Advancing Knowledge-Intensive Entrepreneurship and ...

Advancing Knowledge-Intensive Entrepreneurship and Innovation for Economic Growth and Social Well-being in Europe

Deliverable Title D1.7.5. “Networks and knowledge-intensive entrepreneurship in practice II: Technology-based strategic alliances in industry”

Deliverable Lead: LIEE-NTUA

Related Work package: WP1.7 “The organization of knowledge-intensive entrepreneurship: Networks”

Author(s): Nicholas S. Vonortas

Dissemination level: Public

Due submission date: 30/09/2009

Actual submission:

Version Final

Project Number 225134

Instrument: Collaborative Project (Large-scale integrating project)

Start date of Project: 01/01/2009

Duration: 36 months

Abstract This paper investigates the involvement of newly established knowledge-intensive entrepreneurial firms in strategic alliance networks. We construct a network of publicly announced alliances of an EAGIS group of firms. We find a dispersed network with many components, few hubs, and the vast majority of participants characterized as ultra-peripheral. This characterization particularly applies to our target group of companies. Given that at the time they were quite young and inexperienced, one would hardly expect anything else.

Project co-funded by the European Commission under Theme 8 “Socio-Economic Sciences and Humanities” of the 7th Framework Programme for Research and Technological Development.

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D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” of 26

Table of contents 1. INTRODUCTION.................................................................................................3 2. THEORETICAL BACKGROUND ...................ERROR! BOOKMARK NOT DEFINED. 3. METHODOLOGY........................................ERROR! BOOKMARK NOT DEFINED.

3.1. DATASET PREPARATION........................... ERROR! BOOKMARK NOT DEFINED. 3.2. IDENTIFICATION OF NETWORK HUB ORGANIZATIONS.................................. 11

4. EMPIRICAL RESULTS.......................................................................................15 4.1. DESCRIPTIVE STATISTICS RESULTS ................................................................ 15 4.2. SOCIAL NETWORK ANALYSIS RESULTS............................................................ 18

FIX TABLE OF CONTENTS

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1. Introduction

The confluence of important developments in the international economic environment during

the past three-four decades has turned inter-firm cooperation into an important mechanism of

business interaction and market and technology access. Particularly in high- and medium-tech

industries, the private sector has increasingly used various kinds of cooperative agreements

such as joint ventures, joint R&D, technology exchange agreements, co-production, direct

minority investments, and sourcing relationships to advance core strategic objectives. Called

alliances in this paper, such agreements imply deeper and steadier relationships than arm’s-

length market exchanges but fall short of complete mergers. They involve mutual dependence

and shared decision-making between two or more independent parties. When research and

development is a focus of the partnership, universities and other research institutes may also

participate.

This paper investigates the involvement of newly established knowledge-intensive

entrepreneurial firms in strategic alliance networks as a way to access and exchange

knowledge and share resources. We use a sample of company members of the AEGIS survey

population sample comprised by firms established during 2001-2007 in the eighteen

predetermined sectors and ten European countries. This is a sister deliverable to deliverable

1.7.4 which has looked at collaborative R&D funded by EU Framework Programmes in RTD.

Our alliance sample here involves agreements that the companies created outside public

programmes.

As documented in Vonortas and Zirulia (2010) (deliverable 1.7.1), during the past

couple of decades a very extensive literature on networks has emerged in economics,

management and organization theory. An important part of this research has focused on

business networks arising from technological agreements among various organizations

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(especially companies) especially in the most dynamic and knowledge-intensive (Malerba and

Vonortas, 2009). This literature comes to complement an even longer one on strategic

alliances which has proliferated since alliances started becoming widespread in the early

1980s (Hagedoorn et al, 2000).

This paper focuses on business networks and entrepreneurial knowledge-intensive

enterprises (KIEs) defined to be no more than eight years old when the data was collected. As

is well known, network analysis critically depends on the delineation of the network. In this

case the network is defined on the basis of the sample population of the AEGIS survey: we

matched company names with the Thompson SDC database to obtain the set of publicly

reported alliances which included one or more companies from this sample population.

The findings confirm expectations. Following the prior literature, we find a network

which is quite dispersed, with many components, few hubs (even fewer when they core group

of firms are considered), no hubs that strongly connect across individual modules, and the

vast majority of participants characterized as ultra peripheral. This characterization

particularly applies to our target group of companies. Given that at the time they were quite

young and inexperienced, one would hardly expect anything else.

These results underline the arguments that young entrepreneurial companies need

more established players to enter networks in order to enjoy the benefits of trust formation,

information acquisition, market access, and business opportunity identification.

The paper proceeds as follows. Section 2 defines the context of alliances. It also

summarizes some basic theoretical arguments and findings in the network literature. Section

3 explains the data used here to create the network and the methodology to analyze it. Section

4 illustrates our results. Finally, Section 5 concludes.

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2. Context

2.1. Definitions Alliances refer to agreements whereby two or more partners share the commitment to reach a

common goal by pooling their resources together and by coordinating their activities.

Partnerships denote some degree of strategic and operational coordination and may involve

equity investment. They can occur vertically across the value chain, from the provision of raw

materials and other factors of production, through research, design, production and assembly

of parts, components and systems, to product/service distribution and servicing. Or, they can

occur horizontally, involving competitors at the same level of the value chain. Partners may

be based in one or more countries.

A narrower set of partnerships can be characterised as innovation-based, focusing

primarily on the generation, exchange, adaptation and exploitation of technical advances.

These arrangements are of primary concern to all countries as a result of expected direct

contribution to national capacity building.

2.2. International Context

Since the early 1980s, when the first data were put together to map a sudden burst of inter-

firm cooperation, it has been established beyond doubt that partnerships have become a very

important mechanism of business interaction and market and technology access around the

world. A proliferating literature in economics, business and policy has tried to identify and

interpret the important features of cooperation among firms, universities, and other public and

private organizations.

A set of developments in the international economic environment has underlined the

explosion of business partnerships since the late 1970s. Four changes, in particular, seem to

be key: 1. Globalization. Transnational companies have pushed into new product and geographical

markets relentlessly.

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2. Technological change. The pace of technological advance has accelerated significantly,

partly as a result of increasing competition through globalization. In addition to being an

outcome of competitive pressures, however, technology is an enabler of globalization.

Technological capabilities have diffused around the world more widely than ever before.

3. Notion of “core competency”. Increasing international competition and faster pace of

technological advance have robbed firms of their ability to be self-sufficient in everything

they want to do. The current management mantra is to do internally what a company does

best and outsource the rest through partnerships.

4. Economic liberalization and privatization. This process has led to unprecedented

international flows of capital in the form of both foreign direct investment and portfolio

investment. Developing countries have managed to increase their share of the intake (but

the distribution among them remains highly skewed).

Such developments have changed the nature of international business interactions that

has supported the development of a score of developing countries since the mid-twentieth

century. Traditional mechanisms of technology transfer including licensing, the acquisition of

capital goods, and the transfer of complete technology packages through foreign investment

are being supplemented by many semi-formal and formal new mechanisms for gaining access

to technologies and markets. These new mechanisms entail the formation of dense webs of

inter-organizational networks that provide the private sector with the necessary flexibility to

achieve multiple objectives in the face of intense international competition. The result has

been an increasing interdependence on a global scale that few firms interested in long-term

survival and growth can escape.

Knowledge-intensive activities and high-technology sectors have been at the centre of

alliance formation worldwide.

2.3. Literature Review

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Vonortas and Zirulia (2010) divide the study of networks and knowledge-intensive enterprises

(KIEs) into two categories:

i) Effects of networks on KIE participants. Antecedents and consequences of networking

behaviour on a subset of nodes (KIEs). What are the main motives for KIEs to enter

collaborative agreements? What KIE attributes affect the number and type of relations

in which KIEs are involved? What KIE attributes are affected by the relations?

ii) Effects of KIE participation on the network. Role of KIE nodes in network structure,

evolution, and performance. What is the role of KIE nodes in the network? How do

they affect network performance such as the rate of technological progress, the

division of innovative labour and the direction of technical change?

Starting with the first category, extensive analytical work in the first category has shown that

the major expected benefits to firms participating in cooperative R&D agreements include:

• R&D cost sharing

• Risk sharing and uncertainty reduction

• Access to complementary resources and skills of partners, including technological

knowledge and human capital

• Continuity of R&D effort and access to finance

• Exploitation of research synergies

• Strategic flexibility, market access and the creation of investment ‘options’

• Promotion of technical standards

• Keeping up with major technological developments

• Effective deployment of extant resources and further development of the firm’s

resource base

• Co-opting competition and gain market power

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Many of these would be expected to be of particular relevance to KIEs, including cost

sharing, access to partner resources and skills, R&D effort continuity, access to finance, and

research synergies.

The present paper falls primarily in the 2nd category above.1

The network literature has stressed the importance of social capital. Social capital

refers to opportunity: individuals with more social capital get higher returns to their human

capital because they are positioned to identify and develop more rewarding opportunities. The

social capital of individuals is akin to the network resources of firms (Walker et al., 1997).

Entrepreneurs will be keenly interested in the earlier phases of their companies’ development

to expand both their own social capital as well as the network resources of their firms.

The literature has recognized two broad channels of network influence on members

(Gulati, 1998). The first relates to informational benefits obtained through network ties and

positioning. The second relates to control benefits (governance) that are generated by being

more advantageously positioned in the network or by being part of a tightly knit network.

Although analytically different, these two benefits also overlap significantly since the control

benefits largely emanate from the possession and manipulation of information.

An intense debate has run on what authors recognize as a fundamental trade-off

between organizational stability and variety in network structure. The accumulation of social

capital is dependent upon the maintenance and strengthening of the prevailing relationships;

hence a tendency to freeze the structure of interactions into stable patterns. The more stable

the patterns of interaction become, however, the more the characteristics of firm organization

the network acquires, i.e., the more it strives for specialization and the less capable it grows in

achieving its fundamental objective of variety. Increasing coordination deprives individual

partners of the ability to pursue potential avenues of exploration.

1 Similarly with the sister deliverable 1.7.4. In contrast, deliverable 1.7.6 is better classified in the first category.

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On the basis of the different information requirements between exploitation and

exploration, Rowley et al. (2000) have argued for high-density and strong ties for exploitation

and for low-density and weak ties for exploration. Strong ties are said to facilitate rich

exchanges of fine-grained information to assist firms in obtaining a deep understanding of a

specific innovation in order to refine and improve it. Weak ties are said to be especially

important for flexibility and low-density network structures preferable for broad searches in

uncertain environments requiring relatively high investments in exploration. But, not

everyone agrees (Hagedoorn and Duysters, 2002). Nooteboom and Gilsing (2004) argue

somewhere in between. They expect dense networks and redundant ties in the case of

exploration. Less dense, more stable network structures and non-redundant ties are anticipated

for exploitation.

The discussion on network structure above is important for young, knowledge-

intensive entrepreneurial companies. To the extent that they lean toward knowledge

exploration, rather than knowledge exploitation, less dense networks and weak or redundant

ties may make a more appropriate network structure.

3. Data an Hub Methodology

3.1. Data

The dataset we are employing in this paper is directly related to the deliverables 7.4.3 and

7.4.4. The alliance database developed in D7.4.3 and the alliance network constructed in

D7.4.4 are used as an input for the implementation of the current empirical analysis.

Our strategy was to track down newly established European firms that are

participating in strategic alliances and are also members of the AEGIS survey population

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sample. Primary data sources were a) Thomson SDC Strategic Alliance Database on strategic

alliances across the world within the last decade (2000-2009) and b) the AEGIS survey

population comprised by firms established between 2001 and 2007 in specific sectors2 and

countries3.

From the comparison of the two datasets a final number of 404 firms appearing in

both of them were extracted. An alliance network was then constructed on the basis of the

identified strategic alliances with at least one member organization corresponding to these

newly-established European firms. Table 1 presents a number of indicators representing the

main topological features of the alliance network.

Table 1. Structural features of the KIE alliance network

Nodes (actors) 1071

Edges (links) 2010

No of Components 316

Size of Giant Component 164

% of GC 15%

Density 0.0018

Clustering coefficient 0.655

Characteristic path length 3.828

2 Both manufacturing sectors (14) and service sectors (4) were covered. Manufacturing sectors included high tech (5), medium to high tech (3), medium to low tech (2), and low tech sectors (4). High technology manufacturing consisted of: aerospace, computers and office machinery, radio television and communication equipment, scientific instruments, and pharmaceuticals. Medium to high technology manufacturing consisted of electrical machinery and apparatus, machinery and equipment, and chemicals (except pharma). Medium to low technology manufacturing consisted of basic metals and fabricated metal products. Low technology manufacturing consisted of paper and printing, textile and clothing, food, beverage and tobacco, and wood and furniture. Knowledge intensive business services consisted of telecommunications, computer and related activities, research and experimental development, and selected business services activities. 3 Ten European countries were covered: Croatia, Czech Republic, Denmark, France, Germany, Greece, Italy, Portugal, Sweden, and the United Kingdom.

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Diameter 9

Mean degree 1.877

The networks formed by the organizations participating in the strategic partnerships

we are studying can be characterized as affiliation networks. An affiliation network consists

of information about subsets of actors who participate in the same social activity or event

(group, club, etc.). In the case of the present networks participating organizations are joined

together by their membership in the same strategic alliance. They are, basically, bipartite

structures: the information they contain is most completely represented as a graph consisting

of two kinds of vertices, one representing the actors and the other representing the groups

(alliances). Edges (links) then run only between vertices of unlike kinds, connecting actors to

the groups to which they belong. Affiliation networks are often represented simply as

unipartite graphs of actors joined by undirected edges – twp organizations participating in the

same alliance joined by an edge.

3.2. Identifying Network HUB Organizations

A network hub is an organization that plays a central role in the network. Consequently, these

are organizations more important than others for the structure, evolution, and functioning of

the network. The most obvious approach to identify network hubs is to rank organisations in

terms of number of partners. Network hubs may then be defined simply as those organisations

with a significantly higher number of connections than the average node in the network (high

degree centrality). Another approach is to rank organizations in terms of their particular

location in the network that facilitates connections between otherwise disconnected parts of

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the network (high betweenness centrality). Or one could merge degree and betweenness

centrality values and identify the organizations with the highest combined values.

While these definitions are reasonable, they are not fine-grained enough to represent

the great variety or roles of network participants. In this analysis we follow another

methodology proposed by Roger Guimera and Luis Amaral (2005) which allows to classify

network nodes in a number of ‘system independent’ universal roles based on their

connectivity. While more complicated because it depends on combinations of two

connectivity dimensions, this methodology still yields a simple and intuitive cartographic

representation of complex networks.

We start by identifying network modules. A module can be defined as a community

(a subset of nodes) of highly interconnected organisations that are less connected to

organisations in other communities. Guimera and Amaral (2005) proposed an algorithm based

on the maximisation of a quantity called modularity, which is computationally feasible also

for large complex networks and performs as accurately as the Newman-Girvan algorithm in

allocating nodes to modules. Once network modules (i.e. communities) have been identified,

network nodes can be classified according to the role they play within and between modules.

In particular, Guimera and Amaral proposed a classification of nodes based on two measures:

within-module degree and participation coefficient. Within-module degree measures how well

connected a node is to other nodes within its module. In formal terms, within-module degree

is defined by:

where is the number of links of node i to other nodes in its module , is the average of

over all the nodes in , and is the standard deviation of in . On the basis of this

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measure, nodes with can be classified as module hubs and nodes with as

non-hubs. Informally, a node with a significantly larger than average number of links to other

nodes in its module is defined as a module hub, whereas a node with an average (or lower

than average) number of links is defined as a non-hub.

The participation coefficient captures instead the extent to which a node is connected to

several nodes in other modules. Formally, it is defined by:

where is the number of links of node i to nodes in module s, and is the total number of

links of node i. The participation coefficient of a node is therefore close to one if its links are

uniformly distributed among all the modules and close to zero if all its links are within its

own module.

The combination of these two measures yields a partition of nodes into seven categories (or

roles), four related to non-hub nodes and three to hub nodes:

• Non-hub nodes

o Ultra-peripheral nodes (Role 1).

Node has all its links within its module

o Peripheral nodes (Role 2).

Node has a small positive participation coefficient , i.e. it has a large

fraction of all its links within its module

o Non-hub connectors (Role 3)

Node has a fairly large participation coefficient , i.e. it has

large fraction of all its links to nodes in other modules

o Non-hub kinless nodes (Role 4)

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Node has a large participation coefficient , i.e. it has very few links to

nodes in its own module, it cannot be clearly assigned to any single module.

• Hub nodes

o Provincial hubs: (Role 5)

Node with a large degree has at least 5/6 of its links within its module,

o Connector hubs (Role 6)

Node with a large degree has at least half of its links within its module,

o Kinless hubs (Role 7)

Node with a large degree has fewer than half of its links to nodes within its

module ( ), so that it may not be clearly associated to a single module.

Figure below provides a visual illustration of the regions in which a complex network may be

partitioned according to the roles played by different nodes.

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Figure 1: Partition of Nodes (Network Participants)

Source: Adapted from Guimera and Amaral (2005)

4. Empirical results

4.1. Descriptive analysis

Table 2 shows the distribution of newly-established firms per country participating in

strategic alliances. The countries represented in the Table are those included in the AEGIS

survey. Bigger countries exhibit larger share of firms. More than half of participating firms

are located in the United Kingdom and another 40% of them comes from France, Germany

and Italy.4

4 This may indicate the biases of the SDC Thompson database. This database is built on the basis of publicly announced events (alliances in this case). The covered literature sources (newspapers, magazines, trade publications, etc) are biased towards the English language.

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Table 2: Distribution of AEGIS firms by country

AEGIS firms No of Firms %

Croatia 2 0.5%

Czech Republic 8 2.0%

Denmark 16 4.0%

France 86 21.3%

Germany 50 12.4%

Greece 7 1.7%

Italy 36 8.9%

Portugal 4 1.0%

Sweden 23 5.7%

United Kingdom 172 42.6%

Total 404 100%

Τhe average number of alliances per participant is slightly above 1.5. However, not all

actors are equally active: as Figure 2 illustrates, the large majority of organizations participate

in just one strategic alliance. A very small share of organizations, mainly global firms, that

exhibits a large number of participations: a mere 4.2% of the actors have more than three

alliances.

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Figure 2: Participation intensity

Figure 3 shows the distribution of alliances according to their size, i.e. the number of

participating organizations. Nine out of ten alliances consist of two organizations. Only 3% of

alliances exhibit more than 3 participants. A first observation follows: The low intensity of

participation coupled with the small alliance size make up for a sparse network and minimize

the indirect benefits of participating to such networks.

Figure 3: Alliance size

89.9%

6.7%

2.5%

1.0%

87.7%

6.8%

1.4%

4.2%

0.0% 20.0% 40.0% 60.0% 80.0% 100.0%

1

2

3

>3

No

of a

llian

ces

AEGIS firms Other

90%

7%

2% 1%

2 entities

3 entities

4 entities

> 4 entities

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4.2 Social Network Analysis

The structural characteristics of the network under consideration are summarized in Table 3.

Table 3: Topological Properties of the Analyzed Networks5

Analyzed period 2000-2009

Number of alliances 664

Number of participants 1438

Average no of participants per alliance 2,17

Number of unique organizations 1070

Number of nodes 1070

Number of edges (links) 2010

Average degree 1,88

Network density 0,0018

Number of components 316

Size of largest component 164

% of largest component 15,3

Network diameter* 9

Average path length* 3,83

Transitivity* 0,16

Average clustering coefficient* 0,53 *Computed on the largest component

The alliance network appears quite fragmented. It is rather small in terms of both

participants (nodes) and connections (edges), comprises of alliances of small average size,

5 Number of nodes: Number of organizations in network Number of edges: Number of connections between these organizations Average degree: Number of other organizations which an organization is directly connected to Network density: Share of all theoretically possible connections that have materialized (ratio of number of actual connections over the maximum number of possible connections) Number of components: Number of directly or indirectly connected subgraphs (groups) in the network Size of largest component: Number of organizations in the largest component Network diameter: Largest number of connections separating two organizations (largest component) Average path length: Average number of connections separating two organizations (largest component) Transitivity: Ratio of triangles to triplets in the network (largest component) Average clustering coefficient: Index indicating the extent to which the organizations connected to a given organization tend to also be connected to each other (largest component)

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and has a large number of components. The largest of these components consists of just 164

nodes which account for 15.3% of the total number of nodes in the network. Despite the

relatively low overall network connectivity, those organizations connected in the largest

component are at an average 3.8 steps away, which practically means that they form a core of

reasonably well-connected organizations. Moreover, these organizations are, directly or

indirectly, interconnected via collaboration while the longest path among them is six steps.

This means that there is a proper environment in which knowledge exchange and share of

resources can be undertaken.

Table 4 shows the participant distribution according their role as hubs or peripherals in

the examined network following the taxonomy presented in Figure 1.

Table 4: Participating Organizations (Nodes) Distribution by Role in the Network

Roles Total AEGIS firms

Role 1 Ultraperipheral 998 (93%) 397 (98%)

Role 2 Peripheral 0 0

Role 3 Non-hub Connectors 0 0 Non-hub nodes

Role 4 Kinless Non-hubs 0 0

Role 5 Provincial Hubs 72 (7%) 7 (2%)

Role 6 Connector Hubs 0 0 Hub nodes

Role 7 Kinless Hubs 0 0

Total 1070 404

The vast majority of organizations (93%) are ultra-peripheral non-hub nodes. Almost

all (98%) of the newly-established KIEs belong in this category. In practical terms, these

companies have the most marginal effect on the network, its characteristics and evolution.

Only a small share of organizations (7%) can be characterized as hubs in their own respective

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modules. Even so, as provincial hubs their connectivity across modules is rather weak. Only

seven firms from the AEGIS population are included in this category. The general picture

here is one of sparse connectivity with only few organizations being hubs in their modules but

not being able to strongly interconnect modules. Very few newly established firms play such

role.

Table 5: Network Participant (Node) Distribution by Organizational Type

Type Organizations

(%) Hubs (%)

Industry 1057 (98.8%) 69 (95.8%)

Other 3 (0.3%) 0 (0%)

Research 3 (0.3%) 1 (1.4%)

University 7 (0.7%) 2 (2.8%)

Total 1070 (100%) 72 (100%)

Table 5 gives details on the distribution of nodes by organizational type. Not surprisingly,

industry fully dominates. Table 6 distributes network participants by country/region. At first

glance, European and North American firms seem to lose strength as hubs relative to their

participation, whereas firms for Asia and Oceania gain. For Europe at least this must be

partly attributed to the fact that these are newly established companies (by definition).

Table 6: Network Participant (Node) Distribution by Country/Region

Region Organizations

(%)

Hubs

(%)

Europe 493 (52%) 8 (44%)

North America 325 (34%) 2 (11%)

Japan 29 (3%) 1 (6%)

Oceania 37 (4%) 1 (6%)

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Other Asian countries 57 (6%) 4 (22%)

Other countries 11 (1%) 2 (11%)

Total 952 (100%) 18 (100%)

Figure 4: Degree distribution of network

A network property that has attracted attention in a wide range of different networks is

the degree distribution, P(k), which estimates the probability that a randomly selected node

has k links (Barábasi et al. 2002). The degree distribution for the giant component of our

strategic alliance network is depicted in Figure 4. The histogram indicates that the distribution

is highly skewed, with the majority of organizations having a small number of direct links,

whereas only a small proportion of actors demonstrate a large number of connections. Such

degree distributions follow a power-law P(k) ~ k-γ with scaling exponent γ taking a value

between 2.1 and 4 (Barábasi and Albert 1999). This finding suggests the disproportionate

effect of a few organizations on the alliance network’s connectivity.

y = 0.406x-2.061 R² = 0.7685

0.001

0.01

0.1

1

1 10 100

P(k)

k

TOTAL Power law

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Figure 5: Visualization of the Alliance Network6 (White = industry, light grey = university, black = public research institute, dark grey = other types of institutions) (Circle = EU27 countries, Square = North America, Rounded square = Oceania, Triangle Up = Japan, Triangle Down = other Asian countries, box = other regions, diamond = unspecified) (Bigger nodes = hubs, smaller nodes = non hubs)

Figure 5 illustrates the interconnection of organizations in the strategic alliances

network. Each node in the graph is given specific attributes in relation to its region of origin,

its organizational type and its role within the network. In particular, the color of the node

indicates the type of organization, for example white nodes are firms, while the shape of the

nodes associated with the region of origin, for example circle nodes referred to Europe. The

size of each node is directly related to its role (which is based on the aforementioned hub

methodology), i.e. hub organizations are represented with larger nodes. The location of each

organization in the sub-network’s visualization is generally related to its distance from each

6 Networks are visualized with Netdraw (Borgatti, 2002)

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other node. Therefore, organizations in the periphery of network are those exhibiting larger

paths (characteristic path lengths) in their connections, while nodes that are located close to

each other may also belong to the same community.

This visual representation has revealed that there are many distinct groups of

organizations in which nodes are collaborating inside each group but they are sparsely linked

to other groups. Figure 5 also indicates that network hubs, even though only “provincial

hubs”,7 appear to play an important role in the alliance network as they act as bridges between

groups (modules) making possible the indirect connection of a large share of organizations.

Figure 6: Visualization of network (only AEGIS firms)

7 Provincial hubs have low inter-module connectivity.

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The network’s visualization changes considerably when we include only the selected

young firms from the AEGIS population (Figure 6). The most obvious observation would be

that these firms are sparsely interconnected, thus they are highly depended on their global

partners, mainly network hubs, the presence of which is enhancing the overall network’s

connectivity.

5. Conclusion

This paper has presented an exercise where a sample population of newly established

companies is matched to a population of publicly announced strategic alliances in order to

draw a network of relationships of the identified (404) companies. These companies were

recently established across ten country members of the European Community – but with the

large majority residing in the big four – and operated in eighteen manufacturing and service

sectors. In other words, the core population here is rather dispersed.

The findings confirm expectations. Following the prior literature, we find a network

which is quite dispersed, with many components, few hubs (even fewer when they core group

of firms are considered), no hubs that strongly connect across individual modules, and the

vast majority of participants characterized as ultra peripheral. This characterization

particularly applies to our target group of companies. Given that at the time they were quite

young and inexperienced, one would hardly expect anything else.

Going back to the network literature, these results underline the arguments that young

entrepreneurial companies need more established players to enter networks in order to enjoy

the benefits of trust formation, information acquisition, market access, and business

opportunity identification. To the extent that our target population of companies are in the

knowledge exploration phase, the results also agree with the hypothesis in the literature

regarding optimal network structure: less dense, looser network and weaker tie formation.

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The limitations of this study include the geographical and sectoral dispersion of the

core company population which allow for significant variations in behaviour. Nevertheless, it

is our expectation that the basic findings and messages of this paper would not change

dramatically with a more geographically and sectorally coherent population. This, of course,

is subject to further investigation.

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Bibliography Barábasi, A.L., and R. Albert. 1999. Emergence of scaling in random networks. Science, 286,

509–12.

Barábasi, A.L., H. Jeong, Z. Neda, E. Ravasz, A. Schubert, and T. Vicsek. 2002. Evolution of

the social network of scientific collaborations. Physica A 311, no. 3–4, 590–614.

Guimera, R, Amaral, LAN, 2005. Functional cartography of complex metabolic networks,”

Nature 433,. 895–900.

Gulati, R. (1998) “Alliances and networks”, Strategic Management Journal, 19: 293-317.

Hagedoorn, J, and G. Duysters (2002) “Learning in dynamic inter-firm networks: The

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

Hagedoorn, J., A. N. Link, and N. S. Vonortas (2000) Research partnerships. Research Policy,

29(4-5), 567-586.

Malerba, F. and N. S. Vonortas (2009) Innovation Networks in Industries, Edward Elgar.

Nooteboom, B. and V. A. Gilsing (2004) “Density and strength of ties in innovation

networks: A competence and governance view”, Working Paper, Rotterdam School of

Management.

Rowley, T., D. Behrens and D. Krackhardt (2000) “Redundant governance structures: An

analysis of structural and relational embeddedness in the steel and semiconductor

industries”, Strategic Management Journal, 21: 369-386.

Vonortas N.S. and L. Zirulia. 2010. Business network literature review and building of

conceptual models of networks and KIE. Deliverable 1.7.1 in Workpackage 1.7 of the

AEGIS Project.

Walker, G., B. Kogut, and W. Shan (1997) “Social capital, structural holes and the formation

of an industry network”, Organization Science, 8: 109-125.

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