Social network formation and clusters: A two-way
job mobility network analysis of the Leiden Bio
Science Park
Fleur Louise de Groot
Student Number 335947
Master of Entrepreneurship, Strategy and Organization
Erasmus School of Economics, Rotterdam
Supervised by Sandra Phlippen
Co-read by
Completed December 2010
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Executive summary
This study investigates the degree to which a high tech cluster is embedded in a local
network, the characteristics that a well-functioning cluster network possesses, the
different types of actors involved in cluster networks and the central position of specific
actors in a cluster network. It proposes a new methodology in cluster network analysis
which includes both scientific and higher management team member employees in the
network analysis simultaneously, based on the importance of a combination of scientific
and market knowledge in high technology industries. Specific network properties are
linked to increased network performance and cluster success. The cluster network is
analyzed using job mobility networks of scientists and higher management team
members in the Leiden Bio Science Park in the Netherlands. The results imply that
successful clusters are highly embedded in local networks. The cluster network possesses
qualities that are inherent to well-functioning networks, which has positive implications
for cluster performance. The theoretical and empirical results support the inclusion of
management team members in future analyses and support the use of policy measures to
stimulate job mobility in clusters.
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Table of Contents
Executive summary..................................................................................................... 1
1. Introduction ................................................................................................................. 3
2. Theoretical Background .............................................................................................. 7
2.1 The Embeddedness of Networks .............................................................................. 8
2.2 Network Topology .................................................................................................. 15
2.3 Types of Actors in Cluster Networks...................................................................... 18
2.4 Central Actors in a Network ................................................................................... 23
3. Methodology ............................................................................................................. 24
3.1 Local Embeddedness Measures .............................................................................. 27
3.2 Topological Characteristics of Networks................................................................ 28
3.3 Types of Actors ....................................................................................................... 32
3.4 Central Actors: Bridging and Brokerage ................................................................ 33
4. Data Collection ......................................................................................................... 35
4.1 Scientist Network .................................................................................................... 37
4.2 Management Team Member Network .................................................................... 39
5. Results and Analysis ................................................................................................. 40
5.1 Level of Embeddedness of the LBSP Scientist Network........................................ 40
5.2 Topology of the LBSP Scientist Network .............................................................. 43
5.3 Type of Actor Networks ......................................................................................... 46
5.4 Central Actors in Combined Network .................................................................... 48
6. Conclusions ............................................................................................................... 50
6.1 Level of Embeddedness .......................................................................................... 50
6.2 Topology of a Successful High-tech Cluster .......................................................... 51
6.3 Type of Actors in Cluster Networks ....................................................................... 52
6.4 Central Roles in High-tech Cluster Networks ........................................................ 53
7. Limitations ................................................................................................................ 55
Acknowledgements ................................................................................................... 58
Appendix ................................................................................................................... 59
Bibliography ............................................................................................................. 60
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1.
Introduction
High-technology (high-tech) clusters are a popular agenda item for local and national
governments world-wide. Clusters can be defined as a geographically concentrated group
of firms active in similar or closely connected technologies and industries with both
horizontal and vertical linkages (Dahl, 2002). Resources are invested into developing
high-tech clusters as it is widely believed that the clustering of companies, educationalinstitutes and knowledge institutes stimulates an innovative climate which in turn will
benefit the economy, education and the employment opportunities1. More specifically
they are said to catalyze economic transformation, drive growth, enhance stability and
provide a chance for economic success (Mallet, 2004; Porter, 1998). High-tech clusters
are for example supported on a European level as part of the ambitious Lisbon strategy
set out in 2000, which aims to make the EU the most competitive economy in the world
by 2010.
Considering the economic importance yet relative scarcity of successful high-tech
clusters, a large amount of literature has probed into the underlying dynamics that
influence their formation and performance. The existence of ‘social networks’ in clusters
has emerged as an important factor in the determination of general cluster success
(Breschi & Lissoni, 2003; Gulati et. al.; 2000). Social networks provide benefits for
clusters by providing ‘social capital’ (Inkpen & Tsang, 2005; Uzzi & Gillespie; 2002).
Social capital can be defined as the ‘aggregate of resources embedded within, available
through, and derived from the network of relationships possessed by an individual or
organization’ (Inkpen & Tsang, 2005). A firm or individual can thus benefit from being
embedded in a social network through improved access to resources and information.
A central mechanism through which social networks in clusters form is through the
mobility of employees between firms (Dahl, 2002; Franco & Filson, 2000; Klepper,
2002). This ‘job mobility’ consists of employees moving between existing firms as well
as leaving firms to create start-ups within the cluster. As these mobile employees remain
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Leiden Bioscience Park website http://www.leidenbiosciencepark.nl/about_leiden_bsp accessed on 12-092010
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related to their former colleagues, while at the same time forming new relations within
their new company, a ‘job mobility network’ is created. The evident importance of job
mobility for cluster success has in turn sparked the interest of scholars in the functioning
and dynamics of the labor markets and job mobility networks in clusters (Casper, 2007;
Casper & Murray, 2005; Higgings & Gulati, 2003).
While there is qualitative and quantitative evidence that looks into the effects that labor
market variations have on micro level job mobility networks, limited evidence is
available on the specific mechanisms at work in job mobility networks of clusters on a
micro level. Exceptions are the studies by Casper and Murray (2005) and Casper (2007).
The former looks into the effects that macro-level institutions have on micro-level job
mobility network dynamics in the successful bioscience clusters of Cambridge and
Munich, while the latter looks into the development of the San Diego bioscience cluster.
These studies find that in all three cases well developed job mobility networks exist, but
contain limitations in terms of data set completeness, the types of actors included in the
networks and a bias towards pro-publishing firms.
Considering the limitations of the studies by Casper and Murray (2005) and Casper
(2007), the general lack of research in this area and the economic importance of clusters,
it is of interest to verify previous results and to refine and extend previous methods. In
order to do so this study will perform a job mobility network analysis of a bioscience
cluster based on four main research questions. These research questions are;
1.
To what degree is a successful high-tech cluster embedded in a local network?
2.
What are the characteristics of the topology of a successful high-tech cluster
network?
3.
What type of actors are involved in the functioning a high-tech cluster network?
4.
What type of actors play a central role in the functioning of a high-tech cluster
network?
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Improving and extending previous research on job mobility networks in successful
clusters by answering the above questions can provide additional evidence for policy
makers that job mobility is a potentially valuable tool for stimulating cluster growth and
performance. More detailed information about the empirical characteristics of job
mobility networks can help identify target areas or actors for which specific policies can
be designed.
In order to attach any kind of value to a network measure one can either compare the
network over time or one can compare it to a real or random network. In this study the
research questions will be answered by analyzing the job mobility network of scientists
and management team (MT) members in the Leiden Bio Science Park (LBSP) in the
Netherlands. The networks will be analyzed by comparing selected network measures to
previous studies and/or to randomly generated networks. This research aims to provide
additional empirical evidence for the existence of dynamic job mobility networks in
successful clusters. The first goal is hence to provide improved evidence that the LBSP as
successful cluster is embedded in a local network (cluster network). The second aim is to
identify and demonstrate the topological characteristics of well-functioning cluster
networks. Thirdly this study aims to support the inclusion of different types of actors in
social network analyses and recommends a change in current cluster network analysis
methodology. The fourth aim is to illustrate the importance of central actors in a network
and to identify their characteristics in a cluster network that includes both scientists and
MT members.
The study will be structured as follows. First a theoretical framework will be built in
Chapter 1 which supports the research questions. Next the method of data collection will
be presented in Chapter 2. Chapter 3 will explain the data collection process, after which
the results of the analyses will follow in Chapter 4. The LBSP scientist job mobility
network will be presented in Sections 4.1 and 4.2, and a comparison will be made with
the networks as found in the bioscience clusters of Cambridge and Munich. In Section 4.3
the MT member job mobility network will be presented. In Section 4.4 the central
employees in the combined network will be identified. Lastly in Chapter 5 the
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conclusions will be presented and the implications of the results and limitations of the
research methods and theory will be discussed.
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2.
Theoretical Background
There is increasing recognition in literature that social capital is the key to building and
sustaining a successful high-tech cluster. A ‘successful cluster’ can be interpreted in a
broad sense, meaning that the firms in the cluster are increasing in number, surviving,
growing or making a profit. Talented managers and scientists provide high-tech firms
with the human and social capital which forms the foundation of their innovative
capabilities and performance (Becker, 1962; Casper & Murray, 2005; Higgings & Gulati,
2003). Social network theory suggests that it is especially the process of interaction
between skilled individuals within a cluster which determines cluster success. Social
capital is thus formed through social networks. Specifically of importance are said to be
the informal links between scientists, engineers and managers. These informal links raise
the innovative capacity of a high-tech cluster through disseminating technological and
market intelligence (Casper, 2007).
A social network can form based on any type of linkage between individuals and firms,
wherefore many different types of cluster networks have been analyzed. Next to job
mobility networks, previous studies have looked at co-patenting networks, formal
alliance networks, R&D networks and personal networks for example (Johannisson,
1998; Owen-Smith & Powell, 2004; Porter et. al., 2005; Zaheer & George, 2004). This
study will consider a job mobility network as theory and research suggests that a strong
link exists between the social network of an individual within a cluster and their job
mobility network (Casper, 2007; Casper & Murray, 2005; Almeida & Kogut, 1999). A
related observation is that firms embedded within regions with a decentralized culture of
high mobility and knowledge diffusion have a regional advantage over firms who are not
(Casper, 2007; Herrigel, 1993; Sabel, 1992; Saxenian, 1994; Storper, 1997).
Based on this research it is suggested that the existence of a high level of job mobility
and a well developed job mobility network in a cluster creates various forms of social
capital. Social capital will be considered to be any benefit that a firm derives from being
‘embedded’ in a social network, as is implied by the definition. As ‘job mobility’ is a
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prerequisite for a ‘job mobility network’, it should be noted that they are two distinct
concepts. The benefits that a cluster derives from job mobility in general and the benefits
that it can derive from job mobility networks specifically will therefore be addressed
separately in Section 2.1, which concerns the benefits for a cluster of being ‘embedded’
in a network.
It is also interesting to look at the specific topological characteristics of a network. How
can the structure of a network influence the formation and performance of the network in
terms of creating social capital? Different types of network structures will be described in
Section 2.2. As mentioned, a social network is formed by relations between any
individuals of firms within a cluster. Why do cluster network studies include only one
type of actor, often the scientists? Do managers contribute equally to the formation of
social capital? Sector 2.3 will support the inclusion of both scientists and managers in
cluster network analysis. In the combined network of scientists and managers there will
always be some actors who perform more important functions than others. Is one type of
actor more central than the other, and what function do these central actors play in the
overall network? The theory concerning central actors will be addressed in Section 2.4.
2.1 The Embeddedness of Networks
While there are many definitions of embeddedness, this paper will use Granovetter’s
original formulation that embeddedness refers to the notion that all economic behavior is
embedded in social context (Granovetter, 1973). Specifically structural embeddedness
will be considered, which captures the extent to which an entity is entrenched in a
network of relationships (Grewal et. al., 2006). Based on this definition of structural
embeddedness, a well developed job mobility network with a high level of connections
can be directly related to a high level of embeddedness of firms and individuals in a
cluster network.
In recent literature embeddedness has been considered mostly as an inter-organizational
phenomenon where the level of embeddedness depends on the quality of the relationships
between firms (Almeida & Kogut, 1999; Uzzi, 1996). This paper supports an individual
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approach however, where a job mobility network is used as a proxy for analyzing the
level of local embeddedness in the social network (Casper & Murray, 2005). This
individual approach as opposed to a firm-based approach is based on the individualistic
nature of social capital, job mobility and social networks. The underlying thought is that a
high level of inter-firm mobility of employees within a cluster leads to an extensive job
mobility network where many individuals are acquainted with one another through past
or current employment. These connections between individuals simultaneously form
connections between firms and thus connections within the cluster. As one type of
connection automatically constitutes the other type of connection, there will be no
differentiation made throughout the paper between the embeddedness in a network of
individuals, firms or clusters. Each constitutes the same idea.
Now that the theory behind the method of analysis has been explained, it must be further
clarified why the level of embeddedness is of importance for a high-tech cluster. Next to
the theories about the formation of social capital that have been mentioned, another
theory that considers some of the benefits and processes involved in the embedding of a
firm in a network is the theory of embedded local economic growth. It states that local
economies are “islands of superior productivity, integrated into a global mosaic of
production that brings the reward of sustainable local accumulation” (Taylor, 2005). This
superior productivity is said to be the result of the complex process of ‘embedding’,
which Taylor referred to as “the incorporation of firms into place-based networks
involving trust, reciprocity, loyalty, collaboration, co-operation and a whole raft of
untraded interdependencies” (Taylor, 2005). This process of embedding creates social
capital, which in turn fosters the creation of products, services, processes and ideas that
are not appropriated by individual actors but are collectively shared in the system
(Leborgne & Lipietz, 1992).
Other benefits generated by being embedded in a network that have been mentioned are
that it will provide improved opportunities for learning, access to technologies and
resources, increased legitimacy and an opportunity to improve the firms its competitive
position (Dyer & Singh, 1998; McEvily & Zaheer, 1999; Nohria & Eccles, 1992). Three
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specific mechanisms involved in the embedding of a cluster network that can lead to such
benefits will be considered in more detail in Section 2.1.2 to 2.1.4, namely the function of
a network as labor pool, the safety net function of the network and the overlap between
professional networks and social networks. First of all however Section 2.1.1 will support
an important general benefit of job mobility for the cluster.
2.1.1 Job Mobility as Mechanism for Knowledge Spill-overs and Knowledge
Diffusion
A knowledge spill-over is an exchange or movement of ideas amongst individuals or
between firms in which there is no transaction involved. Knowledge spill-overs can
benefit firms in terms of leading to innovation and growth. They are most likely to occur
in a specialized industry where firms are located in close proximity to one another and
when these firms are involved in highly innovative, high-tech industries such as in a bio
science cluster. This is so because the type of knowledge involved in such industries is
highly tacit and therefore requires face-to-face interaction for transfer. Tacit knowledge
by definition is non-codifiable and cannot be formalized (Audretsch & Feldman, 2003).
Casper and Murray (2005) mention that knowledge spill-overs can take place through
various mechanisms, including through job mobility, spontaneous social interaction
between employees and planned interaction. Social connections are thus a requirement in
order for knowledge to spill-over.
The difference between knowledge spill-overs and knowledge diffusion is important to
note here. In relation to job mobility, knowledge diffusion refers to knowledge that is
embodied in an employee which is included in the economic value involved in switching
jobs, such as the salary of the employee and the recruitment costs. Knowledge diffusion
is also highly valuable for the performance of a cluster and is often a driving force behind
head hunting and acquiring new talented employees with a valuable knowledge base. A
knowledge spill-over here is the ‘extra’ knowledge that such a new employee brings to a
firm that is not included in their economic value.
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The reason that knowledge spill-overs and diffusion are strongly linked with job mobility
is because “ideas are embedded in the minds of individuals” (Feldman, 2000), and
movement of these individuals between firms allows employers to benefit from the
knowledge that has been accumulated throughout their careers with other firms (Dahl,
2002). Knowledge therefore flows between companies with the mobility of employees.
This includes both the movement of an employee between 2 existing firms and the
situation in which an employee creates a start-up. Both knowledge spill-overs and
knowledge diffusion are thus related to cluster benefits in terms of human capital as
opposed to social capital.
The existence and importance of knowledge diffusion in relation to job mobility is well
supported by literature. Almeida & Kogut (1999) for example analyze data on the interfirm mobility of patent holders and empirically show that the inter-firm mobility of
engineers influences the local transfer of knowledge and that this flow of knowledge is
embedded in regional labor networks. The importance of the knowledge that employees
involved in a start-up have accumulated in their parent firm is shown to be a very
important feature in a paper by Klepper (2002), who finds that the success of a new firm
in the US automobile industry is to a large degree determined by the experience and
background of the founder.
Evidence supporting the existence of knowledge spill-overs in relation to job mobility on
the other hand is not as straight forward. The concept of knowledge spill-overs itself has
been criticized strongly by Breschi and Lissoni (2001) who describe it as a ‘black box’.
They state that the evidence supporting knowledge spill-overs is ambiguous, that
scientists over-interpret this theoretical concept and that research does not go into the
mechanisms through which knowledge spill-overs actually occur. Dahl (2002) embraces
this criticism in his paper on the mobility of engineers in Danish knowledge clusters and
shows that job mobility is in fact a mechanism through which knowledge spill-overs
occur. A collection of other literature also supports the role that job mobility plays in the
creation of knowledge spill-overs and considers it to be a central mechanism fueling
rapid innovation (Almeida & Kogut, 1999; Casper, 2005; Saxenian, 1994). Evidence in a
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study on patenting and job mobility by Kim & Marschke (2005) supports that the
turnover of scientific personnel can be an indication of technological knowledge
diffusion and spill-overs.
Job mobility as mechanism for knowledge diffusion and spill-overs may specifically be
important for the LBSP because the other routes through which knowledge can be
transferred (spontaneous social interaction between employees and planned interaction)
are not likely to be fruitful. The main reason for this was mentioned by park manager
Annelies Hoenderkamp, who said that even when other firms might not benefit from the
information, many firms prefer to keep their innovative ideas a secret and thus
consciously prevent knowledge spill-overs and diffusion from occurring (Hoenderkamp,
2009). Another reason why job mobility may specifically be an important knowledge
spill-over and diffusion mechanism for the LBSP is because about 80% of the current
companies are spin-offs, which were started mostly by current or former employees of
the Leiden University Medical Centre (LUMC). The high number of spin-offs itself is
evidence that there may be a significant amount of knowledge spilling over and diffusing
through job mobility, which is contributing to the growth and success of the cluster.
2.1.2 Job Mobility Network as Labor Pool
An extensive job mobility network can be highly valuable for a cluster by functioning as
a labor pool from which firms can recruit employees. Both the size and the quality of the
potential labor pool can influence high-tech cluster performance and growth. The degree
to which firms have access to such a pool of talented highly specialized employees is
crucial for high-technology firms (Becker, 1962; Higgins & Gulati, 2003). This coincides
with the statement that the structure and dynamics of the local labor market have
important performance implications for high-tech clusters (Almeida & Kogut, 1999;
Audretsch & Feldman, 1996).
According to Saxenian (1994) one of the reasons behind the success of the Silicon Valley
cluster was the strong regional labor market for engineers, scientists and workers. People
in the area were mobile within the spatial boundaries of the region, but were not
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necessarily bound to a certain company and would switch jobs relatively easily and often.
This kept both the firms in the area and the employees competitive and operating on the
edge of technology, which improved the overall innovative capacity of the region.
On top of having access to a pool of talented employees, the job mobility network can
provide valuable information to a company on the level of expertise and talent of a
potential future employee. A current employee can recommend a former colleague for a
new job based on a positive previous experience of co-employment. An employee may
even be recruited based on positive information provided by a third party.
An example of a situation in which the job mobility network functions as a labor pool is
when a university scientist is involved in the startup of a spin-off company and
subsequently recruits former colleagues to join his team. Next to the fact that the founder
is aware of the existence of his former colleagues as potential employees, the chance that
these former colleagues will join a possibly risky new venture is larger when they have
some social connection to the potential new employer. This is because through previous
co-employment or even through second-degree ties, a certain degree of mutual trust can
form between them which can overcome part of the information asymmetry involved.
That direct ties and second-degree ties can overcome information asymmetry in
economic transactions is generally supported by empirical evidence as found in
Granovetter (1985), Gulati (1995), Shane and Cable (2002) and Uzzi (1996).
2.1.3 Job Mobility Network as Safety Net
In order to be able to have a high level of job mobility, next to a pool talented employees
being available, the success of the firms in the cluster is in part determined by their
ability to entice skilled managers and employees to leave lucrative and often safe jobs to
join a new firm. This is specifically relevant for start-up ventures, as these usually entail
the highest levels of risk, while at the same time being crucial for the innovativeness and
growth of a cluster. According to Casper (2007) the reason why risk-adverse scientists
and managers are willing to leave their jobs for risky new ventures is because within
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successful clusters the embeddedness of individuals within social networks makes it safe
to do so.
Casper (2007) highlights the importance of a job mobility network as a safety net on
which individuals can rely in case a new venture fails and they lose their job. The easier it
is for an individual to be re-employed locally, the more likely they may be to switch to a
failure prone job. This results in a ‘recycling mechanism’ of employees (Brahami &
Evans, 1999; Casper, 2007). A relatively smooth recycling mechanism was also observed
by Saxenian (1994) in the Silicon value cluster, who mentioned that “moving from job to
job in Silicon Valley was not as disruptive of personal, social, or professional ties as it
could be elsewhere”. Casper (2007) even suggests that if such a safety-net does not exist
it can seriously hinder the success of a cluster and could be a reason why clusters fail,
even if they have reached a sufficient size for survival.
A career affiliation network as safety-net in combination with the existence of a talented
labor pool goes hand-in-hand with a higher level of job mobility. If the labor market
functions smoothly it enables firms to alter their research strategy quicker through hiringand-firing when necessary. Firms within the cluster can thus react to market changes
faster than competitors outside of the cluster. Such organizational flexibility can provide
firms with a considerable competitive advantage which can ultimately contribute to
overall cluster performance.
2.1.4 Job Mobility Networks and Social Networks
The last mechanism through which a job mobility network and the related level of
embeddedness can influence cluster success is through the strong connection between the
job mobility network or the ‘professional’ network of an individual and their social
network or friendship network. Individuals can form social connections through
professional connections, by for example meeting contacts of colleagues at social events.
The opposite can also occur, as a social connection can also lead to a professional
connection. This happens for example when the founder of new company recruits a
fellow researcher whom he knows from college or from a social club outside of the
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cluster. Also considering the fact that individuals tend to live at a reasonable distance
from their work, it is likely that an overlap exists between the social and professional
network of an individual. These social network connections would increase the total
number of ties in the network within a cluster if they were to be identified and included.
Social networks of people play a similar role in stimulating knowledge spill-overs and
job-mobility within clusters as job mobility networks do. There is evidence that the social
network of top-managers of a company in terms of the network size, the strength of
connections and the distance between actors, is positively related to firm performance in
high-tech industries (Collins & Clark, 2003). Saxenian (1994) mentions that social
networks in Silicon Valley increased labor mobility across firms and by doing so created
an additional mechanism of knowledge diffusion. Casper (2007) links sustainable
networks in clusters to the existence of dense social networks across key personnel.
Breschi and Lissoni (2001) on a similar note highlight the degree to which social and
professional contacts overlap and mention that ‘epistemic proximity may arise from
shared work or study experiences, or former cooperation efforts that required face-to-face
contacts and a high degree of socialization… Although highly dispersed in space,
members of these epistemic communities share more jargon and trust among them than
with any outsiders, no matter how spatially close’.
2.2 Network Topology
While the embeddedness of a high-tech cluster from an individual perspective can reveal
much about the network and labor market dynamics, the topology of a network can also
provide insightful characteristics from a more holistic point of view. The topology of a
network refers to the physical layout pattern of interconnections between actors. There
are many different types of measures that can be taken into consideration, several of
which are straight forward in their implications for the functioning of a social network.
These measures will be mentioned in the methodology section. Two topological
characteristics of social networks that do require theoretical background as to their
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interest for the analyses are the core-periphery model and the small-world network
phenomenon.
2.2.1 Core-periphery Model
While the benefits of clusters are well-researched, the process through which clusters
become sustainable remains to be further researched and developed. One study which
does consider cluster development over time based on career history data is by Casper
(2007). He finds evidence that there is a strong core of employees who were central to the
development of the San Diego bio tech cluster. This core group evolved as the scientists
had all worked together at one of the first companies in the cluster, which went bankrupt.
Most continued their careers in the cluster by starting new ventures or joining existing
companies.
One way in which such a core can thus develop is based on historic reasons, as a core of
actors may have been part of the original network present at beginning of the formation
of the cluster, as was the case in San Diego. This type of structure in the development of
a cluster is known as a ‘backbone’ as discussed by Casper (2007). Multiple studies
support the idea that a small group of valuable employees of one of the oldest firms in a
network can become the founders of new ventures within the cluster and in this way
become central in both the process of development and the current functioning of the
network (Casper, 2007; Feldman, 2003; Sorensen, 2005).
The core can function as a catalyst in the emergence of a network, as it entails a group of
initially tied actors to which new network members can latch on to, which promotes the
growth of a cohesive network (Casper, 2007; Powell et. al, 2004). These core members
are hence crucial to the embedding process in these networks.
Another study related to the development of clusters by Feldman (2003) considers the
idea that ‘existing firms may serve as anchors that establish skilled labor pools,
specialized intermediate industries and provide knowledge spillovers for new technology
intensive firms in the region’. She suggests that one large and successful firm can form
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the basis of the direction of specialization and of the growth of a cluster. Similar to the
‘backbone’ theory of Casper, she supports the idea that such an anchor firm can supply a
cluster with potential entrepreneurs who can use the knowledge of their parent firm to
create start ups. This supports the existence of a core-periphery structure in successful
high-tech cluster networks
It will be interesting to see whether the LBSP scientist network has evidence of a central
core of scientists who were perhaps part of such a backbone structure as mentioned in
cluster development literature. In an ideally locally embedded network however, where
all ties are equally dispersed, such a backbone or small core should not be visible. That it
is not visible however does not need to mean that it does not exist.
2.2.2 Small-world Phenomenon
In order to measure the performance of a network, an interesting method has been
developed by Watts (1999) and others based on the analysis of real-world networks,
known as the ‘small-world’ method. They noted that in large real-world networks, there
is often a structural pattern that seems paradoxical. On the one hand they found that in
general the path length, or average number of links along the shortest paths between
actors in a network, is relatively short, regardless of how large a network is. A wellknown example is the “6 degree of freedom” principle, which states that in order to get
from any one person in the world to another one must pass through only 6 connections.
On the other hand it turns out that there is a large degree of clustering visible locally,
where people in the same geographical area such as in neighborhoods are all connected to
one another. This means that the density in such neighborhoods is much higher than if
connections had been formed there at random. This is paradoxical, as the former states
that we are connected to everyone in the world through short distances, while the latter
suggests that at the same time we live in a narrow social world, where everyone knows
everyone. Examples of small-world networks are power grid networks and friendship
networks.
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When a network has small-world properties it is considered to be a pareto optimal
equilibrium as it means that the network is both robust and efficient (Wilwhite, 2001).
Wilwhite (2001) shows this by proving that there are significantly lower search and
negotiation costs involved in a hybrid model of economic exchange when the network
possesses small-world characteristics. It is thus an ideal network structure for the spread
of information and hence can positively influence the speed of innovation within a
network. He suggests that there are even ‘private incentives for such a system to arise’,
which for cluster networks can be interpreted to mean that it is worth investing resources
into stimulating the formation of a small-world network structure in order to improve the
performance of the network. Ultimately a small-world structure can thus contribute to a
well-functioning network which can positively influence cluster success.
2.3 Types of Actors in Cluster Networks
Now that the benefits of being embedded in a cluster network based on job mobility and
the theories of well-developed networks have been discussed, a step-back will be taken
and the actors involved in a high-tech cluster network will be considered. Who is actually
responsible for the formation and performance of a cluster network? Most literature
focuses on the role of scientific employees in the transfer of knowledge and the formation
of labor mobility networks. The characteristics of scientists in the labor market will
therefore be considered first in Section 2.3.1.
It can be argued however that next to scientists, another type of employee of high-tech
firms also plays an important role in the formation and functioning of a cluster network,
namely the higher management team (MT) members. Why is the job mobility networks
of scientist usually analyzed while senior managers and (star) scientists both can be
assumed to play an important role in the determination of firm success? MT members
strongly influence the strategy and direction of a firm and are responsible for bringing a
product to the market successfully. This can be said to be just as important for firm
success as the initial innovation development by scientists. This also holds for high tech
firms involved in non-R&D firms, such as manufacturing.
18
The importance of MT members in cluster networks will be further elucidated in Section
2.3.2. It will be argued that the MT members can influence the level of embeddedness
and the functioning of the network in the same way as scientists can. This includes a role
in the process of knowledge spill-overs, the process of labor pool formation, their
involvement in the formation of a safety net and the importance of their social network
connections.
2.3.1 Scientists
Current literature mainly focuses on the scientific employees of firms when analyzing
networks (Casper et. al, 2005; Saxenian, 1994). This is as mentioned because scientists
are suggested to play an important role in knowledge spill-overs and knowledge diffusion
due to the high level of tacitness of the knowledge involved in their high-technology
functions. Working on the edge of technology, their knowledge may be unique and solely
transferrable to another firm by the scientist switching jobs. Their knowledge tends to be
highly specific and crucial in the process of innovation.
An interesting question that follows is whether scientific employees in high-tech
industries are highly mobile between firms or not. Saxenian (1994) noticed that within
the Silicon Valley scientists were indeed highly mobile and that this contributed to the
performance of the cluster. She suggests that scientists can be so highly dedicated to their
field of study and to advancing technology that they may not be loyal to their firms, but
rather to science. She observed that scientists would switch jobs easily as long as they
were provided with better research facilities, a higher salary or more research funds for
example. Ackers (2004) notes that it is the nature of scientific jobs that leads to a high
mobility of scientists, as she says that career progression in scientific research ‘demands a
very high level of mobility’.
As scientists are likely to be mobile, the question arises whether they are mobile locally
(within the cluster) and/or non-locally. Job mobility tends to be geographically bound as
individuals are generally reluctant to move. An employee is therefore likely to prefer a
new job close to home as opposed to having to move to a new city or even country. The
19
geographic pull-factor is stronger in case a scientist has a partner, has children, has family
in the area or has a well-established career (Acker, 2004). It can therefore be expected
that scientists may be mobile especially within the cluster.
There is also evidence that scientists are motivated to be mobile globally based on
institutional factors. Research by Dickson (2003) on the migration of Italian scientists
supports that they are not motivated to switch firms based on financial gains, but are
rather concerned with the general funding of science and the influence that this has on
their ability to perform their job. Dickson (2003) says that scientists ‘leave their home
countries … to seek an environment in which they can “work effectively with enthusiasm
and support”. Other institutional push- and pull factors include the degree of contractual
insecurity, the cost of living, pensions and social benefits such as healthcare and
childcare (Acker, 2004; Dickson, 2003).
Is the institutional setting in the Netherlands on a national level a potential push- or pullfactor? Are there specific push- or pull factors of the LBSP on a local level? These are
interesting questions for further research, but cannot be addressed in this paper based on
the local scope and micro-level of analysis. The institutional setting will however be
considered as possible influential factor in the determination of local job mobility levels.
It can lead to national differences between job mobility levels in clusters. It is therefore
interesting to gauge the mobility of scientists not only compared to other clusters, but
also compared to employees from the same cluster and thus with the same institutional
setting.
2.3.2 Management Team Members
In the network analyses of high-tech industries, the role of the MT seems often
overlooked. Senior managers as opposed to other non-scientific employees specifically
play an important role in the relationships that companies have with the environment in
determining the direction and strategy of the company for example. They hence clearly
have an influence on the success of the company. If they play such an important role for
the firm, are they not also likely to be important in the formation and functioning of the
20
cluster network? Casper (2007) supports the investigation of other types of actor
networks and performs a job mobility analysis of senior managers. He provides evidence
that senior managers form dense social networks within the San Diego bio science
cluster. Similar to scientists, MT members are expected to be mobile locally, as their
movement is geographically bound while career advances or entrepreneurial ventures
often call for a job switch. In the following paragraphs it will be argued that MT
members play a similar role in the creation of social capital within a cluster as scientists
do, and that ultimately a cluster network is more complete once the two networks are
combined.
Firstly it can be argued that contrary to intuition, MT members can also be involved in
knowledge spill-overs. They can play a role in the spill-over of both tacit knowledge and
generic knowledge. Tacit knowledge is a broad concept, which includes not only
scientific knowledge but also technical knowledge, which can be held in managers in the
form of either unparalleled previous experience in a unique line of business or by a
unique combination of scientific and industry knowledge for example. Technical
knowledge can be highly specific and tacit in that it is often transmitted in “the jargon of
a much closer and restricted community (an ‘epistemic community’). Members of the
community learn it by joining it to practical experience, and cannot transmit it to any
outsider by informal means” (Breschi & Lissoni, 2001). Most managers in the LBSP also
have a scientific background and are likely to also understand at least an important part of
the scientific jargon next to their technical and managerial knowledge.
As the definition of a knowledge spillover is the exchange of ideas amongst individuals
without an economic transaction, ‘generic’ management knowledge can also spillover.
General management knowledge of MT members may specifically be valuable because it
may not be as generally applicable as one might expect. As mentioned such a highly
specific and unique industry like the bio technology (bio tech) industry involves a high
degree of jargon and specific knowledge, which is also used in the general management
knowledge of such a company. The generic knowledge is thus often combined with tacit
21
knowledge in the form of specific jargon as used in the epistemic community, and in this
way can be valuable as a spill-over.
Next to knowledge spill-overs, the related concept of knowledge diffusion is also a
valuable aspect of MT member job mobility. The unique qualities that an MT member
possesses are valuable assets for firms which they are willing to pay a price for. An MT
member who for example has the knowledge and experience of setting up a bio-tech
company has an exclusive combination of generic and tacit knowledge, which when used
in another company or start-up can contribute to cluster success.
Secondly, next to knowledge spill-overs, MT members are expected to be involved in the
same cluster-wide benefits of being embedded in a job mobility network as scientists are.
MT members are also a part of the valuable labor pool within the cluster as they may
have a unique combination of understanding of the industry, scientific knowledge and
management know-how as mentioned. Having an experienced founder with the relevant
industry know-how can clearly benefit a new venture. Such new ventures do not just
benefit from the knowledge base of their managers, but also derive benefits from their
professional and social networks. Sorensen (2005) for example suggests that founders of
high-tech firms commonly recruit friends and former colleagues. Higgins and Gulati
(2003) find that recruiting talented senior managers is in fact strongly linked to the
success of bio tech firms, which also links back to the importance of MT member job
mobility and the availability of a labor pool.
Thirdly, the earlier mentioned high level of job mobility of MT members and their unique
knowledge base are intertwined with the function of the network of an MT member as a
safety net in case of unemployment. It is vital for the development of a high-tech cluster
that MT members willingly leave safe jobs to found new ventures, or start new ventures
next to another job. A safety net increases the chances that MT members will be willing
to engage in such risky entrepreneurial new ventures. The safety net of MT members in
part is already determined by the nature of board functions, as MT members are often a
member of multiple boards at a given time. If one of the firms in which a multi-board
22
member is involved fails, he or she will not be left unemployed. A large percentage of
MT members in the LBSP dataset are members of multiple boards at the same time.
Fourthly, again similar to scientists, MT members are likely to have a strong overlap
between their professional and social networks, as supported by Sorensen (2005). This
will strengthen and expand the overall network in which they are embedded. These four
factors suggest that MT members perform an important function in the formation and
performance of a cluster network and hence of the overall cluster. It will therefore be
interesting to see whether the characteristics of the scientist and MT networks differ or
not, considering these theoretical similarities. The overall importance of MT members in
a local cluster network as theorized supports the notion that they ought to be included in
high-tech cluster network analysis.
2.4 Central Actors in a Network
The novel combined network of the scientist network and the MT network can give
interesting clues as to the structure and characteristics of a more complete cluster
network. As the combined network consists of 2 possibly distinct types of epistemic
communities, these networks may not be fully integrated. Based on the importance of the
combination of scientific and industry knowledge in high tech industries, it could be
predicted that those individuals with the highest degree of aptitude in combining the
scientific and the management knowhow will occupy more central positions in the
network than others.
Interestingly enough there are several members in the data sets who belong to both the
scientific data set based on previous patents and the MT member data set based on
current management positions. Theoretically actors in such a position could be potential
bridge-builders between the two types of networks and could have a broker position
within the network. Such a broker can control the flow of information through the
network, making this a potentially powerful and vital position in the network. At the
same time such a position can be a single point of failure, as part of the network may not
be able to reach one another if the actor drops out of the network.
23
The existence of a link between the scientists and MT members is highly valuable as it is
crucial for the success of the bio tech industry that highly innovative scientific
discoveries are successfully introduced to the market. This requires skilled managers with
industry knowledge, an industry-wide network and a combination of scientific and
management experience, which are thus indispensible in high-tech industries. Boschma
(2005) for example supports that a combination of technical and market knowledge is
valuable specifically for the process of knowledge spill-overs. He states that is it not
sufficient for a high-tech firm to simply possess the often highly tacit knowledge, but that
is crucial for the absorption process of knowledge as described by Cohen and Levinthal
(1990) that a firm also possesses the technical and market competencies in dealing with
the specific market and technology related to that knowledge (Boschma, 2005). These
competencies may thus be embodied in the scientist-managers, and proof of a broker
position could support the dependence of the network upon them.
Another mechanism through which these broker functions are of importance to the
overall network is through their contribution to the desirable small-world properties as
mentioned in Section 2.2.2. Actors with a broker function contribute to a low path length
and a high cluster coefficient based on the nature of a broker function. The central
function as a broker can also depend simply on the job history of an individual and on
their personal level of mobility as opposed to the type of function the individual has. The
position in the network of these management-scientists will therefore be analyzed.
3.
Methodology
In order to study a high-tech cluster network the bio tech industry has been chosen as a
relevant industry as it is known for the high levels of technology involved and the speed
of innovation and change. Bio tech concerns the commercialization of scientific
discoveries related to genetic engineering, which is a relatively new industry with a high
level of potential and global competitiveness. Significant levels of resources are
employed to stimulate new firm development and bioscience cluster formation. It is also
an interesting industry as there are few firms actually profitable and because a strong link
24
has been suggested to exist between those companies that are successful and the degree to
which they are linked to external networks of various kinds (Bathelt et. al., 2002; Porter,
1998; Powell et al., 1996).
In order to investigate the job mobility network in a cluster, the Leiden Bio Science Park
will be used as a case study. The LBSP is a useful and appropriate case as it is home to
more than half of all biomedical life science companies in the Netherlands. The city of
Leiden, the Province of South-Holland and the Dutch Ministry of Economic Affairs
consider the LBSP to be the life science hotspot of the Netherlands2. The centre also has
recently won the prize for best business park of the Netherlands in 2009. Leiden won the
prize for its daring choice to specialize in biomedical life science and for making a
success out of it.
The LBSP itself has recognized the importance of micro-level job mobility as they
mentioned that they have recently made plans to facilitate labor-mobility through the
creation of a labor-pool to “retain workforce and talent, provide jobs and improve
employability”3. As the LBSP is a successful cluster, and as successful clusters have been
linked to the existence of extensive job mobility networks in previous studies, this paper
is biased towards finding a well developed job mobility network. This is not a problem
however as the main aim of this study is not to prove the existence of such a network but
to further explore the underlying dynamics and characteristics of such job mobility
networks.
Employee careers are a vital instrument through which mobility, accumulated social
capital and human capital are established (Baker, 2000; Uzzi & Lancaster, 2003; Burton
et. al. 2003, Casper, & Murray, 2005). A career does not only encompass employment
with a traditional firm, but also includes co-patenting activities and faculty positions. In
combination with the informal connections established between employees, these
activities contribute to the human capital that individuals take to firms (Murray, 2004;
2
3
http://www.leidenbiosciencepark.nl/about_leiden_bsp
Presentation by Annelies Hoenderkamp
25
Casper & Murray, 2005). The job mobility network for scientists is therefore defined as
all previous employment at the LBSP, including faculty positions and co-patenting
activities.
A job mobility network is a social network which can be defined as a bounded set of
connected social units. The boundary of the network is defined by the firms in the LBSP,
as only current and former employees of the park are included in the data sets. The
‘connectedness’ of these actors is of a binary type, where a connection is established
based on whether people have worked for the same company, or whether they have not.
Employees are assumed to know one another regardless of whether they were employed
at a company at the same time, which is not seen as a problem based on three reasons.
Firstly the majority of the data covers an approximate 10 year time-span, wherefore the
likelihood that 2 individuals worked within the same firm is relatively large. Secondly,
many jobs in the data set are academic jobs, which tend to be long term affiliations,
sometimes even for life. This increases the likelihood that 2 actors worked for the same
institution at the same time. Thirdly, as the institutions and departments of the university
have been separated to the finest degree, there is again an increase in the likelihood that 2
actors worked there simultaneously. Even if actors did not work simultaneously, there is
a reasonable chance that they do know each other directly or indirectly.
The network will be looked at from an individual actor point of view as well as from a
holistic point of view, as local connections of actors can be important for understanding
the social behavior of the whole population. Firstly the method used to analyze the
scientist network will be clarified on an individual (embeddedness) and holistic level
(topology). Then the method for the analysis of the MT member network will be
explained briefly, as it is similar to the method used for the scientist network. Next the
method for the overall network analysis of central actors will be illuminated. For all 3
networks, the structure and characteristics can be best made visible through a network
visualization which will be made using the Netdraw application of UCINET. The random
networks have been constructed using the ‘Erdos Renyi’ random network function in
Ucinet and Netdraw.
26
3.1 Local Embeddedness Measures
As opposed to the standard approach to embeddedness which takes the firm as unit of
analysis, this paper will consider inter-personal relationships as the basis of the cluster
network as described. As well-developed and successful cluster, the LBSP is expected to
be strongly locally embedded in the network. There is hence expected to be a high level
of mobility amongst scientist and managers alike. There are various approaches to
characterizing the extent and form of the embedding of actors in a network. Since there is
not one way of indexing the degree of embedding, multiple approaches can be used
(Hanneman & Riddle, 2005).
A network can be either compared over time, to other networks or to randomly generated
networks. The local embeddedness measures selected, which include the size of a
network, the distribution of components and the network density, will be compared to
those of the Munich and Cambridge clusters as analyzed by Casper and Murray (2005).
This is done because network data for these clusters is readily available and can
demonstrate relative values of these measures, as they are not indexed values. These
relative values can reflect important social conditions for embedding and will next be
considered in further detail.
3.1.1 Main Component Connectedness
The distribution of components looks at the total number of nodes in a network and how
many of those nodes are linked versus how many are not linked. Casper and Murray
(2005) call this the ‘degree of connectivity’. It is measured by the percentage of people
connected to the main component of the network, which is the largest group of connected
nodes. If the network is fully connected, a message that starts anywhere can eventually
reach everyone. If in one population most actors are embedded in at least one dyad, while
in the other population there are many isolated actors, the social structures are likely to
vary (Hanneman & Riddle, 2005). A study by Owen Smith and Powell (2004) found that
in the bio tech industry about half of the network members are generally in the main
component. Casper (2007) found that in the San Diego bio tech cluster 95% of the senior
managers was linked to the main component by the time the cluster had fully developed.
27
In a perfectly locally embedded network the actors are expected to all be connected to the
main component, as opposed to being isolated. Based on theory and earlier findings it is
thus expected that a relatively high percentage of actors in the LBSP scientist network is
connected to the main component.
3.1.2 Density
The density of the network measures the amount of interconnectedness per actor.
For binary data, density is the ratio of the number of adjacencies that are present divided
by the number of pairs, so the number of actual ties in proportion to the possible ties
within the network. The problem with the existing measures of density is that they are
size-dependent, which is why it is important that the number of nodes for the LBSP
scientist data is similar to Cambridge and Munich, to allow for comparison. The relative
density of a network can give insights into for example the speed at which information
diffuses among nodes, and the extent to which actors have high levels of social capital. In
a strongly embedded cluster network with a high level of mobility, the actors are
expected to have a relatively high level of interconnectedness and the level of density is
expected to be high.
3.2 Topological Characteristics of Networks
The topology of a network considers a top down perspective. This is valuable in order to
get an understanding and description of the network population as a whole based on the
way in which individual actors are constrained by the texture of their relations to one
another (Hanneman & Riddle, 2005). In order to get an understanding of the topology of
a high-tech cluster, several measures and characteristics will be considered which will be
described in the following sections. This includes the degree of network centralization,
the core-periphery structure, the path length, the cluster coefficient and the small-world
properties of a network.
3.2.1 Network Centralization
28
Network centralization is also known as global centrality, which measures the degree to
which a network is focused around a few central nodes (Scott, 1991). Freeman (1979)
based the measure of global centrality around the ‘closeness’ of actors and it is expressed
in terms of the distances amongst various points. Other definitions of network
centralization include “the degree to which relations are guided by formal hierarchy” and
“the degree to which an inter-organizational network is dominated by a few places"
(Irwin & Hughes, 1992). It is equivalent to the variance in network ties per actor. When
the variance in the number of network ties per actor is low, no actor enjoys substantially
more ties than any other actor and therefore no actor is more central than any other.
Conversely, when the variance in the number of network ties per actor is high, some
actors have proportionately more ties and therefore are more central than others. In a
strongly embedded network, optimally functioning network the ideal situation would be
that all actors enjoy a roughly equal number of ties to one another. If all nodes in the
network have the same degree centrality than the network centralization is 0.
3.2.2 Core-periphery Model
The core-periphery structure of a high-tech cluster will be considered as it can give clues
about the hierarchical structure of ties within a network as well as about the process of
development. A high-tech cluster network may for example consist of a small core of
closely interconnected actors surrounded by a periphery of actors whom are loosely
connected to this main core through a low number of direct or indirect ties. The
difference between the inter-connectedness of the core versus the loosely connected
periphery can for example indicate the presence of a hierarchy in the network or the
existence of a ‘backbone’, where one group of actors (the core) is more important than
another in the formation and functioning of the network.
While historic development data of the LBSC cluster is beyond the scope of this research,
the current measures do partially include such a historical perspective based on the fact
that the position of an actor in the network depends on the number of ties an actor has,
which is related to the number of jobs an actor has had, which can be expected to be
related to the number of years an actor has been in the network and the age of an actor.
29
At the other end of the spectrum lies a network which has no clear visible core and where
all ties are relatively equally dispersed. In such a case, where there is a very large core
and only a small or virtually no periphery, it can be argued that it is beneficial for the
overall functioning of the network. Any given actor in the network can more easily reach
another and knowledge can be transferred quickly and efficiently. Such an equally
dispersed network can be related to a high level of embeddedness. It is hence expected
that the LBSP scientist network does not have a strong core-periphery structure, unlike
the structure found in the Cambridge and Munich clusters. All scientists are expected to
ideally be on a similar level, without there being a strong hierarchy present in the
network. There is thus also not expected to be any evidence of a backbone structure in the
scientist network. The structure will be analyzed primarily based on a visualization of the
network topology.
3.2.3 Path Length
The path length, or geodesic distance, is determined by the average distance between all
pairs of nodes. It shows the network interconnectedness, so how far each actor is from
another actor as a source of information. When a network is highly connected it suggests
that there is a system through which information is likely to reach everyone and in a
relatively short amount of time. A short average path length also increases the chances of
information reaching other actors. The path length found is compared to the path length
that would results if the links between actors had been formed at random as it is size
dependent. If the network is highly locally embedded, the path length is expected to be
significantly lower than the random path length. Path length is also an indication of the
diameter of the network, and a smaller diameter compared to a given number of nodes
can indicate a higher level of embeddedness and performance.
It should be noted however that an extremely short path length may however not always
be a desirable characteristic for a network to posses. This can indicate that the actors in a
social network are highly similar in terms of for example their knowledge bases, which
can reduce the possibility of knowledge diffusion and spillovers (Cowan, 2004). Based
30
on these characteristics, the path length for the LBSP scientist network is expected to be
relatively short, but not extremely short.
3.2.4 Cluster Coefficient
The cluster coefficient measures the degree to which a network can sustain connected
when nodes are removed from the network. This robustness will be high when nodes are
organized into cliques, where everyone knows everyone. When one person drops out of
such a clique, the remaining members will all remain connected. In assessing the degree
of clustering, it is thus useful to look at the direct neighborhood of actors. It can also be
useful to compare the cluster coefficient to the overall density.
Two different measures can be used. The overall graph clustering coefficient is the
average of the densities of the neighboring areas of all actors in the network. The
weighted cluster coefficient on the other hand gives a weight based on the neighborhood
densities proportional to their size. Actors with larger neighborhoods will receive more
weight in calculating the average density. As networks larger in size are generally (but
not always) less dense than smaller networks, the weighted average clustering coefficient
is normally less than the un-weighted cluster coefficient (Hanneman & Riddle, 2005).
If a high degree of clustering is visible in the network it can be an indication that some
groups of individuals are relatively more embedded in the network than others and that
embeddedness occurs very locally in certain neighborhoods. A high level of clustering
may also be an indication of the existence of a certain degree of hierarchy within the
network. A high clustering coefficient thus does not indicate a high level of
embeddedness for the whole cluster but it instead indicates strong local differences
between groups of actors. This measure can hence be related to the core-periphery
structure mentioned earlier.
3.2.5 Small-world Analysis
The small-world characteristics of a network can be measured by comparing the actual
path length and cluster coefficient value to the values of a network of the same size and
31
density if it had been randomly formed. It must be noted that there are 2 different types of
structures of a network that can lead to the small-world properties of a high cluster
coefficient and a low path length. These structures vary considerably and will have a
different degree of centralization. One structure involves a network consisting of several
highly clustered groups of actors which are isolated except for being connected through
single direct paths, which leads to a low level of degree centrality. This is similar to a
hub-and-spoke type of network. The second way is through a network which consists of
more evenly spread out clustered groups which are close to one another and connected
through various actors, thus having a high degree of centralization. The former may have
a stronger form of an ‘achilles heel’, where few paths are crucial in order to keep the
clusters of actors connected and thus keep path length short. For the robustness of the
network the latter is hence the ‘stronger’ type of network. Whether either one type of
structure implies a higher level of embeddedness in the network however is debatable.
Considering that a lower level of centralization as discussed earlier can be related to a
higher level of embeddedness, the ‘achilles heel’ network is the expected small-world
structure for the LBSP scientist network.
3.3 Types of Actors
Once the network of the scientists has been analyzed, the network of the MT members
will be considered based on their importance in high-tech cluster networks as supported
by theory. The two networks will be compared to one another based the measures
mentioned for the scientists, with a focus on the differences between the two types of
networks.
Ideal for comparison would be if the MT network consisted of exactly the same number
of actors as the scientist network. As this was not possible based on limitations of the size
of the MT member data base, the size dependent measures will be taken into account. It is
expected that the MT member network exhibits similar characteristics in the level of
embeddedness and topology, as the scientist network as they are expected to play similar
roles in the micro-dynamics of job mobility network formation.
32
3.4 Central Actors: Bridging and Brokerage
The structure of the combined network is expected to posses the characteristics of a well
developed and well functioning type of network. In this combined network some actors
are expected to perform a bridging function between the 2 types of actor networks.
Actors with the ‘special’ characteristic of being included in both the scientist and MT
member database could play a central role in the overall network.
Whether the manager-scientists do in fact play a bridge-building function can be tested
using the betweenness centrality measure, which is defined as the number of times that
any node needs a given node to reach another node by the shortest path (Freeman, 1979).
Actors in such a broker position must have a high level of betweenness centrality, as the
two types of actors are expected to be indirectly connected to one another through a
broker actor. Tested will be whether the 10 scientist-managers in the dataset on average
have a higher level of betweenness centrality than the rest of the scientists and managers.
Ideally a highly embedded cluster network would have few actors with such an above
average level of betweenness centrality, as it may be better for the stability of the network
to not be dependent upon a few central actors. The betweenness centrality of these
scientist-managers in the network will be considered as opposed to the historical
development of the cluster. As those who have been in the network longer are more likely
to have had multiple jobs and are thus more likely to be central to the network, part of the
historical aspect does influence this measurement by definition as mentioned. Related to
this historical aspect, it is important to note that there may be intervening variables which
influence the level of betweenness centrality. As shown in Diagram 1, the age of an actor
can influence both the measure of betweenness centrality and the chance that an actor is
both a scientist and an MT member.
33
Actor Age
Betweenness
Centrality
Scientist+MT
Member
Diagram 1: Age as intervening variable
The former is because an older actor is more likely to have had a larger number of jobs
within the cluster, and the latter is due to the fact that an older actor is more likely to have
started as a scientists and then to have become an MT member later on during their
career. The fact that these measures are all interrelated can distort the measure as the
highest levels of betweenness may hence be the result of these actors being the oldest
actors in the network as opposed to having a broker position due to their function. As age
is not included in the database however this will not be corrected for so the results will be
biased.
A similar bias may occur based on a possible relation between the level of betweenness
centrality of actors in the network and the number of patents (or publications) that an
actor has. An actor with a high number of patents is more likely to have worked in the
cluster for a long time and with many other firms and scientists. This is reinforced by the
fact that multiple patents included in the dataset were joint projects of two or more firms.
The number of patents is again also directly related to the age of an actor and the
possibility of being a scientists-MT member, as older actors are more likely to have had
an extensive scientific career during which they have patented inventions, which is a time
consuming process in the bio tech industry. While the number of patents is available, it
has been decided that it is not in line with the qualitative nature of this analysis to go into
a quantitative analysis of the influence that the number of patents has on the level of
betweenness centrality of an actor.
34
4.
Data Collection
The data necessary for the analysis of the research questions consist of a scientist data
base and an MT member data base. First of all the generally applicable data collection
processes will be explained, after which the specific data sets for the scientists and MT
members will be considered. The data collection has been based on a list of companies as
provided by the LBSP management, which includes all companies that were located at
the park between 1985 and 2009. Several companies included in the list have ceased
business, have moved locations or have either merged or been acquired, which has been
consistently dealt with in the data sets. The most recent form of each firm within the park
has been used as variable. It is not considered to be a problem that based on this method
an employee of an acquired firm is considered to have worked for the acquiring firm, due
to the likelihood that employees are transferred to the new firm. Such merged and
acquired firms also turned out to be uncommon in the data set.
The University of Leiden and LUMC in some research may be considered as one firm,
but due to the size of both the university and the LUMC it is not realistic to assume that
employees from various departments are in contact with one another. Instead the choice
has been made to separate the departments as much as possible in order to avoid any bias,
including the separate institutes related to the University of Leiden. This resulted in a list
of 128 firms, institutes and departments.
In the process of building the two datasets of the job histories of the scientists and the MT
members, the number of jobs that an actor had within the cluster and the order in which
the actors had these jobs was crucial to determine. First it was important to determine
what a ‘job hop’ constitutes, and how ties are formed. Ties between individuals in the
dataset are created through employment, publication or patenting with a common firm.
Under this rule of tie-formation, ties linking individuals are only formed through job
mobility. As an employee switches jobs he or she is expected to stay in contact with
former colleagues while forming new ties at the new firm. Co-patenting and copublication are included as job hops because there has likely been a high level of
35
interaction between such actors over an extended period of time, similar to if the actors
had been colleagues.
The order in which actors had the jobs was important to determine because the most
current jobs of actors had to be excluded to avoid bias towards current networks as is
applied in the method by Casper & Murray (2005). This is based on an argument by
Newman & Park (2003) that “within affiliation networks coefficient correlations are
biased upwards as groups of individuals are selected into the network on the basis of a
current joint affiliation, such as working for the same company” (Casper & Murray,
2005). By including only prior functions and by excluding any links created based on
current functions, this problem is circumvented.
For an actor to be included in the network they must thus have one visible ‘job hop’ next
to their most recent function within the park which adds up to 3 cluster jobs. If an actor
did not have at least 3 jobs within the cluster throughout their career they were excluded
from the dataset. Ties are assumed to last indefinitely. There is some evidence that a 5 to
10 year time-frame is more realistic (Casper, 2007; Uzzi & Spiro, 2005), though this is
debatable as colleagues can remain tied (friends) for life. Especially in the modern era of
social networking websites it has become easier to stay or get into contact with former
colleagues, so a decay-factor will not be included.
In case an actor worked for 2 companies at the same time, the job first started counted as
the oldest held job of the 2. The order of jobs was inserted based on 1 being the current
job, 2 being the previous job and so forth. In case an employee no longer worked for at
the LBSP in 2009 they were still included in the dataset. Both of the datasets built are
assumed to be incomplete in terms of including all scientists and MT members and thus
consist of a large sample as opposed to full networks. The MT member network however
is assumed to be reasonably complete as the database as provided initially was
constructed using a complete list of firms in the LBSP as provided by an employee of the
LBSP itself. The scientist network on the other hand was based on patent data, which
creates a bias toward pro-patent firms, research intensive firms and ‘star’ scientists. This
36
is not considered to be a problem however as the scientist samples are intended to include
the most important scientists only and are designed so as to contain a minimal level of
bias in other areas, as will be explained in the specific data collection sections.
After the deletion of the most recent jobs of the actors, a 2-mode matrix was built of the
individuals versus the firms using 0 to indicate no prior employment and 1 to indicate
prior employment. This matrix was converted into a 1-mode matrix of individual versus
individual using Ucinet, where 1 means that the actors have worked at a common firm.
All ties are assumed to be reciprocal. The network visualizations have been made using
the 1-mode networks in Netdraw and the measures have been calculated using Ucinet.
4.1 Scientist Network
The scientist database was based on a patent data base as retrieved from the European
Patent Office. The OECD ‘regionalized’ this data by dividing it into the region of
inventors and applicants. From the rough dataset the firms within the ‘Leiden &
Bollenstreek’ area were selected (code NL331) and the companies within the park were
handpicked from this list. This resulted in a list of 634 scientists with patents at 20
different firms. The patent data is an appropriate method to identify the scientists most
relevant for the formation and functioning of the cluster network as ‘star’ scientists are
expected have the most influence on cluster performance and success.
To check the completeness of this data, it was checked by hand whether the other firms
on the list of firms at the LBSP applied for any patents. Some had done so through
foreign offices. The patents were only included if a Dutch inventor was involved as it can
be assumed that some of the knowledge will have come from the Dutch firm to which the
inventor is related. This provided a partial job history of these scientists.
Next the double entries were eliminated and the scientists were sorted based on the
number of total patents. This was done because scientists with the highest number of
patents are expected to be the star-scientists who play the most prominent roles in the
37
innovation and success of the cluster. In case scientists had the same number of patents
they were sorted in alphabetical order based on their last name.
This database was added to and double checked by searching for the job histories of each
scientist using all possible online sources and databases. This included networking
websites such as LinkedIn, company websites, publishing databases, patent databases,
personal websites, university websites, and newspaper articles. This was done so as to
obtain the most complete job history record possible and to avoid any bias towards for
example jobs with pro-patenting firms or the largest firms. Next to these sources a
snowball sampling method was also used to randomly double check who scientists had
worked together with by for example going through the list of patent applicants listed
with a patent and co-publishing authors.
The job histories of scientists were checked in descending order of the number of patents
until a total of 71 scientists had been identified with a minimum of 3 jobs within the
LBSP. This was done in order to have the same number of actors as the Cambridge
cluster so as to be able to optimally compare these two cluster networks. It turned out that
71 out of a 124 scientists researched had a minimum of 3 verifiable functions with a firm
in the LSBP. As the most recent functions of the scientists were excluded, it appeared
that many new spin-off companies were not included in the final data base, which is a
possible source of bias towards older firms and employees.
Another criticism is that some jobs could have been missed in this process as there might
not be any online evidence connecting scientists to a firm. The last criticism is that the
patent data does not lead to a representative sample of scientists from the cluster, but
rather displays the cream of the crop when it comes to scientists. The resulting network
therefore may not be representative of the cluster network of all scientists or employees.
This implicates that caution must be taken when generalizing the results. Overall
however it is believed that the final data base gives a relatively unique and complete
picture of the job mobility of the most important scientists within a high-tech cluster.
38
4.2 Management Team Member Network
In order to build a data base of the job histories of the MT members in the LBSP a readymade data base was used as basis, which was provided by the LBSP foundation. This
included a list of 163 MT members working within the park in 2009 and only included
firms in their job histories that were located at the LBSP at that moment. MT members
are defined as senior managers, chief executives, chief scientific officers, chief financial
officers, vice presidents or any other senior employees listed as senior management by
the firm. In order to check the completeness of the MT member list and the job histories
as provided all possible online resources were used. Often company websites gave
complete overviews of the current higher management, but previous functions were more
difficult to identify. Each MT member was thoroughly researched by hand, which lead to
many additions to the original data set. A snowball sampling method was again also
applied to double-check the completeness of the database. This resulted in a list of 49 MT
members out of 163 who could be related to a minimum of 3 firms within the LBSP
cluster. It is a limitation that the original database was limited in size, as it would have
improved the possibility of comparison if the MT member network had consisted of 71
actors like the scientists network.
39
5.
Results and Analysis
The results will now be presented along the lines of the four main research questions and
in the same order as the methodology sections. The first results will thus be of the level of
embeddedness of the scientist network, second will be the topology of the scientist
network, third the comparison of the scientist and MT member networks and lastly the
central actors in the combined network.
5.1 Level of Embeddedness of the LBSP Scientist Network
Based on the visualization of the scientist network as shown in Diagram 2, the LBSP job
mobility network appears to be extensive and well developed. The scientists are highly
mobile between firms at the park, especially when compared to the Cambridge and
Munich cluster networks which are shown in Diagram 3 and 4. The LBSP network
displays a far higher and more evenly spread level of interconnectedness and thus of
embeddedness.
40
Diagram 2: Visualization LBSP scientists (71 actors)
Diagram 3: Visualization Cambridge scientists (71 actors) (Source: Casper & Murray (2005)
“Cambridge career affiliation network” pp. 63)
41
Diagram 4: Visualisation Munich (82 actors) (Source: Casper & Murray (2005) “Munich career
affiliation network”, pp. 64)
In order to further asses the level of embeddedness the number of actors connected to the
main component and the density of the network were calculated. Of the 71 scientists in
the LBSP network, 67 are connected to the main component as shown in Table 1. This is
94.4% of all actors, which is a very high number and almost identical to the 95% as
found by Casper (2007) in the San Diego bio science cluster. This level is higher than the
73% of Cambridge and the 64% found for the Munich cluster. The density of the LBSP
scientist network as shown in Table 2 was 0.194, which indicates that 19.4% of all
possible ties are formed within the network. This is therefore neither a very dense nor a
very sparse network. In comparison to a network of the exact same size such as
Cambridge however the LBSP does have a higher level of density, as the Cambridge
network has a sparse level of interconnectedness with only 6.8% of all possible ties
formed. The extremely high percentage of actors connected to the main component and
the relatively higher level of density indicate that the LBSP is strongly embedded in a
cluster network.
Important to note is that in the scientist network, 32 of the total of 128 firms, departments
and institutions in the park are included in the final data base which excludes current jobs
of scientists. There is thus a strong bias towards certain departments, which are mainly
associated with the Leiden University and the LUMC as predicted based on the fact that
most scientists start their careers with the university.
Table 1: Main component connectedness
Total number not
in main comp
Number of
isolates
Largest (% of
total)
Cambridge
17
13
52 (73%)
Munich
21
13
56 (64%)
LBSP Sc.
4
4
67 (94.4%)
42
Table 2: Overall Network Density
Network Density
Cambridge
0.068
Munich
0.068
LBSP Sc
0.194
5.2 Topology of the LBSP Scientist Network
Based on the visualization in Diagram 2, the LBSP scientist network does not appear to
have a small or strong core of actors. Instead it appears to have a very large core which is
not focused around a few central actors but is relatively evenly spread out. The periphery
is hence very small or non-existent and there is no clear back-bone visible. This is very
different from the networks in Cambridge and Munich, which consist of small central
cores that are strongly interconnected and are surrounded by a loosely connected
periphery.
The visual image of the network is supported by various network measures. The level of
degree centrality found as presented in Table 3 is 37.7%, which coincides with the
generally central structure of the large core. The level of centralization is higher than in
Cambridge (20%) but not very high overall. There thus are some actors present in the
network who are more central than others and who have relatively more ties, but the
variance in the number of ties per actor remains relatively low. This indicates that there
may be a stronger hierarchic structure present in the LBSP cluster compared to the
Cambridge cluster, but that the overall power within the LBSP network is more evenly
spread out over the actors.
The average path length of the scientist network shows that the network is not randomly
generated but that it is likely to be influences by social dynamics. An actor must pass
through 2.08 actors on average to reach any other actor, compared to 2.312 for the
randomly generated network. This is not a large difference, but when comparing to
Cambridge with a path length of 3.701 it indicates a relatively efficient network for the
transfer of information. The diameter of the network is hence also smaller than the
43
Cambridge and Munich networks which is positive for network performance. The
network would be more efficient if it had a lower path length, but this is perhaps not
realistic as there will always be some actors located in less central positions based on
fewer connections due to for example a difference in age. A very short path length as
mentioned may in fact not even always be a desirable characteristic for a network to
posses.
Next to the network being efficient, the cluster coefficient suggests that the LBSP
network is also highly robust. The cluster coefficient is 0.824 compared to 0.126 for a
randomly generated network, as presented in Table 4. Cambridge and Munich showed
similar high levels of ‘cliqueness’. This indicates that some groups of actors may be more
embedded in the network than other groups. As the LBSP however has a higher level of
density than Cambridge (0.194 > 0.068) while having a similar cluster coefficient, the
LBSP can be said to be more evenly distributed.
That the LBSP network appears to be a well developed and well functioning network is
supported by the evidence that the network possesses small world characteristics. There
was both a high cluster coefficient and a relatively low path length. Other social networks
that have been labeled ‘small world’ found path lengths above their random path length,
similar to Cambridge, averaging between 2.5 and 3.5 (Kogut & Walker, 2001).
Considering the structure of the small world network, the LBSP does not appear to have
the structure of an ‘achilles heel’ as mentioned nor the strongest type of network, based
on a medium level of network centralization. It rather seems to hold the middle ground
between these 2 small-world structures. The existence of small world properties in a
medium dense network as opposed to a dense network again suggests that the formation
of the network is not a random process but that there are social networks driving the
characteristics of the network structure (Casper, 2007).
44
Table 3: Degree of centrality / network centralization
Mean node centrality S.D. Network centralization
Cambridge
4
4.6
21.3%
Munich
3
7.0
19.8%
LBSP Sc.
13.57
9.2
37.7%
Table 4: Path Length
Actual network Random comparison network of same size and density
Path length
Path length
Cambridge
3.701
2.312
Munich
3.578
2.085
LBSP Sc.
2.080
2.312
Table 5: Cluster Coefficient
Actual network
Random comparison network
of same size and density
Cluster Coefficient
Cluster Coefficient
Cambridge
0.855
0.126
Munich
0.835
0.148
LBSP Sc.
0.824
0.126
Table 6: Small World Analysis
CC
Path Length
Random CC
Random
Path
Length
LBSP Sc.
0.824 2.080
0.126
2.312
LBSP MT
0.650 2.561
0.148
2.085
LBSP Sc+MT
0.835 2.259
0.126
2.312
45
5.3 Type of Actor Networks
There are clear differences visible between the scientist and MT member networks,
though the MT member network does contain some of the properties of a wellfunctioning and well-developed network. While it is difficult to compare the 2 based on a
difference in the number of actors in the networks, the MT member network appears to
be more similar to the networks found in Cambridge and Munich than to the scientist
network. Comparatively few actors are connected to the main component (53.5%
<94.4%). This is similar however to the proposed industry average of 50% as suggested
by Owen Smith & Powell (2004). There are 13 actors who are isolated from the main
component but who are connected in pairs and in a small group. This can be driven by the
low number of interconnectedness in general as MT members may not be as mobile as
scientists, but it can also be driven by the low number of firms included in the dataset due
to the low number of actors (49<71). The relatively low number of actors decreases the
chance that actors are connected to the main component through common employment.
The network is sparse with 6.3% of all possible ties actually formed compared the 19.4%
for the scientist network. Considering that larger networks by definition become sparser,
the MT member network is relatively even sparser that the scientists network. This all
indicates that the MT member network is relatively less embedded than the scientist
network.
The MT member network is not guided by a few central actors compared to the scientist
network, with a level of centralization of 17.9% compared to 37.7% for the scientists.
The core of the network is smaller compared to the total number of actors in the network,
but also appears to be evenly spread out. A small periphery of about 7 actors who are
only connected to the core through 2 or 3 ties is visible. There appear to be 3 small cores
that are connected through only a few actors. This structure is reminiscent of the ‘achilles
heel’ structure as mentioned in the small-world network structures. The path length of
2.561 and cluster coefficient of 0.065 compared to a random path length of 3.062 and
cluster coefficient of 0.016 do indicate that the network possesses small world properties.
46
As explained this is beneficial for network robustness and efficiency and thus for overall
network performance.
The larger number of ties of the scientists suggests that they have a higher level of social
capital compared to the MT members. While the MT member is also robust and efficient,
it is not as extensive as the scientist network. The networks are difficult to compare
however based on a difference in the number of actors. It would strengthen the analysis if
the MT network size could be increased. Another influential factor may the fact that a
larger sample of firms is included in the MT member data base, as 42 out of 128 firms are
included in the final data set compared to 32 out of 128 for the scientist data base, despite
the larger number of actors in the scientist data base. This leads to a more spread out and
less biased selection of firms for the MT members, which can lead to lower clustering
and density values for example. The comparison and the characteristics of the MT
network overall however do still offer support for the inclusion of MT members in the
network analysis as there is evidence that there is a considerable degree of job mobility
and proof of social network dynamics.
Table 7: MT Member Network Characteristics
Network Measure
Main Component Connectedness
26/49 (53.5%)
Density
0.063
Network Centralization
17.9%
Path Length (random)
2.561 (3.062)
Cluster Coefficient (random)
0.065 (0.016)
47
Diagram 5: Visualization LBSP MT (49 actors)
5.4 Central Actors in Combined Network
When combining the network of the scientists with the MT members it resulted in a
network that appears highly similar to the scientist network. The network seems to be
well developed and strongly embedded, with 83.6% connected to the main component
and a density of 0.117. The actors who are both scientist and manager have been
highlighted to display their position in the network. Some clearly occupy central
positions as predicted while others have relatively few connections and are located on the
periphery of the cluster as opposed to connecting groups.
The average betweenness centrality of the 10 scientist-managers supports this observation
as displayed in Annex 1, as it is 60.8 compared to an average of 48 for the other 100
scientists and managers. Compared to the maximum betweenness centrality that was
found of 574.5, this is not a high average, which therefore offers limited support for the
notion that scientist-managers perform a brokerage function in the combined network.
The average of 60.8 is pulled up by the few scientist-managers who do occupy central
positions, as there is a high level of variance present in the betweenness centrality of the
48
10 scientist-managers. For the top 10 actors in the combined network the average
betweenness centrality is 334, and only 1 scientists-manager is included, as visible in
Annex 2. This can be translated into the fact that 70% of the top 10 broker positions are
occupied by scientists compared to 61% if the actors had been randomly picked. Based
on this evidence scientists thus seem to occupy the most central positions in the network,
though this evidence is not convincing. These results could have been influenced by the
fact that scientists have more connections based on the larger data set, but might also be
influenced by other variables such as the age of an actor. Whether the scientists in the
database however are older than the MT members is beyond the scope of this research.
Diagram 6: Visualization combined Scientist and Management network (110 actors)
Table 8: Betweenness Centrality
Mean S.D.
Max
LBSP Sc + MT (100) 48.0
105.9
574.5
SC & MT (10)
83.4
198.4
60.8
49
6.
Conclusions
6.1 Level of Embeddedness
There is strong support for the notion that the LBSP as successful cluster is highly
embedded in a cluster network, as expected. The network possesses efficient and robust
properties wherefore information can easily and quickly reach all scientists in the
network. Especially compared to other clusters the LBSP has favorable network
properties. There also appears to be evidence that networks formation is guided by social
network dynamics based on the non-random formation of the job mobility network of
scientists. Underlying reasons are that the boundaries of the network are determined by a
select group of firms, which in turn determine connectedness between individuals, which
creates patterns in the structure and topology of the network.
The level of job mobility of the scientists overall was surprisingly high. It turned out to
be much higher than for the MT members and previously analyzed clusters. This high
level of job mobility indicates a high level of creation of social capital, which is
beneficial for cluster success. It also indicates that the LBSP is likely to possess
characteristics that fuel job mobility, which could include various types of push- and pull
factors such as a tolerance to job mobility, positive institutional factors, the availability of
career opportunities and/or a high degree of cooperation between firms within the LBSP.
Based on the theories and results it appears as though there may be an upward spiraling
mechanism at work, where a high level of job mobility plays a role in the increase in
cluster innovation and success, which in turn may further induce job mobility increases
based on new opportunities. A network thus seems to be self-reinforcing through job
mobility, which again creates positive externalities for the cluster.
The high level of mobility found can also be considered as evidence for the existence of a
high degree of knowledge spill-overs and diffusion. The fact that there is such a high
degree of mobility can also indicate that there is a sufficiently large potential labor pool
within the cluster in case you include current cluster employees in the potential labor pool
as suggested. In case the potential labor pool is defined as employees not currently
50
employed by a firm in the LBSP however, than it could imply that there is perhaps even a
relative scarcity of suitable outside employees from which LBSP firms can recruit, as was
suggested by Annelies Hoenderkamp. The LBSP firms may rely mostly on current park
employees to fill vacancies and start new ventures, which would lead to a high level of
local mobility, as found. This can be connected to the existence of a ‘recycling’
mechanism of employees as suggested by Casper (2007) and Saxenian (1994). Whether
such a shortage of scientists is truly the case ought to be further investigated by analyzing
the degree to which new functions within the LBSP are filled by employees with previous
jobs at the LBSP.
6.2 Topology of a Successful High-tech Cluster
The characteristics of the topology of the network of the LBSP were mostly as predicted
by theory, and indicate that the LBSP network has beneficial properties for network
performance. There is some hierarchy present in the network, though the power is
relatively evenly distributed. The presence of hierarchy is not as ideal for the transfer of
information as a non-hierarchic network, but is inevitable based on the individual
differences in the extent of scientist careers. The fact that the hierarchy is comparatively
average whilst simultaneously evenly distributed is therefore considered to be a positive
characteristic of the LBSP network.
The large evenly distributed core and small periphery of scientists also support the high
level of embeddedness and relative low degree of hierarchy in the network, which is
positive for network performance and hence cluster performance. This structure
optimizes the role of individuals in the spread of knowledge which positively impacts the
spread of innovation in the cluster. In comparison to the Cambridge and Munich clusters,
the scientist network displayed very different properties. This can mean that scientists in
the LBSP are embedded to a higher degree but may also be related to a difference in the
data collection or completeness of the data for Munich and Cambridge or it may be
driven by a difference in institutional factors for example.
51
No evidence was found to support the theory of development of a cluster as proposed by
Casper (2007). This could indicate that the San Diego case, where the management of a
failed company formed the basis of a successful bio science cluster network, was a rare
event. It is more likely to be the case that such a backbone is present, but is not visible
due to the strong development of the network around the core actors over time. A
historical development study of the LBSP scientist network could clarify whether the
latter is the case. The scientist network further displays ideal properties for network
performance as small-world characteristics are present and the power distribution
between groups within the cluster is not highly skewed.
6.3 Type of Actors in Cluster Networks
There appeared to be sufficient theoretical evidence that the type of actors that can
influence cluster performance through social networks include both scientists and MT
members. The networks of these two types of actors however vary more than expected.
Interestingly the MT member network is actually more similar to the Cambridge and
Munich networks than to the scientist network. The fact that the MT network
characteristics differ from the scientist network can indicate that a difference in function
can influence the level and dynamics of mobility. Are managers really less mobile and
less embedded in a cluster network than scientists? In order to extend the support for this
claim further research is necessary.
The difference in the two networks also stems from the difference in the number of actors
and possibly from a difference in the number of firms included in the final data set. The
fact that the MT member data set includes a larger sample of firms than the scientist data
set means that it is less biased, and could be an important reason why the network is not
as highly interconnected as the scientist network.
The difference in the networks found however can also be considered as support for the
inclusion of MT members in overall cluster network analysis, as perhaps they are in fact
part of a different type of epistemic community and have different social networks than
scientists do. MT members undoubtedly play an important role in the growth and
52
performance of clusters. Regardless of the reasons behind their lower levels of mobility,
there is theoretical and empirical evidence that supports the importance of management
functions in the job mobility network dynamics of a cluster. Just as the scientist network
the formation of the MT member network is non-random. The MT network is both robust
and efficient and hence possesses several of the properties mentioned in theory related to
an ‘ideal’ network. This is all evidence that social networks drive the formation of the
network, which provides the means for social capital to be created which positively
influences cluster success.
The theoretical and empirical support for the importance of senior management members
in cluster network analysis has important implications for future cluster network analysis.
By only including scientists in the analysis of cluster networks, an important group of
actors is left out who have a significant amount of influence on the network itself, the
functioning thereof and the overall performance of the firms in a cluster. By only
considering the senior management networks, the scientists are left out, while they
undoubtedly play an important role in cluster success. The interaction between, and the
integration of these two types of actors is vital for cluster success by combining scientific
and market knowledge. The scientists are dependent upon the MT members and vice
versa. Their networks therefore ought to be combined in order to provide an accurate
picture of the social dynamics within a cluster which influence cluster success.
6.4 Central Roles in High-tech Cluster Networks
The combined network displayed well-developed characteristics as predicted, and the MT
network and scientist network appear to be integrated. While the expectation was that
scientist-managers occupy central positions in the combined network, evidence to support
this claim was relatively weak. It appeared that percentage wise more scientists
performed bridge-building functions instead, though not to a convincing degree. Theory
and empirical evidence do offer support that central actors can perform important
functions as brokers in a network. They contribute to the small-world characteristics of
the network, keeping it robust and efficient in function. It however remains unclear
whether such a central role is related to the function of an actor or whether it can be
53
attributed to personal characteristics. It is possible that a founder of new firms in the
cluster is a reason why an actor is a central broker in a network, as opposed to the
function of scientist- manager leading to such a central position. The causality is unclear,
and the many limitations of this analysis cloud the interpretation of these results,
wherefore further analysis is recommended.
The overall implications of the results of the analysis are that while the job mobility
networks of scientists and MT members are well developed, it may be worth further
developing through stimulating mobility. This study provides evidence that policy
makers can potentially influence cluster performance and success through influencing job
mobility networks. Based on these results the LBSP should definitely implement their
plans to create and facilitate a labor pool and could for example further support the
collaboration between scientists of different firms in joint projects. It is important that the
policy measures do not solely focus on scientist-mobility but that they also take higher
management team member mobility into consideration based on the importance of the
commercialization of science and entrepreneurial new ventures. On a national level the
LBSP provides a superb example of a well developed high-tech cluster network, which
adds to the existing evidence that a well developed network tends to be part of the
formula for a successful high tech cluster. The network dynamics in a cluster may be an
important factor in explaining the variation in success of high-tech clusters.
The job mobility network characteristics as identified in the LBSP case may partially be
generalized to apply to other high-tech clusters. As mentioned however, it is important to
realize that there are possible institutional forces at play and that the scientist network
includes only star-scientists and not a random sample of employees.
54
7.
Limitations
Various limitations in the methodology and data collection impact the measures and
interpretation of the results, some of which have been mentioned earlier. An important
concern which could be improved upon is the fact that the MT member network consists
of fewer actors than the scientist network, which influenced the network characteristics
and hindered comparison. Another possible driver behind the differences between the
scientists network, the MT network, the Cambridge network and the Munich network is
that the scientist network also included co-patenting and co-publishing activities as
shared job experiences even in case the patent was issued under only one of the firm
names for which a scientist may not have worked for on a daily basis. This is justified for
the scientist network analysis by the fact that co-publishing and co-patenting does
increase the chances that employees have worked closely together and have formed
durable ties, but could lead to an artificial intensification of network connectedness and
hence could lead to difficulties in directly comparing the networks. This is because MT
members are generally not involved in co-patenting and co-publishing activities.
Another concern is that it is highly likely that a few large firms influence the
characteristics of the network. Not nearly all the firms present in the LBSP are
represented. It would be highly interesting for future research to construct a scientist
database based on the database of firms within the LBSP as opposed to using a patent
database. Ideally all scientists of the cluster should be included, or a random sample
should be selected. This would reduce the bias towards pro-patent and larger firms and
toward the star scientists. While this is extremely labor intensive, this would decrease the
level of bias in the scientist network to an unparalleled degree.
Excluding all current positions of actors is also likely to have had a profound impact on
the structural characteristics of network as it excluded many positions in new ventures
and younger firms for example. It thus created bias towards older firms and actors,
specifically towards the university. Perhaps the reason for excluding the current positions
can be reconsidered or worked around. Another issue in the overall methodology is that
55
no decay of ties was included. It may specifically have impacted the scientist network if
a decay of ties were to be included, as the MT member job histories in general were more
recent than the scientist career histories. It would also have decreased the bias towards
university-based ties, as these tend to be the oldest ties in the data base. This is based on
the fact that more scientists than MT members started their career at the LBSP with the
university and had longer career spans within the cluster in general.
As mentioned another limitation involves the reasons why some actors are more central
in the network than others. A central position is linked to both the level of
interconnectedness and the relative position of an actor in a network, which may be
related to other factors such as actor age, the number of patents an actor has or other
personal characteristics as opposed to the type of function. A higher age is not only
related to a higher chance of being both a scientist and a manager, but also means a
higher chance of more job hops. As it remains unclear what factors exactly influence the
central position of an actor in a network, it could be interesting to investigate exactly
what type of people perform these bridging functions in the network. This could for
example be analyzed by for example including variables such as the exact function
description, actor age, the number of patents, the number of working years, the level of
education (PHD) and the size of the firms for which the actor has worked. Based on the
list of limitations and suggestions for further research one can conclude that there is room
for improvement and further research in this area in general.
The theoretical section also raised an interesting question of whether scientists are locally
or globally mobile. By investigating the mobility of scientists within and outside of the
LBSP more insight can be given as to the motivational factors in the determination of
mobility of employees, which can be interesting for policy makers.
Next to methodological limitations a theoretical limitation should also be mentioned.
There is an interesting study by Taylor (2005) which questions the validness of the theory
of embedded local economic growth in general. Taylor suggests that the model is
overdrawn in that it does not sufficiently incorporate “the imperatives of capitalism, the
56
impact of unequal power relations and the exigencies of time”. He suggests that it is
overdrawn in that it ‘fetishes proximity, promotes the chaotic concept of ‘institutional
thickness’ and labours under the limitations of the equally chaotic concept of ‘social
capital’” (Taylor, 2005). These limitations undermine the entire theoretical foundation of
this study, yet have not found general support from the wider scientific community. They
are thus interesting to take into consideration in future studies but do not provide enough
theoretical and empirical leverage to impede future research in this area.
57
Acknowledgements
I would like to thank various people without whom this study would not have been
possible. I would like to thank my supervisor Sandra Phlippen for sparking my interest in
the topic of clusters during the seminar ‘Governance, Clusters and Networks’, for
organizing a group of students who could write their thesis on this topic, for setting up
the project with the Leiden Bio Science Park and for her guidance and critical comments
throughout the process. I especially want to thank her for continuing to supervise me
while I was in Australia. I want to thank Harmen Jousma and his colleagues at the
Leiden Bio Science Park for their practical and theoretical insights and for providing the
data bases of the firms and management team members. Lastly I want to congratulate and
thank my fellow group members for their contributions and would like to wish them the
best of luck for the years to come.
58
Appendix
Annex 1: Betweenness Centrality Scientist-MT members
Actor Name
Betweenness
Bout, Abraham
169.217
De Haan, Peter
0.000
Ijzerman, Ad
169.876
Melief, Kees
0.000
Platenburg, Gerard
23.430
Spaink, Herman Pieter
46.939
Stegmann, Antonius, Johannes,
0.000
Hendrikus
Strijker, Rein
0.000
van Deutekom, Judith Christina
0.000
Theodora
van Wezel,Gilles
198.449
60.791
Mean
Annex 2: Betweenness Centrality Top-10 Actors Combined Network
Actor Name
Betweenness
Type of Actor
Drijfhout, Jan, Wouter
574.474 Scientist
Heyneker, Herbert, L.
450.313 Scientist
Hooykaas, Paul Jan Jacob
426.468 Scientist
Van Boom, Jocobus Hubertus
405.757 Scientist
Valerio, Domenico
346.195 Scientist
Hennink, Wilhelmus Everhardus
296.768 Scientist
Yallop,Christopher
264.000 MT member
Stolpe,Onno
202.585 MT member
van Wezel,Gilles
198.449 Both
Schilperoort, Robbert Adriaan
176.696 Scientist
Mean
334.171
59
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