included. It is also of interest to note that

constraint and hierarchy are not significant in models 2, 3, and 4 where they have been

considered. Our controls also demonstrate limited levels of association with

collaboration. Our full model shows intraorganizational capabilities (i.e. technical

intensity and collaborative experience) not to be significantly related to collaboration.

Membership in an alliance is not significant in any model. While events are significant at

p < 0.001 for all models, IT shows a negative sign and is significant at least at p < 0.05

for models 1, 2, 3, and 4. Models 3 and 4 are the best models (R² = 0.73).

HLM Models

Models 5 to 9 are the two-level HLM models, results are presented in table 3. We

performed our analyses using the HLM 6 software. The HLM technique is useful in


13

dealing with correlated error structures and allows for greater degrees of freedom than

traditional regression techniques (Luke, 2004). Following Luke’s (2004) advice, we

ensured that level 1 residuals were independent and normally distributed around zero

within groups, and that random effects were normally distributed and independent with

an average of zero across groups. We performed our estimations with restricted

maximum likelihood models since they entail less bias than full maximum likelihood

models in the random effects while producing the same results for the fixed effects (Luke,

2004: 26). We kept the fixed effects estimations with robust standard errors since they did

not differ significantly from other estimations, thereby confirming the absence of

specification errors for our models (see Raudenbush et al., 2002: 276). We did not center

the variables since we did not experience important colinearity issues.

Model 5 is the blank model that tests whether or not we may use 2-level models. Models

6, 7 and 8 test the impact of the various modes of embeddedness without the controls

associated to intraorganizational capabilities while model 9 represents the full model.

------------------------------------------


------------------------------------------

Model 5, the null model, provides evidence that we are faced with a multilevel

phenomenon by showing that a significant portion of the variance in collaboration is

explained by the armed forces (i.e. organizations) under study (χ² = 445.78; df = 36; p <

0.001; ICC = 0.51). The clique variable is significant (p < 0.001) in models 6, 8, and 9. In

model 7, centrality (p < 0.001) and hierarchy (p < 0.05) are significant. Hierarchy is also

significant in models 7 and 8 (p < 0.001).

The alliance variable is significant in model 7 (p < 0.001). IT is negative and significant

(p < 0.05). Our best model is model 8 (deviance = 1026.82) immediately followed by

model 9 (deviance = 1028.24), the full model, since they are the models that minimize

deviance. However, model 9 doesn’t have significant random effects (χ² = 31.86; df = 35;

p > 0.5; ICC = 0.00), but it is still of interest to note that it is the only model where our

control for collaborative experience is significant (p < 0.05).


14

Confirmatory interviews

Following the above results, we conducted a series of semi-structured confirmatory

interviews with high-ranking military officers in order to add depth and richness to our

results (Cooper & Schindler, 2003: 325). As mentioned earlier, we used the reasoned

choice technique (Royer et al., 1999) to select the participants. The high-ranking officers

we interviewed were chosen on the basis of their extensive international experience

during operations and international exercises such as the ones in our sample. We ensured

the multiplicity of sources (Yin, 1994) by including one US Air Force officer in the

sample to cross-validate what Canadian Army officers had shared. We also made sure

they varied in rank, by having a spectrum that ranged from a lieutenant-colonel to a

brigadier-general equivalent. The profile of interviewees is presented in table 4.

We mitigated sources of error by clearly informing participants of our research objective,

scope and context. We scheduled the interviews a short while after our initial contact with

participants and after obtaining their consent. This allowed them to gather information

they would not otherwise have at hand and it enabled them to organize their thoughts

(Doz, 1996). We reduced the possible biases induced by non-verbal factors by conducting

phone interviews with all participants. After the completion of interviews, we sent our

notes about their respective interview to participants for feedback and additional

comments.

------------------------------------------


------------------------------------------

Interviewees all agreed on the importance of collaboration in a network setting. They

contended that large-scale international exercises promoted cross-learning opportunities

and interoperability between armed forces. On the importance of cliques, they mentioned

that “Coalitions […] are important (CA001)” adding that “the synergy and the multiplier

effect that come from interoperability are implied by joint forces in a coalition (US001).”

Centrality was also mentioned to be a way of increasing the amount of military

capabilities one could access. Also, some level of constraint was perceived to be

inevitable since depending on other organization was necessary as self-sufficiency did not

prove a viable option. Along the same lines, hierarchy was also mentioned to have an


15

impact on collaboration. One participant eloquently put it this way: “We won’t go alone

into operations. […] We shall lead some of them (CA002).” With regards to our control

variables, it is interesting to note that the interviewees felt some weight should be given

to technical intensity and military spending. They also felt it was important to belong to

an alliance. They also felt that IT may cause technical blunders that render collaboration

more difficult. And even though they felt that events themselves were somewhat

important to explain collaboration, they felt that collaborative experience would have a

strong impact on collaboration.

Hypotheses results

Our hypotheses results are summarized in table 5. We discuss their implications below.

Hypothesis 1. The first hypothesis, which stated that membership in many cliques is

positively associated to collaboration in an interorganizational social network setting, is

supported. The association between collaboration and our clique variable is supported (p

< 0.001) by both techniques used. Interviewees also found it to be important and relevant

for this research.

These results enable us to reconcile the arguments put forth by Burt (1995), for structural

holes, and Coleman (1988), for the benefits of network closure. In fact, closed-loop

approaches have traditionally been pitted against the structural hole view. The former

being associated to high levels of trust among network members with few heterogenous

information available and the latter associated to a good vehicle for the circulation of

heterogeneous information whit the caveat of a lower trust environment. Finding that

simultaneously belonging to many cliques is positively and significantly related to

collaboration has two important positive consequences: 1) it enables an armed force to

benefit from the elevated trust that comes from belonging to individual cliques and 2) it

still allows access to heterogeneous information from belonging to many such cliques.

This result makes for an optimal environment where actors may share novel information

in a high trust environment. A good example in our sample is the Polish Armed Forces.

They remain close to some former Warsaw Pact members and with many NATO

countries; they are also knowledgeable of their partners’ doctrines and equipment. This


16

makes them valuable in the context of the War on Terror since a number of rogue states

employ the doctrinal concepts and equipment of the former Soviet Union.

Hypothesis 2. We found partial support for the second hypothesis that organizational

centrality is positively associated to collaboration in an interorganizational social network

setting. Past research has shown it to be positively associated with an actor’s performance

in a given network (Ahuja, Galletta, & Carley, 2003) and with their access to information

(Borgatti & Cross, 2003). We found centrality appears as positive and significantly

associated to collaboration only when the clique variable is not included in models.

Such partial support may be explained by a possible substitution effect between cliques

and centrality, cliques overtaking centrality when they are jointly considered. It is also

possible that this may be attributable to the high maintenance costs of a central position in

a given network. Thus, a peripheral but “well connected” could be as advantageous in

terms of accessing new information with less maintenance costs. In our sample, the most

central armed force belongs to the United States. It is able to keep its central position

because of its large endowment in military resources. However, other armed forces like

Canada’s and Belgium’s that are not able to support the elevated costs of centrality adopt

a peripheral position since their dominant strategy cannot be associated with centrality.

Hypothesis 3. Our third hypothesis stated that an organization’s aggregated constraint is

positively associated to collaboration in an interorganizational social network setting. It is

not supported. This may be attributed to the will of armed forces to diversify their

collaboration-related risks by trying to reduce dependency on any given actor through the

maintenance of many ties with other armed forces. For example, the Polish Armed Forces

collaborate with the UK’s and Czech Republic’s independently of other ties it might have

with other armed forces.

Hypothesis 4. The fourth hypothesis, that the hierarchical status of an organization is

positively associated to collaboration in an interorganizational social network setting, is

partially supported. Network status may have some importance for potential collaborators

or for information information-seeking (e.g. Cross, Rice, & Parker, 2001). The hierarchy


17

variable is significant (p < 0.01) in two of the three HLM models it was included in, and

thus has a very limited impact on collaboration in the context of this study.

While it may be interesting for an actor to collaborate with another of different

hierarchical status, this difference in status may neutralize any potential advantage

gained. This may be in part attributable a possible the possible differentiation in tie decay

associated with hierarchical status. In other words, it may be advantageous for the

Morrocan Armed Forces to collaborate with the Armed Forces of France, the latter

having a privileged position as a member of many cliques and of NATO. However, this

relationship wouldn’t necessarily be of a direct help to the armed forces of Morocco

should they collaborate with Sweden’s. The insight of a potentially differentiated decay

according to one’s hierarchical status comes from the example of the Canadian Armed

Forces. Very active at the beginning of the period, their hierarchical status in the network

decreased over time but it remained a player because of close ties with the USA and the

UK that maintained a high hierarchical status. The same may be said of Turkey because

of its privileged links with some NATO partners.

------------------------------------------


------------------------------------------

CONCLUSION AND IMPLICATIONS

Contributions and limits

We found that simultaneous membership in many cliques is strongly associated with

collaboration. While our hypotheses on the effect of centrality and hierarchy were

partially supported, the one about the effect of constraint wasn’t. This implies that our

contribution is twofold: 1) our results stem from an international interorganizational

sample which are hard to come by when studying these types of social networks; 2) our

research is grounded in two recognized and emerging quantitative techniques

supplemented with a qualitative confirmation that adds richness and strong internal

validity.

This study bears some limits. The first important limit is that, by concentrating on armed

forces, the generalizability of this research and external validity may be affected.


18

Collaboration between armed forces may be a signal that is more important in this sector

than others thus emphasizing needs for the replication of this study across many

industries. A second limit is related to the collaborative environment and organizational

settings that are not shaped by market forces. While these allow us to study the effects of

embeddedness in a “noiseless vacuum”, it nonetheless constitutes a challenge. It could be

circumvented by looking at other actors involved in different collaborative settings and

comparing the proportion of variance in collaborative activity that is attributable to the

environment. A third limit consists in access to the type of data we used. Such research is

most data intensive therefore creating challenges of its own. On one hand, the data is

most often costly, proprietary and is compiled by specialized firms or think tanks for thei

own needs. On the other hand, these data are international in nature and this leads to

important efforts in standardizing the different data series. Beyond these limits, however,

lie crucial implications for both academics and practitioners.

Implications for academics

This research is positioned in a stream that aims at better understanding

interorganizational collaboration in a network setting. It contributes to a better

understanding of “positional strategies” and the implications of organizations’

“relationality”, which we would define as their propensity to embed in networks of

continuous relationships. This enables researchers to move beyond the traditional weak

tie/strong tie dichotomy (see Coleman, 1988; Granovetter, 1973) that is very useful but

descriptive research, but does not fully address the challenges behind studying network

characteristics as continuous variables. Such a transition would have the benefit of giving

researchers a common quantitative framework.

Implications for practitioners

Notwithstanding the fact that our sample was composed of armed forces, this research

has many implications for practitioners in a variety of organizational settings. First,

managers need to realize the importance for their organizations of being simultaneously a

member in many groups of tightly linked actors (i.e. cliques) since they can access

multiple sources of new information and benefit from a high trust environment. Managers


19

also need to understand that centrality, while possibly good for their organization’s ego,

may have but limited advantages because it is a very resource-intensive strategic choice.

Second, there is tremendous benefit in understanding the architecture of the

interorganizational social network that surrounds a given organization. This provides for

a better strategic evaluation of actual and potential partners that classical strategic

perspectives anchored in industrial organization and the resource-based view.

REFERENCES

ABCA. 2005. Coalition Operations Handbook. Rosslyn, VA: The ABCA Programs

Office.

Afuah, A. 1998. Innovation Management : Strategies, Implementation and Profits.

Oxford: Oxford University Press.

Afuah, A. 2000. How much do your co-opetitors' capabilities matter in the face of

technological change? Strategic Management Journal, 21(3): 387-404.

Ahuja, G. 2000a. Collaboration Networks, Structural Holes, and Innovation: A

Longitudinal Study. Administrative Science Quarterly, 45: 425-455.

Ahuja, G. 2000b. The duality of collaboration: Inducements and opportunities in the

formation of interfirm linkages. Strategic Management Journal, 21(3): 317-343.

Ahuja, M. K., Galletta, D. F., & Carley, K. M. 2003. Individual Centrality and

Performance in Virtual R&D Groups: An Empirical Study. Management Science,

49(1): 21-38.

Andersen, P. H. 2003. The embeddedness of selfish routines: How routines are replicated

in business networks. Industry and Innovation, 10(2): 159.

Araujo, L., Dubois, A., & Gadde, L.-E. 2003. The Multiple Boundaries of the Firm.

Journal of Management Studies, 40(5): 1255-1277.

Bangens, L., & Araujo, L. 2002. The structures and processes of learning. A case study.

Journal of Business Research, 55(7): 571-581.

Barney, J. 1991. Firm Resources and Sustained Competitive Advantage. Journal of

Management, 17(1): 99-120.


20

Batt, P. J., & Purchase, S. 2004. Managing collaboration within networks and

relationships. Industrial Marketing Management, 33(3): 169-174.

Beckman, C. M., & Haunschild, P. R. 2002. Network learning: The effects of partners'

heterogeneity of experience on corporate acquisitions. Administrative Science

Quarterly, 47(1): 92-124.

Benfratello, L., & Sembenelli, A. 2002. Research joint ventures and firm level

performance. Research Policy, 31: 493-507.

Benner, M. J., & Tushman, M. 2002. Process Management and Technological

Innovations: A Longitudinal Study of the Photography and Paint Industries.

Administrative Science Quarterly, 47: 676-706.

Benson, K. J. 1975. The interorganizational Network as a Political Economy.

Administrative Science Quarterly, 20(2): 229.

Bialik, C. 2007. Sorry, You May Have Gone Over Your Limit Of Network Friends. Wall

Street Journal - Eastern Edition, 250(117): B1-B1.

Bierly, P., & Chakrabarti, A. 1996. Generic Knowledge Strategies in the U.S.

Pharmaceutical Industry. Strategic Management Journal, 17: 123-135.

Borgatti, S. P., & Cross, R. 2003. A relational view of information seeking and learning

in social networks. Management Science, 49(4): 432-445.

Brusoni, S., Prencipe, A., & Pavitt, K. 2001. Knowledge specialization, organizational

coupling, and the boundaries of the firm: Why do firms know more than they

make? Administrative Science Quarterly, 46(4): 597-621.

Bryan, L. L., Matson, E., & Weiss, L. M. 2007. Harnessing the power of informal

employee networks The McKinsey Quarterly(4): 1-11.

Burt, R. S. 1995. Structural Holes: The Social Structure of Competition. Cambridge

MA: Havard University Press.

Buskens, V., & Weesie, J. 2000. An experiment on the effects of embeddedness in trust

situations - Buying a used car. Rationality and Society, 12(2): 227-253.

Carmines, E. G., & Zeller, R. A. 1979. Reliability and Validity Assessment. Newbury

Park CA: Sage.

Chen, H., & Chen, T.-J. 2002. Asymmetric strategic alliances: A network view. Journal

of Business Research, 55(12): 1007-1013.


21

Chung, S., Singh, H., & Lee, K. 2000. Complementarity, status similarity and social

capital as drivers of alliance formation. Strategic Management Journal, 21(1): 1.

Cohen, W. M., & Levinthal, D. A. 1990. Absorptive Capacity: A New Perspective on

Learning and Innovation. Administrative Science Quarterly, 35: 128-152.

Coleman, J. S. 1988. Social Capital in the Formation of Human Capital. American

Journal of Sociology, 94: S95-S120.

Colombo, M. G., & Mosconi, R. 1995. Complementarity and Cumulative Learning

Effects in the Early Diffusion of Multiple Technologies. The Journal of

Industrial Economics, 43(1): 13-48.

Cooper, D. R., & Schindler, P. S. 2003. Business Research Methods (8 ed.). Boston MA:

McGraw Hill/Irwin.

Cross, R., Rice, R. E., & Parker, A. 2001. Information seeking in social context:

Structural influences and receipt of information benefits. Ieee Transactions on

Systems Man and Cybernetics Part C-Applications and Reviews, 31(4): 438-448.

Cyram. 2003. Netminer II, 2.4.0 ed. Seoul: Cyram Co.

Darling, M., Parry, C., & Moore, J. 2005. Learning in the Thick of It. Harvard Business

Review, 83(7/8): 84-92.

Deroïan, F. 2002. Formation of social networks and diffusion of innovations. Research

Policy, 31(5): 835-846.

Dosi, G. 1997. Opportunities, Incentives and the Collective Patterns of Technological

Change. The Economic Journal, 107(444): 1530-1547.

Doz, Y. L. 1996. The Evolution of Cooperation in Strategic Alliances: Initial Conditions

or Learning Processes? Strategic Management Journal, 17(Summer Special

Issue: Evolutionary Perspectives on Strategy): 55-83.

Dyer, J. H., & Nobeoka, K. 2000. Creating and managing a high-performance

knowledge-sharing network: The Toyota case. Strategic Management Journal,

21(3): 345-367.

Erickson, C. L., & Jacoby, S. M. 2003. The effect of employer networks on workplace

innovation and training. Industrial & Labor Relations Review, 56(2): 203-223.

Fafchamps, M., & Minten, B. 2002. Returns to social network capital among traders.

Oxford Economic Papers, 54(2): 173-206.


22

Freeman, L. C. 1979. Centrality in social networks conceptual clarification. Social

Networks, 1(3): 215-239.

Ghoshal, S., & Bartlett, C. A. 1990. The Multinational Corporation as an

Interorganizational Network. Academy of Management Review, 15(4): 603.

Goyal, S. 2003. Learning in Networks: a survey, Economic Discussion Paper no. 563:

66. Colchester, UK: University of Essex.

Granovetter, M. 1973. The Strength of Weak Ties. American Journal of Sociology,

78(6): 1360-1380.

Grant, R. M. 1996. Prospering in Dynamically-Competitive Environments:

Organizational Capability as Knowledge Integration. Organization Science, 7(4):

375-387.

Green, S. B., & Salkind, N. J. 2003. Using SPSS for Windows and Macintosh:

Analyzing and Understanding Data (3 ed.). Upper Saddle River NJ: Prentice

Hall.

Gulati, R. 1995a. Does familiarity breed trust? The implications of repeated ties for

contractual choices in alliances. Academy of Management Journal, 38(1): 85.

Gulati, R. 1995b. Social structure and alliance formation patterns: A longitudinal

analysis. Administrative Science Quarterly, 40(4): 619.

Gulati, R. 1998. Alliances and Networks. Strategic Management Journal, 19: 293-317.

Gulati, R. 1999. Network location and learning: The influence of network resources and

firm capabilities on alliance formation. Strategic Management Journal, 20(5):

397-420.

Hagedoorn, J., & Duysters, G. 2002. Learning in dynamic inter-firm networks: The

efficacy of multiple contacts. Organization Studies, 23(4): 525-548.

Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. 1998. Multivariate Data

Analysis (5 ed.). Upper Saddle River NJ: Prentice Hall.

Hausman, J. A. 1978. Specification Tests in Econometrics. Econometrica, 46(6): 1251-

1271.

Hayward, M. L. A. 2002. When do firms learn from their acquisition experience?

Evidence from 1990-1995. Strategic Management Journal, 23: 21-39.


23

Hsiao, C. 2003. Analysis of Panel Data (2 ed.). Cambridge, UK: Cambridge University

Press.

Huemer, L. 2004. Balancing between stability and variety: Identity and trust trade-offs in

networks. Industrial Marketing Management, 33(3): 251-259.

Inkpen, A. C., & Tsang, E. W. K. 2005. Social Capital, Networks, and Knowledge

Transfer. Academy of Management Review, 30(1): 146-165.

Jarillo, C. J. 1988. On strategic networks. Strategic Management Journal, 9: 31-41.

Jovanovic, B., & MacDonald, G. M. 1994. Competitive Diffusion. Journal of Political

Economy, 102(1): 24-52.

Keister, L. A. 1999. Where do strong ties come from? A dyad analysis of the strength of

interfirm exchange relations during China's economic transition. International

Journal of Organizational Analysis, 7(1): 5-24.

Khazam, J., & Mowery, D. 1994. The Commercialization of RISC: Strategies for the

Creation of Dominant Designs. Research Policy, 23: 89-102.

Knight, L. 2002. Network learning: Exploring learning by interorganizational networks.

Human Relations, 55(4): 427-454.

Kogut, B. 2000. The network as knowledge: Generative rules and the emergence of

structure. Strategic Management Journal, 21(3): 405-425.

Kogut, B., & Zander, U. 1996. What firms do? Coordination, identity, and learning.

Organization Science, 7(5): 502.

Lam, A. 2003. Organisational Learning in Multinationals: R&D Networks of Japanese

and U.S. MNEs in the U.K., DRUID Working Paper No 03-03: 52.

Leonard-Barton, D. 1995. Wellsprings of Knowledge. Boston MA: Harvard Business

School Press.

Liebeskind, J. P., Oliver, A. L., Zucker, L., & Brewer, M. 1996. Social Networks,

Learning, and Flexibility: Sourcing Scientific Knowledge in New Biotechnology

Firms. Organization Science, 7(4): 428-443.

Lin, G. Y., Luby, R. E. J., & Wang, K.-Y. 2004. New Model for Military Operations.

ORMS Today, 31(6): 26.


24

Loveridge, R. 2002. Incorporating the Multinational: Socio-technical Interfaces between

the MNC Affiliate and the Host Country. Asian Business & Management, 1(2):

153.

Luke, D. A. 2004. Multilevel Modeling. Thousand Oaks, CA: Sage.

Madhavan, R., Koka, B. R., & Prescott, J. E. 1998. Networks in Transition: How Industry

Events (Re)Shape Interfirm Relationships. Strategic Management Journal,

19(5): 439-459.

Marsden, P. V. 2002. Egocentric and sociocentric measures of network centrality. Social

Networks, 24(4): 407-422.

McGrath, C., Krackhardt, D., & Blythe, J. 2003. Visualizing Complexity in Networks:

Seeing Both the Forest and the Trees. Connections, 25(1): 37-47.

Medlin, C. J. 2004. Interaction in business relationships: A time perspective. Industrial

Marketing Management, 33(3): 185-193.

Moody, J., & White, D. R. 2003. Structural Cohesion and Embeddedness: A Hierarchical

Concept of Social Groups. American Sociological Review, 68(1): 103-127.

Nonaka, I., & Takeuchi, H. 1995. The Knowledge Creating Company : How Japanese

Companies Create the Dynamics of Innovation. NY: Oxford University Press.

Norman, P. M. 2004. Knowledge acquisition, knowledge loss, and satisfaction in high

technology alliances. Journal of Business Research, 57(6): 610-619.

Pavitt, K. 2002. Innovating routines in the business firm: what corporate tasks should

they be accomplishing? Industrial and Corporate Change, 11(1): 117-133.

Penrose, E. T. 1959. The Theory of the Growth of the Firm. NY: John Wiley & Sons.

Powell, W. W., Koput, K. W., & Smith-Doerr, L. 1996. Interorganizational Collaboration

and the Locus of Innovation: Networks of Learning in Biotechnology.

Administrative Science Quarterly, 41(1): 116-145.

Pyka, A., & Saviotti, P. 2001. Innovation Networks in the Biotechnology-Based Sectors,

Discussion Paper no. 205: 35. Augsburg: Universitaet Augsburg - Institute for

Economics.

Raudenbush, S. W., & Bryk, A. S. 2002. Hierarchical Linear Models: Applications and

Data Analysis Methods (2 ed.). Thousand Oaks, CA: Sage.


25

Reagans, R., & Zuckerman, E. W. 2001. Networks, diversity, and productivity: The

social capital of corporate R&D teams. Organization Science, 12(4): 502-517.

Rothaermel, F. T. 2001. Incumbent's Advantage through Exploiting Complementary

Assets via Interfirm Cooperation. Strategic Management Journal, 22(6-7): 687-

699.

Rowley, T., Behrens, D., & Krackhardt, D. 2000. Redundant Governance Structures: An

Analysis of Structural and Relational Embeddedness in the Steel and

Semiconductor Industries. Strategic Management Journal, 21: 369-386.

Royer, I., & Zarlowski, P. 1999. Échantillon(s) In R.-A. Thiétart (Ed.), Méthodes de

recherche en management. Paris: Dunod.

Scott, J. 1991. Social Network Analysis: A Handbook. Newbury Park CA: Sage.

SIPRI. 2004. Facts on International Relations and Security Trends database, Vol. 2004.

Stockholm: Stockholm International Peace Research Institute.

Sobrero, M., & Roberts, E. B. 2002. Strategic management of supplier–manufacturer

relations in new product development. Research Policy, 31: 159-182.

Tether, B. S. 2002. Who Cooperates for Innovation and Why: An Empirical Analysis.

Research Policy, 31(6): 947-967.

Thorelli, H. B. 1986. Networks: Between Markets and Hierarchies. Strategic

Management Journal, 7: 37-51.

Treddenick, J. M. 1998. Distributing the Defence Budget: Choosing Between Capital and

Manpower. In D. Bland (Ed.), Issues in Defence Management: 57-83. Kingston

ON: Queen's University.

Uzzi, B. 1996. The Sources and Consequences of Embeddedness for the Economic

Performance of Organizations: The Network Effect. American Sociological

Review, 61(4): 674-698.

Uzzi, B., & Lancaster, R. 2003. Relational Embeddedness and Learning: The Case of

Bank Loan Managers and Their Clients. Management Science, 49(4): 383–399.

Vanhaverbeke, W., & Noorderhaven, N. G. 2001. Competition between Alliance Blocks:

The Case of the RISC Microprocessor Technology. Organization Studies, 22(1):

1-30.


26

Véry, P., & Arrègle, J.-L. 1996. Combinaison de ressources et avantages concurrentiels.

In A. Noël, C. Koenig, & G. Koenig (Eds.), Perspectives en management

stratégique, Vol. Tome IV : 1995/96: 225-246. Paris: Economica.

Walker, G., Kogut, B., & Shan, W. 1997. Social Capital, Structural Holes and the

Formation of an Industry Network. Organisation Science, 8(2): 109-125.

Wasserman, S., & Faust, K. 1999. Social Network Analysis: Methods and Applications.

Cambridge MA: Cambridge University Press.

Wernerfelt, B. 1984. A Resource-Based View of the Firm. Strategic Management

Journal, 5(2): 171-180.

Yin, R. K. 1994. Case Study Research. Thousand Oaks CA: Sage.


27

ANNEX – TABLES

Table 1: Descriptive statistics and Correlations

Mean S.D. 1 2 3 4 5 6 7 8 9 10

1 Collaboration 0.79 1.27

2 Technical

Intensity 2.94 E-02 1.82 E-02 -0.11*

3 Military

Spending 2.52 1.65 0.01 -0.08

4 Experience 4.00 6.50 0.54** -0.11* -0.02

5 Clique 0.32 0.37 0.73** -0.17* -0.07 0.64**

6 Centrality 0.28 0.28 0.44** -0.15** -0.07 0.59** 0.67**

7 Constraint 0.18 0.17 0.44** -0.07 -0.03 0.34** 0.65** 0.65**

8 Hierarchy 7.54E-02 0.12 0.46** -0.09 -0.02 0.58** 0.58** 0.57** 0.69**

9 Event 0.43 0.50 0.23** -0.01 0.03 0.15** 0.12* 0.05 0.02 0.15

10 Alliance 4595740 10304822 0,58** -0,19** -0,08 0,59** 0,82** 0,57** 0,68** 0,54 0,04

11 IT 1,275 0,43 -0,18** 0,23** -0,02 -0,20** -0,08 -0,06 0,03 0,04 -0,05 -0,04

N = 407 ** Significant at 0.01 (2-tailed) * Significant at 0.05 (2-tailed)


28

Table 2: Two-way fixed effects panel data analysis

Dependent Variable = Collaboration (Standard error in parentheses)

Variables Model 1 Model 2 Model 3 Model 4 Constant

0.94 (0.53)

0.99 (0.56)

0.80 (0.55)

0.88 (0.77)

Technical Intensity

1.89 (4.77)

Military Spending

-0.01 (0.07)

Experience

-0.004 (0.02)

Cliques

1.72*** (0.26)

1.48*** (0.34)

1.47*** (0.36)

Centrality

1.70*** (0.40)

0.33 (0.50)

0.39 (0.54)

Constraint

0.24 (0.40)

0.30 (0.40)

0.160 (0.57)

Hierarchy

0.65 (0.52)

0.50 (0.51)

0.60 (0.60)

Alliance

0.22 (0.30)

0.18 (0.31)

0.20 (0.30)

0.19 (0.31)

Event

2.04E-8** (7.08E-9)

1.93E-8** (7.27E-9)

2.02E-8** (7.10E-9)

1.99E-8** (7.26E-9)

IT

-0.66* (0.33)

-0.71* (0.34)

-0.79* (0.34)

-0.78** (0.34)

F-test for no fixed effects

F(46. 356) = 4.50*** F(46. 354) = 7.54*** F(46. 353) = 4.38*** F(46. 350) = 4.09***

Hausman Test: value for m

11.25* 48.21*** 15.43* 30.62***

Country-year effect

Yes Yes Yes Yes

R²

0.72 0.71 0.73 0.73

Note: 37 cross-sections for a time series length of 11. *** p < 0.001 ** p < 0.01 * p < 0.05


29

Table 3: HLM Analysis

Dependent Variable = Collaboration

Variable Model 5 Model 6 Model 7 Model 8 Model 9

Fixed Effects

Coeff.

(S.E.)

T-ratio Coeff.

(S.E.)

T-ratio Coeff.

(S.E.)

T-ratio Coeff.

(S.E.)

T-ratio Coeff.

(S.E.)

T-ratio

Constant

0,79***

(0,15)

5,11 58,81**

(18,02)

3,26 105,39**

(33,19)

3,18 76,23**

(26, 81)

2,84 80,24*

(29,04)

2,76

Alliance

-0,08

(0,15)

-0,52 1,15***

(0,23)

5,07 -0,13

(0,17)

-0,75 -0,37

(0,20)

-1,87

Year

-0,03**

(0,01)

-3,23 -0,05**

(0,02)

-3,17 -0,04**

(0,01)

-2,82 -0,04**

(0,01)

-2,75

IT

-0,97

(0,54)

-1,79 -0,79*

(0,59)

-1,38 -1,06

(0,55)

-1,94 -1,02*

(0,51)

-2,01

Technical Intensity

3,29

(2,67)

1,23

Military Spending

0,05

(0,0260)

1,73

Experience

0,03*

(0,01)

2,36

Cliques

2,59***

(0,22)

11,67 2,56***

(0,24)

10,76 2,63***

(0,22)

12,07

Centrality

1,22***

(0,22)

5,64 0,00

(0,23)

0,02 -0,30

(0,28)

1,09

Constraint

-0,51

(0,33)

-1,57 -0,41

(0,38)

-1,07 0,21

(0,56)

0,36

Hiérarchy

1,16**

(0,39)

2,58 1,23***

(0,32)

3,86 0,63

(0,48)

1,31

Random Effects

S.D.

(V.C.)

Khi² S.D.

(V.C.)

Khi² S.D.

(V.C.)

Khi² S.D.

(V.C.)

Khi² S.D.

(V.C.)

Khi²

Constant

0,91***

(0,83)

445,77 0,21**

(0,05)

59,69 0,42***

(0,17)

119, 75 0,18*

(0,03)

52,23 0,04

(0,01)

31,86

Level 1

0,90

(0,80)

0,89

(0,69)

0,88

(0,78)

0,83

(0,69)

0,84

(0,71)

Model Fit Deviance Param. Deviance Param. Deviance Param.

Deviance Param. Deviance Param.

1160,16 2 1034,01 2 1104,93 2 1026,82 2 1028,24 2

ICC 0,51 0,06 0,17 0,05 0,00

Note: 37 countries (level2 units) and 407 year-country (level 1 units). The “Event” variable was withdrawn as it yields no results due

to the impossibility of inverting a matrix to that effect. All models presented here are fixed-effects with robust standard errors, which

imply a low sensitivity to specification errors.

S.E. = Standard Error

S.D. = Standard Deviation

V.C. = Variance Component

Param. = Parameters

ICC = Intraclass Correlation Coefficient

*** p < 0,001 ; ** p < 0,01 ; * p < 0,05


30

Table 4: Profile of interviewees

Subject no. Military

Experience

(years)

Nature of

International

Experience

Rank Service Country

CA001 16 Exercises and

operations

Lieutenant colonel Army Canada

CA002 25 Exercises and

operations

Colonel Army Canada

CA003 25 Exercices and

operations

Colonel Army Canada

US001 34 Exercises,

operations,

liaison

Colonel (Ret’d) serving in a

Brigadier General capacity

Air Force USA

Source: Interviews by author.

Note : (Ret’d) = retired.

Table 5: Hypotheses Results

Hypothesis Panel Data Analysis Hierarchical Linear Modeling

H1: Cliques and

Collaboration

Supported

p < 0,001

Supported

p < 0,001

H2: Centrality and

Collaboration

Partially Supported

p < 0,001

Partially Supported

p < 0,001

H3: Constraint and

Collaboration

Not Supported

p > 0,05

Not Supported

p > 0,05

H3: Hierarchy and

Collaboration

Not Supported

p > 0,05

Partially Supported

p < 0,01


31

APPENDIX A

Table A1: The 37 armed forces in our sample

Entity Affiliation

Algeria

Germany

Austria

Belgium

Bulgaria

Canada

Cyprus

Danemark

Spain

Estonia

USA

Finland

France

Greece

Hungary

Italy

Jordan

Latvia

Lithuania

Luxemburg

Macedonia (FYROM)

Malta

Morocco

Norway

Palestine

Netherlands

Poland

Portugal

Czech Republic

Romania

Other

NATO

PfP

NATO

PfP

NATO

Other

NATO

NATO

PfP

NATO

PfP

NATO

NATO

NATO

NATO

Other

PfP

PfP

NATO

PfP

Other

Other

NATO

Other

NATO

NATO

NATO

NATO

PfP


32

UK

Russia

Serbia and Montenegro

Slovakia

Slovenia

Sweden

Turkey

NATO

PfP

Other

PfP

PfP

PfP

NATO

Source : Compiled from SIPRI data.

Legend : NATO is the North Atlantic Treaty Organization. PfP is the Partnership for Peace.

NOTE : Russia and the former USSR are under Russia. Germany takes into account the former

GDR and GFR. Macedonia is not recognized by Turkey which treats it as a Former Yugoslav

Republic.

Documents

DOCUMENT DE TRAVAIL 2009-016 - FSA ULaval · point to the importance of cliques and structural holes for the explanation of interorganizational collaboration in a network setting