Upload
others
View
9
Download
0
Embed Size (px)
Citation preview
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.
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 2 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
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 3 of 26
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
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 4 of 26
(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.
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 5 of 26
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.
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 6 of 26
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
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 7 of 26
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
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 8 of 26
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.
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 9 of 26
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
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 10 of 26
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.
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 11 of 26
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
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 12 of 26
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
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 13 of 26
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)
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 14 of 26
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.
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 15 of 26
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.
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 16 of 26
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.
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 17 of 26
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
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 18 of 26
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)
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 19 of 26
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
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 20 of 26
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%)
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 21 of 26
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
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 22 of 26
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)
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 23 of 26
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.
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 24 of 26
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.
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 25 of 26
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.
AEGIS 24.04.2012
D1.7.5 “Networks and Knowledge-Intensive Entrepreneurship in Practice II: Technology-based Strategic Alliances in Industry” Page 26 of 26
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.