1. Master’s Thesis
M.Sc. in Finance and Strategic
Management
Dept. of Finance
Copenhagen Business School
2007
Advisor:
Henrik Sornn-Friese
Associate Professor
Dept. of Industrial Economics
and Strategy
Author:
Søren Korsbæk
231080-1863
Henrik Madsen
090480-2743
January 17th, 2007
The Mechatronics Cluster in Southern
Jutland
- An application of social network analysis and
cluster theory
TheMechatronicsClusterinSouthernJutland-Anapplicationofsocialnetworkanalysisandclustertheory

2. Abstract
The objective of this paper is to identify the strategic
challenges and opportunities within the mechatronics cluster.
This allows us to develop practical and deliverable cluster
initiatives. Our emphasis differs from much of the previous
work on clusters as we combine the theoretical insights from
cluster theory with the methodology from social network
analysis. Thereby we construct an operational typology of
clusters where we emphasize how relations create benefits for
cluster members. Based on this we carry out a firm-level
analysis of the cluster in which we identify structural patterns
and subgroups within the cluster based on actors’ attributes.

3. Acknowledgements
To begin with, we would like to express our gratitude to Jørgen Mads Clausen,
Hans Martens and Hans Henrik Fischer for initially allowing us ‘inside’ the
mechatronics cluster. In addition, through personal interviews and correspondence;
Hans Martens, Søren Sloth Møller, Hans Henrik Fischer and Sune Søndergaard
have been very helpful by allowing us access to prior studies, and in discussing the
current state of the cluster and their vision for it.
This thesis must also be attributed to the many participants in our empirical study,
who have given us invaluable insights into the complexity and dynamics of the
mechatronics cluster (in alphabetical order); Clas Nysted Andersen (Managing
Director of Nielsen & Nielsen Holding), Steen Askholm (Head of Finance at PCS-
gruppen), Bo Balstrup (CEO of Center for Software og Innovation), Erling Bjærre
(Head of Development at OJ Electronics), Hans Peter Boisen (Director Business
Development at Danfoss Drives), Jan Christensen (Coordinator of Health Care
Innovation), Søren Eckhoff (Project manager at COWI), Christen Espersen
(President of Minibooster Hydraulics), Michael Hansen (Owner and manager of
IO-Connect), Egon Borgkvist Jensen (Vice President & COO of Linak), Poul
Jessen (CEO of PAJ Systemteknik), Ulrik Jensen (Investment manager at
Syddansk Venture), Frank Kirchheiner (Owner and manager of Kirchheiner El-
Teknik, Tønder), Lars Bo Kjøng-Rasmussen (Head of Development at Focon
Electronic Systems), John Klindt (CEO of Micotron), Brian Kristiansen (CEO of
Lodam Automation), Erik Krogh-Rasmussen (Managing director of Danlamp), Per
Lachenmeier (CEO of Lachenmeier), Hans Martens (CEO of Center for
Erhvervsudvikling, Sønderborg), Bjørn Peters (CEO of SKAKO), Amy Petersen
(P.A. to the management at Danimex Communication), Henrik Petersen (CEO of
Alsmatik), Peter Petersen (CEO of Delfi Electronics), Karsten Ries (Project
manager and co-owner of Powerlynx), Jesper Schack (CEO of Telebilling), Claus
Schmidt (Project manager at UdviklingsRåd Sønderjylland), Henning Schmidt-
Petersen (CEO of Servodan), Kristian Strand (CEO of Lodam Electronics),
Michael Lundgaard Thomsen (CEO of Siemens Flow Instruments), Flemming
Toft (CEO of Scientific-Atlanta Denmark), Søren Vilby (CEO of Senmatic), Hans
Ørum (CEO of Høier og Vendelbo).
Finally, we wish to express our gratitude to our advisor Henrik Sornn-Friese for
his guidance throughout the process of writing the thesis. We thank him for the
many helpful references and suggestions on the direction and focus of the thesis;
for his meticulous review of our work; and for the many rewarding discussions.

4. Table of contents
1 Introduction.......................................................................................................1
1.1 Motivation........................................................................................................................1
1.2 Problem statement............................................................................................................2
1.3 Delimitation .....................................................................................................................3
1.4 Methodology....................................................................................................................5
1.4.1 Nature of the study...................................................................................................5
1.4.2 Structure...................................................................................................................8
2 Theoretical framework.....................................................................................9
2.1 Introduction to clusters and how they work.....................................................................9
2.1.1 Concentration of interdependent firms ..................................................................10
2.1.2 Institutions .............................................................................................................12
2.1.3 Relations and benefits from industrial clustering ..................................................12
2.2 Cluster typology.............................................................................................................20
2.2.1 Relations ................................................................................................................20
2.2.2 Composition of actors............................................................................................21
2.2.3 Cluster development over time..............................................................................27
2.3 Cluster initiatives ...........................................................................................................30
2.3.1 What is the purpose of CI? ....................................................................................30
2.3.2 How should CI be carried out and who should do it?............................................32
3 Methods of empirical investigation of clusters.............................................33
3.1 Quantitative Techniques ................................................................................................34
3.2 Qualitative Techniques ..................................................................................................35
3.3 Social Network Analysis ...............................................................................................36
3.3.1 SNA as a methodology ..........................................................................................37
3.3.2 The impact of SNA on our empirical investigation...............................................44
4 Introduction to the mechatronics cluster......................................................47
4.1 Prior studies ...................................................................................................................47
4.2 Our study........................................................................................................................49
4.2.1 The focus of our empirical investigation ...............................................................49
4.2.2 The scope of the cluster studied.............................................................................49
4.2.3 Sampling ................................................................................................................52
4.2.4 The empirical data .................................................................................................52
4.2.5 Outline of the empirical analysis ...........................................................................55
5 The importance of the cluster in a larger context........................................56
5.1 Customers ......................................................................................................................56
5.2 Suppliers ........................................................................................................................57
5.3 Competitors....................................................................................................................58
5.4 Cooperation....................................................................................................................59
5.5 Conclusion on the importance of the cluster in a larger context ...................................61

5. 6 The composition of actors in the core of the cluster....................................63
6.1 The cluster as a social network......................................................................................63
6.1.1 Data on organizational ties ....................................................................................63
6.1.2 Data on personal ties..............................................................................................64
6.1.3 The aggregated network of formal and informal ties ............................................66
6.2 Knowledge of organizations ..........................................................................................68
6.3 Technological scope of the cluster.................................................................................71
6.4 Geographical scope of the cluster..................................................................................78
6.5 Cluster growth................................................................................................................81
6.6 The labor pool in the cluster ..........................................................................................84
6.7 Conclusion on the composition of actors in the cluster.................................................85
7 The network positions of cluster members...................................................87
7.1 The strength of strong ties .............................................................................................87
7.2 Contact between organizations ......................................................................................88
7.2.1 The purpose and strength of the organizational ties in the cluster.........................89
7.2.2 Visualization ..........................................................................................................91
7.2.3 Structural properties...............................................................................................92
7.2.4 Interpretation of structural properties based on attribute data ...............................97
7.2.5 Summary of findings ...........................................................................................101
7.3 Contact between individuals........................................................................................103
7.3.1 The distribution and strength of the personal ties in the cluster ..........................103
7.3.2 Visualization ........................................................................................................104
7.3.3 Structural properties.............................................................................................105
7.3.4 Interpretation of structural properties based on attribute data .............................107
7.3.5 Summary of findings ...........................................................................................107
7.4 The aggregate network of formal and informal ties revisited......................................108
8 Cluster initiatives ..........................................................................................111
8.1 SWOT analysis ............................................................................................................111
8.2 Cluster initiatives .........................................................................................................113
9 Retrospect and Prospects .............................................................................119
10 References ..........................................................................................................ii
10.1 Primary sources................................................................................................................ii
10.2 Secondary sources............................................................................................................ii
1 Introduction
1.1 Motivation
The aim of the thesis was chosen together with members of the ‘mechatronics workgroup’. This
is a group of mechatronics firms and support organizations that meet regularly to discuss cluster

6. 0
1 Introduction .........................................................................................................................................................................................................................................1
1.1 Motivation..............................................................................................................................................................................................................................1
1.2 Problem statement .................................................................................................................................................................................................................2
1.3 Delimitation...........................................................................................................................................................................................................................3
1.4 Methodology..........................................................................................................................................................................................................................5
1.4.1 Nature of the study...................................................................................................................................................................................................5
1.4.2 Structure...................................................................................................................................................................................................................8
2 Theoretical framework ........................................................................................................................................................................................................................9
2.1 Introduction to clusters and how they work ..........................................................................................................................................................................9
2.1.1 Concentration of interdependent firms ..................................................................................................................................................................10
2.1.2 Institutions..............................................................................................................................................................................................................12
2.1.3 Relations and benefits from industrial clustering..................................................................................................................................................12
2.2 Cluster typology...................................................................................................................................................................................................................20
2.2.1 Relations ................................................................................................................................................................................................................20
2.2.2 Composition of actors............................................................................................................................................................................................21
2.2.3 Cluster development over time..............................................................................................................................................................................27
2.3 Cluster initiatives.................................................................................................................................................................................................................30
2.3.1 What is the purpose of CI? ....................................................................................................................................................................................30
2.3.2 How should CI be carried out and who should do it? ...........................................................................................................................................32
3 Methods of empirical investigation of clusters .................................................................................................................................................................................33
3.1 Quantitative Techniques......................................................................................................................................................................................................34
3.2 Qualitative Techniques........................................................................................................................................................................................................35
3.3 Social Network Analysis .....................................................................................................................................................................................................36
3.3.1 SNA as a methodology ..........................................................................................................................................................................................37
3.3.2 The impact of SNA on our empirical investigation...............................................................................................................................................44
4 Introduction to the mechatronics cluster ...........................................................................................................................................................................................47
4.1 Prior studies .........................................................................................................................................................................................................................47
4.2 Our study..............................................................................................................................................................................................................................49
4.2.1 The focus of our empirical investigation...............................................................................................................................................................49
4.2.2 The scope of the cluster studied.............................................................................................................................................................................49
4.2.3 Sampling ................................................................................................................................................................................................................52
4.2.4 The empirical data..................................................................................................................................................................................................52
4.2.5 Outline of the empirical analysis ...........................................................................................................................................................................55
5 The importance of the cluster in a larger context..............................................................................................................................................................................56
5.1 Customers ............................................................................................................................................................................................................................56
5.2 Suppliers ..............................................................................................................................................................................................................................57
5.3 Competitors..........................................................................................................................................................................................................................58
5.4 Cooperation..........................................................................................................................................................................................................................59
5.5 Conclusion on the importance of the cluster in a larger context.........................................................................................................................................61
6 The composition of actors in the core of the cluster.........................................................................................................................................................................63
6.1 The cluster as a social network............................................................................................................................................................................................63
6.1.1 Data on organizational ties ....................................................................................................................................................................................63
6.1.2 Data on personal ties..............................................................................................................................................................................................64
6.1.3 The aggregated network of formal and informal ties ............................................................................................................................................66
6.2 Knowledge of organizations................................................................................................................................................................................................68
6.3 Technological scope of the cluster ......................................................................................................................................................................................71
6.4 Geographical scope of the cluster........................................................................................................................................................................................78
6.5 Cluster growth .....................................................................................................................................................................................................................81
6.6 The labor pool in the cluster................................................................................................................................................................................................84
6.7 Conclusion on the composition of actors in the cluster.......................................................................................................................................................85
7 The network positions of cluster members .......................................................................................................................................................................................87
7.1 The strength of strong ties ...................................................................................................................................................................................................87
7.2 Contact between organizations............................................................................................................................................................................................88
7.2.1 The purpose and strength of the organizational ties in the cluster ........................................................................................................................89
7.2.2 Visualization ..........................................................................................................................................................................................................91
7.2.3 Structural properties...............................................................................................................................................................................................92
7.2.4 Interpretation of structural properties based on attribute data...............................................................................................................................97
7.2.5 Summary of findings ...........................................................................................................................................................................................101
7.3 Contact between individuals..............................................................................................................................................................................................103
7.3.1 The distribution and strength of the personal ties in the cluster..........................................................................................................................103
7.3.2 Visualization ........................................................................................................................................................................................................104
7.3.3 Structural properties.............................................................................................................................................................................................105
7.3.4 Interpretation of structural properties based on attribute data.............................................................................................................................107
7.3.5 Summary of findings ...........................................................................................................................................................................................107
7.4 The aggregate network of formal and informal ties revisited ...........................................................................................................................................108
8 Cluster initiatives.............................................................................................................................................................................................................................111
8.1 SWOT analysis..................................................................................................................................................................................................................111
8.2 Cluster initiatives...............................................................................................................................................................................................................113
9 Retrospect and Prospects.................................................................................................................................................................................................................119
10 References.................................................................................................................................................................................................................................... ii
10.1 Primary sources .................................................................................................................................................................................................................... ii
10.2 Secondary sources ................................................................................................................................................................................................................ ii

7. 1
1 Introduction
The purpose of this chapter is to introduce the reader to the motivations behind this thesis as well
as the structure and methodology applied. Furthermore, we will touch upon considerations with
regards to the nature of the study and its limitations.
1.1 Motivation
The aim of the thesis was chosen together with members of the ‘mechatronics workgroup’. This
is a group of mechatronics1
firms and support organizations that meet regularly to discuss cluster
initiatives (CIs). Their mission statement is; ”…to strengthen the competitiveness and the profile
of the Danish mechatronics cluster by identifying the strategic challenges and opportunities the
cluster is facing and subsequently take action on this background”2
. With this in mind, we
investigated the cluster literature and the prior studies of the cluster, and were intrigued by the
opportunity to deliver new and exciting findings on the mechatronics cluster, and to be pioneers
within a new and promising approach of empirical studies of clusters;
Prior studies of the mechatronics cluster have made extensive analysis of the ‘frame conditions’
in Southern Jutland and of the composition of industries in the cluster. There is, however, a lack
of knowledge on the dynamics within the cluster, more specifically, why and how much cluster
members interact with each other. At the same time, a large stream of the cluster literature states
that many of the benefits from clustering are directly or indirectly dependent on the degree of
interaction between cluster members. However with respect to the mechatronics clusters, little
empirical work has been done in this area.
As such, it is our endeavor to attain an in depth understanding of the dynamics within the
mechatronics cluster in Southern Jutland. More specifically, we strive to understand the purpose,
extent and nature of the relationships between the organizations in the cluster and to characterize
the cluster members in terms of their attributes, but also in terms of their position in the cluster.
This will enable us to develop CIs that specifically target the strategic challenges and
opportunities within the cluster.
1
Mechatronics is defined as the synergistic combination of mechanical engineering ("mecha" for mechanisms, i.e.,
machines that 'move'), electronic engineering ("tronics" for electronics), and software engineering (wikipedia.org).
2
The chairman of the mechatronics workgroup is the director of Center for Erhvervsudvikling (CfE) in Sønderborg.
CfE has formulated the above mentioned mission statement for the mechatronics cluster (www.cfe.dk).

8. 2
We believe that our investigation into the strengths and weaknesses of the dynamics within the
mechatronics cluster is of a unique nature due to its great empirical background and the
innovative methodological approach. Consequently, we are hoping that this study will first of all
be of value to the mechatronics cluster in Southern Jutland, but also make a contribution to
academics and especially practitioners within the cluster field in general.
1.2 Problem statement
The aim of this thesis is to derive at practical and deliverable insights that will enable us to get an
overview, especially, of the dynamics inside the mechatronics cluster in Southern Jutland, and on
this background, strategize on CIs that will enable the cluster to move forward from its present
stage. As such, we seek to answer the following problem statement;
“Based on an empirical analysis of the mechatronics cluster we wish to investigate the
composition of actors and their relations in order to construct a strategy that will address the
identified weaknesses and threats and augment any strengths and opportunities”
In carrying out the analysis and arriving at a well documented answer to the problem statement
we seek to answer the following research questions:
1. How important are relations inside the cluster in a larger context?
We seek an understanding of the overall importance of the actors and ties within the
cluster as compared to outside
2. What is the composition of actors in the core of the cluster?
We seek an understanding of the attributes of the cluster members and distinguish
between central and peripheral actors
3. What are the network positions of cluster members?
We seek an understanding of the roles of central cluster members and any structural
patterns within the cluster as defined by cluster members’ formal and informal ties
Finally, based on the mechatronics cluster’s strategic objectives and the understanding of the
cluster, which we will gain through our research questions; we will answer our problem
statement by proposing CIs that can drive the cluster forward.

9. 3
1.3 Delimitation
In this section we discuss the limitations of our research. These limitations are mainly a result of
two factors; the large literature within cluster theory and the nature of our empirical investigation.
Our ambition to analyze the mechatronics clusters by combining cluster theory and social
network analysis causes us to focus more on relations, than what is typical for cluster scholars.
However, our theoretical perspective on clusters is not limited entirely to this focus, as we
recognize that many schools within the cluster literature have compelling insights. We will later
clarify our theoretical perspective on clusters, but here briefly account for some discussions in the
cluster literature, which have not been included due to the focus of this thesis.
First of all, we note that agglomeration theory can be separated into two classes; agglomeration
forces operating at a general level and agglomeration forces at the level of related firms and
industries (Malmberg et al 1996). Since the mechatronics cluster is defined by prior studies as an
agglomeration of related firms and industries we have chosen to focus our theoretical discussion
on this. Consequently, we only implicitly consider the arguments for agglomeration in general to
the extent that the two classes overlap.
Secondly, topics like ‘social capital’, ‘knowledge’, ‘innovation’ and ‘competitive advantage’
are all disciplines in themselves and we could spend significant amounts of time on each;
however, we introduce them only to the extent they function as a prerequisite for understanding
how clusters work. Also, we discuss the theoretical benefits from clustering; however, we do
not discuss how to directly measure these benefits, as this is seen as a next step in relation to our
ambition of exploring the cluster and proposing CIs. The frame conditions of the cluster have
been extensively explored in prior studies and will therefore not play a central role in this thesis.
To the extent that we touch upon frame conditions, we mainly focus on institutions (non-firms)
as their role can be traced through a relations-centered view. Finally, since the mechatronics
cluster is to a large extent privately led and since frame conditions, which are often the aim of
policy intervention, are outside the scope of our thesis, we only to a very limit extent consider
how government and policy making can play a role.
Finally, it should be noted that although our theoretical discussion of clusters focuses heavily on
knowledge flows and how these presumably translate into innovation advantages, we choose not
to look at the internal workings of firms. Although focusing on how firms translate cluster

10. 4
location into competitive advantage is an interesting topic and would make for a likewise
interesting study, it is considered to be too extensive to include in the study at hand.
There are also limitations to our empirical investigation, which are important to note.
First of all, we note that most of our empirical data stems from questionnaires filled out by the
respondents. Two sources of uncertainty derive from this; firstly, we have not had the
opportunity to make follow-up questions on unclear or interesting aspects of the collected data.
As such, information that we have not been able to uncover through our data collection and
which is not readily accessible through secondary sources is outside the scope of our project. The
second source of uncertainty derives from the fact that our empirical investigation is based on
respondents’ answers with regards to qualitative data, such as their knowledge of other
organizations and the frequency of contact with other cluster members. We deal appropriately
with this uncertainty in our analysis, as we recognize that respondents’ answers are dependent on
their subjective realities, and their ability to remember interactions. However, we will not go into
detail with such considerations, as it is beyond our focus on inter-firm relations in clusters.
Secondly, our empirical investigation is limited by the fact that only 32 out of 65 targeted
organizations participated in our study by filling in the questionnaire (see appendix 1.1).
Although this is by far the best participation rate in any study of the mechatronics cluster so far, it
still means that there is some uncertainty with regards to the non-respondents role in the cluster.
We must also note that the exact boundaries of the cluster from a social network perspective
would require a much larger study, primarily in terms of access to potential cluster members.
Although we have valid grounds to state that the boundary specification of the cluster is as found
in our study, which we will explain in more depth later, it cannot be said with certainty.
Finally, we will in accordance with the aim of this paper, concentrate our empirical
investigation, entirely on the mechatronics cluster and especially the relations between
cluster members. Therefore, a comparison of the mechatronics cluster with other similar clusters
is outside the scope of our thesis, although it would be very insightful. Also, we will not verify
the findings of prior studies of the cluster as they have mainly dealt with aspects of the cluster
outside our scope (frame conditions). However, we acknowledge that the validity of these earlier
findings becomes more and more outdated.

11. 5
1.4 Methodology
In this section, we will account for some of the methodological considerations of our study and
present the structure of the thesis. The purpose is to introduce the reader to the basis of our study
and to allow the reader already at this stage to get an overview of the following analysis.
1.4.1 Nature of the study
As noted in the motivation to this study, there is a lack of empirical studies on clusters in general
and on the dynamics within clusters in particular. Although, many renowned scholars adhere to
the view that benefits from clustering are directly or indirectly dependent on the degree of
interaction between cluster members, much of the cluster literature is very theoretical and lacks a
practical and systematic methodology. As a consequence, we have married the theoretical
insights from cluster theory with the methodological approach of social network analysis. This
has proven to be a very powerful combination that has allowed us to arrive at a far more complex
picture of the dynamics within the mechatronics cluster than any of the prior studies.
In this section we will discuss some of the methodological challenges and considerations, which
we have dealt with throughout writing the thesis; we discuss the case study as a research method,
and the extensive empirical data collected and used in the thesis.
With regards to our study as a case study, we note that in cluster studies and especially in
social network analysis, case studies are often used to test the validity of certain arguments or
specific measures for their ability to explain cluster theoretical attributes of a cluster. Our thesis
is markedly different from such theoretical discourses, as the aim is to derive at solutions
specifically for the case studied; the mechatronics cluster. As such, we consider theory and
models from cluster theory and SNA in order to construct a typology of clusters, and then use
this to analyze the case in question. However, the purpose is not to test the constructed typology,
rather it is to develop CIs that can drive the cluster forward. That is, we focus on the first and last
step in the research process proposed by Harald Enderud, 1979 (in Flyvbjerg 1988). For this
reason, we do not formally propose that the insights gained throughout our thesis can be applied
more generally, although, we briefly consider this in our final conclusion. We also note that due
to our primary use of cluster theory as a prerequisite for our empirical investigation, we take on a
pragmatic view of clusters in which focus is on understanding clusters based on different
arguments and perspectives rather than challenging, and discussing their differences.

12. 6
The basis of our thesis is a large empirical investigation based on input from cluster members.
More specifically, our empirical analysis is based on correspondence with people that are seen as
experts on the mechatronics cluster, such as the chairman of the mechatronics workgroup, a
cluster consultant from Center for Erhvervsudvikling (CfE) in Sønderborg, and a few CEOs and
higher level managers for firms in the cluster. However, most of our empirical data stems from
information gathered through a questionnaire sent to relevant organizations in what was
perceived to be the core of the mechatronics cluster (in section 4.2.2 we account for the selection
of organizations that make up the cluster as defined in this study). The questionnaire, which can
be reviewed in appendix 1.4 and 1.5, was developed based on the knowledge of the cluster from
prior studies (chapter 4), and the insights from cluster theory and SNA (chapters 2 and 3). The
main topics covered by the questionnaire were the respondents’ and the firms’ background, the
identification of the firms’ “most important” customers, suppliers, competitors and cooperative
partner, as well as cluster members’ relations with each other. More specifically, the latter part of
the questionnaire consists of a roster list of the 65 organizations in the cluster as defined in our
study and covers questions about the cluster members’ mutual knowledge of each other as well as
their perceived technological distance and any personal and organizational relations that exist
between them. Finally, we have also gathered data on the age, size and industry of the
organizations, their geographical proximity, and their attendance of mechatronics workgroup
meetings. This information is based on own research on the firms and institutions in the cluster as
well as data supplied to us by CfE. Therefore, we have data on all actors with regards to these
properties.
Given that 32 out of 65 targeted organizations in the cluster have participated in our study, we
have achieved the highest participation rate of any study of the mechatronics cluster so far and
have an overwhelming amount of data from CEOs, owners and higher-level managers (see
appendix 1.3). To raise the response rate for our study, we ‘pitched’ our project to all the
organizations by phone before sending out our questionnaire, and also have a much more focused
target group than prior studies. In comparison, COWI’s (2006) study was also based on
questionnaires send to selected organizations, but only had a response rate of 25%. It has required
several months of data analysis to fully comprehend and compress the main insights from all this
data, and as such all cannot be included in this thesis. The reader will note the extensive size of
the exhibits, but also the overwhelming amount of data and analysis, which can be reviewed on
the enclosed CD.

13. 7
Another factor, which works to increase the size of the thesis and the exhibits, is the space
consuming nature of the graphical social network analysis. The most important graphs are
included in the project, but the majority is placed in the exhibits. This is reasonable because
graphical representations of the networks in question are really just ordered representations of
respondents’ answers to different questions3
. The graphs summarize many of the cross
tabulations of data, which truly enrich our analysis and findings. Therefore, we take care to
extensively explain what conclusions can be drawn from the graphical analysis, and although we
believe it is interesting and furthers the understanding of the findings, the project can be read
without referring to the exhibits.
Finally, we note that in order to ensure respondents’ confidentiality on certain aspects, which
has been required of us, we will refer to actors using unique numbers based on actors’
classification according to NACE codes (see appendix 1.2). Thereby, we conceal actors’ identity,
but still make it possible to meaningfully refer to organizations (furthermore, we allow ourselves
to refer to non-participants, and participants who have given their consent, by name). We will
later go into the composition of actors according to the different industry groupings (NACE
codes), but here note that NACE code “00” has been established to ensure confidentiality. It
consists of 6 firms; two manufacturers of electrical construction equipment, a telecommunication
company, a venture capital firms, a consulting firm, and a wholesale trader of mechatronics
related equipment. The diversity of this group naturally affects our ability to draw conclusions on
behalf of its constituents firms. Finally, although we use NACE codes to refer to actors and to
some extent analyze properties of actors in each NACE code, we acknowledge that these
groupings can be very broad and for this reason we analyze relations and attributes of actors
irrespective of their NACE codes as well.
3
A network refers to the collection of data on a specific dimension of the cluster, such as the network of personal
contact or the network of technological distance between cluster members.

14. 8
1.4.2 Structure
The aim of our thesis is to answer our problem statement and research questions. In large part, we
do this by fully understanding the overall structure and drivers of the mechatronics cluster
together with its mission statement as discussed earlier.
The understanding of the cluster will
come as follows; firstly, we introduce
our theoretical and methodological
perspective on clusters, which
explains our focus when examining
clusters, and how we intend to do this
empirically by developing concrete
tools for analysis (see figure). Next,
we introduce the mechatronics
cluster by briefly presenting the most
important findings from prior studies
of the cluster and then introduce our
own empirical investigation, by
discussing the boundaries of the
cluster and the following structure of
the investigation. Finally, we reach
our own empirical investigation,
which comes in three steps in accordance with our research questions. We will go into more
detail about the three steps and their interrelatedness later, but for now we note that we follow a
logical structure in the sense that each chapter goes deeper and deeper into the complexity of the
cluster. We conclude and summarize our findings in a SWOT analysis and on this background
propose cluster initiatives that can drive the cluster forward. In the final chapter, we very briefly
present an overview of our thesis and discuss the applicability of our study in a larger context.
Finally, we note that the fairly extensive exhibits are structured in accordance with the chapters
in the thesis, so that references within a chapter generally refer to the equivalent chapter in the
appendix.

15. 9
2 Theoretical framework
In this chapter, we will construct a framework reflecting our perspective on clusters. This will
give us a theoretically founded basis for structuring our empirical investigation of the
mechatronics cluster, interpreting the results and based on that suggest appropriate CIs. We split
the chapter into three parts; in the first part, we introduce clusters as a concept and argue for our
theoretical perspective on clusters. Next we use this knowledge to construct a typology of
clusters and explain how various types of clusters function. This provides us with a theoretical
framework, which we can use to structure and interpret our findings on the mechatronics cluster.
The final part uncovers questions regarding cluster initiatives (CIs)4, which is a natural extension
of the first two parts, since efficient CIs precondition a thorough understanding of the specific
cluster and its setting.
2.1 Introduction to clusters and how they work
In this section, we discuss our perspective on cluster. A natural starting point is to discuss what
clusters are and how they function. To do this we will introduce the concept of clusters based on
a list of frequently cited definitions, which we break down into their shared components.
There has not yet been put forward an agreed upon definition of what clusters are; Feser (1998)
states “there is no cluster theory per se, rather a broad range of theories and ideas that constitute
the logic of clusters.” Martin and Sunley (2003) support this by noting that clusters are subject to
“definitional and conceptual elasticity.” To understand this critique let us look at a list of
definitions proposed by some of the most renowned cluster scholars (table adapted from Martin
and Sunley (2003));
Porter (2000, p. 16) “A cluster is a geographically proximate group of interconnected companies and
associated institutions in a particular field, linked by commonalities and complementarities”.
Porter (1998, p. 10) “A cluster is a critical mass of companies in a particular field in a particular location,
whether it is a country, state or region, or even a city. Clusters take varying forms depending on their
depth and sophistication, but most include a group of companies, suppliers of specialised inputs,
components, machinery, and services, and firms in related industries. Clusters also often include firms in
downstream (e.g. channel, customers) industries, producers of complementary products, specialised
4
Cluster initiatives are defined by Sölvell et al (2003) as “…organised efforts to increase growth and
competitiveness of clusters within a region, involving cluster firms, government and/or the research community.”
Cluster initiatives is broader than cluster policies as the latter includes only measures undertaken by actors in the
public sphere (Andersson et al 2004)

16. 10
infrastructure providers and other institutions that provide specialised training, education, information,
research, and technical support, such as universities, think tanks, vocational training providers, and
standards-setting agencies. Finally many clusters include trade associations and other collective bodies
covering cluster members”
Rosenfeld (1997, p. 4) “A cluster is very simply used to represent concentrations of firms that are able to
produce synergy because of their geographical proximity and interdependence, even though their scale of
employment may not be pronounced or prominent.”
Feser (1998, p. 26) “Economic clusters are not just related and supporting industries and institutions, but
rather related and supporting institutions that are more competitive by virtue of their relationships.”
Swann and Prevezer (1998, p. 1) “A cluster means a large group of firms in related industries at a
particular location”.
Simmie and Sennett (1999a, p. 51) “We define an innovative cluster as a large number of interconnected
industrial and/or service companies having a high degree of collaboration, typically through a supply
chain, and operating under the same market conditions.”
Roelandt and den Hertag (1999, p.9) “Clusters can be characterised as networks of producers of
strongly interdependent firms (including specialised suppliers) linked to each other in a value-adding
production chain.”
Van den Berg, Braun and van Winden (2001, p. 187) “The popular term cluster is most
closely related to this local or regional dimension of networks … Most definitions share the
notion of clusters as localised networks of specialised organisations, whose production processes
are closely linked through the exchange of goods, services and/or knowledge.”
Enright (1996, p. 191) “A regional cluster is an industrial cluster in which member firms are in close
proximity to each other.”
Although the focus and wording of the definitions differ, they tend to share four main
components; (1) concentration of interdependent firms, (2) institutions, (3) relations and (4)
benefits from industrial clustering5
. In the following, we will review these four components, and
thus explain our focus area within the large and complex cluster literature.
2.1.1 Concentration of interdependent firms
Industrial clusters evolve around a concentration of interdependent firms. When breaking this
term down, we find that it consists of two elements. One is; concentration of firms, another is;
interdependency between firms.
5
Some of the above definitions also mention the importance of location and market conditions. Such ‘frame
conditions’ must to some extent be in place to support the cluster. However, due to the empirical focus of our thesis,
we will only touch upon such frame conditions briefly in the following. According to Porter (2000a) factor (or
frame) conditions are human-, natural- and capital- resources as well as physical-, technological-, administrative-
and information- infrastructure. Therefore, we note that ‘institutions’ are a subset of frame conditions.

17. 11
We note that a concentration of firms conditions a geographically confined area, as well as a
standard to which the observed concentration can be compared. When focusing on concentration
levels, there are mixed opinions of how large the concentration has to be. Some scholars argue
that the minimum concentration depends on the specific case (Ecotec 2004), while others
advocate fixed measures, such as specific minimum values of location quotients (Bergman and
Feser 1998)6
. However since location quotients consider only one type of measure (number of
employees) and not others; such as number of firms, collected revenue, or the cluster firms’
global market shares there is a dependence on the empirical method applied. At the same time
young clusters with a large growth potential are very likely not to exhibit a high concentration,
but that is not sufficient grounds for ruling them out. One way of setting a lower concentration
limit would be to make it a function of when a cluster starts producing benefits. If this is the case
the limit comes to depend on the industries involved, the absolute- and relative concentration of
firms, how concentration is measured, as well as a host of other parameters which differ both
from cluster to cluster and from study to study7
. Based on this, we believe the concentration limit
to be an empirical question where the answer depends on the specific case.
With regards to the interdependence between firms, we note that interdependence preconditions a
shared denominator among the firms. This shared denominator is related to functional proximity,
i.e. that the firms either cater to the same or similar end-market(s) and/or build upon comparable
competencies. As can be seen from the definitions of clusters on page 9-10, functional proximity
operates on two dimensions. One is the vertical dimension where firms occupying various parts
of the value chain establish interdependency through input-/output relationships. The other is the
horizontal dimension, where firms operating in the same or related markets at similar levels of
the value chain either cooperate or compete on input, output and competencies. The reason why
functional proximity is important will be investigated later. However, we note that by allowing
not only geographical proximity, but also functional proximity, to play a role, we divide
agglomeration theories into two groups; those that deal with agglomeration of economic activity
in general and those that deal with spatial clustering of related firms and industries (Malmberg et
al 1996). The latter is the focus of this thesis.
6
Location quotients compare employment levels in the regional industries in question with corresponding national
industry employment levels. They will be discussed in detail in chapter 3.
7
Unfortunately this does not fully consider young clusters with a large growth potential since benefits have not
necessarily set in at this early stage

18. 12
2.1.2 Institutions
In addition to interdependent firms, cluster definitions also contain institutions. In some studies
‘institutions’ refer to certain norms and values8
, however, in our thesis they refer to the following
non-firm organizations; “…economic agents (e.g., research institutes; universities; primary,
secondary, and higher education; other training institutes; authorities; financial intermediaries
and so on)” (Sornn-Friese 2003). Supporting institutions, such as research institutes and
universities produce knowledge that may be exploited by the firms, and training and educational
institutes provide the firms with a highly skilled labor pool. Furthermore, institutions may work
as intermediaries between firms.
2.1.3 Relations and benefits from industrial clustering
The interdependency between firms in clusters means that there must also be relations or ties
between cluster members, which is the third component in cluster definitions. In the following,
we will briefly introduce the cluster as a social network of relations and the important notion of
social capital. However, since relations produce the majority of benefits from industrial
clustering, we afterwards consider the interplay between these two components in more depth.
The following sub-sections are then divided into two parts in accordance with the nature of
benefits, pecuniary and non-pecuniary benefits.
2.1.3.1 Relations
From the definitions of clusters on page 9-10, we see that especially Rosenfeld, Feser, Simmie
and Sennett as well as Roelandt and den Hertog support a view of clusters as defined by its social
network in that their definitions of clusters entail some notion of relations between cluster
members leading to synergies, competitiveness, or innovativeness.
In a cluster we expect cluster members to be connected through both vertical and cooperative
relations in accordance with the functional proximity between them. In many cases such formal
relations among cluster members over time develop into informal ties (Wolfe and Gertler 2004).
To this end, Gordon and McCann (2005) state that the relations between organizations or
individuals in a cluster rely on; “…a common culture of mutual trust, the development of which
depends largely on a shared history and experience of the decision-making agents”. This culture
8
Such norms and values will be discussed later when discussing relations and social capital in a cluster.

19. 13
of trust is also known as social capital, which Wolfe (2002) refers to as "…various features of the
social organization of a region, such as the presence of shared norms and values that facilitate
coordination and cooperation among individuals, firms, and sectors for their mutual advantage."
More practically social capital works in a cluster because favors are expected to be returned later,
trustworthiness is communicated and tested, past successes of collaboration exist and there is a
real threat of punishment of those who act opportunistically by excluding them from the network
(Sirianni and Friedland 1995).
Finally, since social capital is either attributable to historic or cultural factors in a region's past
(communitarian) or built up through dense networks of interactions of firms engaged in
interrelated activities with a high level of mutual trust (performance-based) it is confined to a
given region (Wolfe 2002, Rawad 2005).
2.1.3.2 Pecuniary benefits
The majority of pecuniary benefits stem from formal trade linkages between actors in various
parts of the value chain. As a consequence most of these linkages are vertical, which produce
benefits in terms of lower transportation- and search costs, a more specialized division of labor,
local outsourcing possibilities and economies of scale.
Proximity between firms located in clusters cause trade between two firms to be subject to lower
transportation costs, especially for distance sensitive goods, such as large and heavy goods
(Johansson 2005). At the same time the high concentration of potential trading partners and the
close geographical proximity lowers search costs, which is important for firms frequently looking
for new suppliers and/or customers (Johansson 2005). In clusters, firms will by focusing on core
competencies often “…gradually move from the horizontal to the vertical dimension of the
cluster by concentrating on some particular process, where they believe they possess or might
develop certain lucrative capabilities, dissimilar to others” (Maskell 2001). Hence, over time
cluster firms typically become more and more specialised causing a deepening division of labor,
which in return leads to improved firm profitability (Bergman and Feser 1999). The
concentration and variety of suppliers that emerges from this movement in return allows for
outsourcing of non-core business to local suppliers which might not only result in increased
profitability, but also increased flexibility with respect to responding to complex and rapidly
changing customer demands (Lublinski 2002). Finally, Krugman (1991) finds that locating close
to a large market might provide firms with a chance to exploit economies of scale.

20. 14
In addition to the above vertical linkages, firms might also obtain pecuniary advantages through
cost sharing. These can be in the form of shared transportation or shared sales and marketing9
.
The above pecuniary benefits stem to a large extent from proximity between actors. However in
recent years drivers of globalization, such as; reduced barriers to trade, improved communication,
enhanced and fastened transportation of goods and people, global finance and the widespread
availability and use of technology have all worked to reduce the role of distance (Enright 1998).
As a result the above pecuniary advantages are being challenged. At the same time, given
globalization and the competitive pressures from low cost countries, technologically
sophisticated firms have been forced to compete, not on cost, but on the basis of differentiated
performance and innovation (Sornn-Friese 2003, Feldman and Martin 2004). This limits the
importance of pecuniary benefits and leads us to discuss what many consider the most important
cluster-benefit; knowledge accumulation10
.
2.1.3.3 Non-pecuniary benefits
To understand why knowledge accumulation occurs and how it can be valuable to firms locating
in clusters, let us consider how knowledge is produced, applied, and through an understanding of
the nature of knowledge, also how it ‘flows’.
In broad terms, firms tap into two sources of knowledge; one is knowledge originating from
inside the firm, which can stem from R&D or intentional and unintentional upgrading of
processes and knowledge. The other is knowledge originating from outside the firm in the form
of knowledge flows from the firm’s environment (Cohen and Levinthal 1990, Johansson 2005).
Internal knowledge creation has a dual effect. The first and most direct effect is the creation of
knowledge that can be applied in innovations and improvement of processes and day-to-day
routines. The second and more subtle effect of internal knowledge creation is that it raises the
firm’s absorptive capacity, which Cohen and Levinthal (1990) define as “the ability of a firm to
recognize the value of new, external information, assimilate it, and apply it to commercial ends,”
and add that “the ability to evaluate and utilize outside knowledge is largely a function of the
9
If this occurs between functionally proximate firms in similar parts of the value chain, it might be competition
distorting behaviour and therefore might conflict with written law (Johansson 2005)
10
There exist different schools of thought in cluster thinking and by viewing knowledge accumulation as the most
important benefit from clustering we adhere to the view of mainly “regional innovation systems” and “dynamic
externalities schools”. For a brief introduction to the different schools please refer to appendix 2.1.

21. 15
level of prior related knowledge”. Sornn-Friese (2003) states that “…globalization forces firms to
innovate faster and rely still more on outside sources of knowledge”, which means that
absorptive capacity becomes increasingly important. However, absorptive capacity can only be
valuable to firms, if they are exposed to external knowledge in sufficient degree and quality,
which is exactly the case for firms located in clusters. To understand why this is so, we need to
look at the nature of knowledge.
Basically knowledge can be divided into two groups, explicit and tacit. Explicit knowledge can
be articulated, codified and stored and can consequently be communicated over long distances.
Examples hereof are manuals, documents, procedures, etc. Tacit knowledge on the other hand
cannot be codified and can thus only be transmitted via training or gained through personal
experience. A simple example of tacit knowledge is”…that one does not know how to ride a bike
or swim due to reading a textbook, but only through personal experimentation, by observing
others, and/or being guided by an instructor” (wikipedia.org). The immobility of tacit knowledge
makes it very valuable to firms that possess it, as it can be source of sustainable competitive
advantages (Barney 2002).
To understand how knowledge can be beneficial on a cluster scale we remember that social
capital within a dense network of relations in a cluster reduces transactions costs and induces
more frequent knowledge sharing. Comparing this insight to Cohen and Levinthal’s (1990)
finding that knowledge is not subject to complete appropriability, we find that in clusters it is
social capital which allows for the rapid diffusion of knowledge. Due to the high concentration of
functionally proximate firms this diffusion results in a large pool of relevant and novel
knowledge, which is accessible at low costs and can be used by firms for innovation purposes.
Adding to this, we find that untraded interdependencies in the form of "…technology spillovers,
conventions, rules and languages for developing, communicating and interpreting knowledge"
allow for transmittance of tacit knowledge among local actors, which is essentially the most
difficult type of knowledge to transmit and therefore also the most valuable (Storper 1995 in
Rawad 2005).
To understand why knowledge does not flow to actors outside the cluster, we recall that
transmitting tacit knowledge requires face-to-face contact which naturally requires more effort if
actors are positioned far away from each other. At the same time, untraded interdependencies are
lacking in relationships to actors outside the cluster making it hard to transmit tacit knowledge.

22. 16
Finally, since social capital is mainly confined to a given region it does not exist to the same
extent in relationships to actors located outside clusters, which is why these relationships are
more costly and always precondition immediately foreseeable benefits for the parties involved
(Baptista 2000, Bathelt et al 2002, Martin and Sunley)11
. In this way cluster members have
proprietary access to novel knowledge that can be used for innovation purposes12
.
Now that we have talked about why knowledge flows can occur inside clusters and why they can
be valuable to cluster firms, let us look at how they occur as this allows us to come full circle on
the relations that exist in clusters. In broad terms we can separate knowledge flows into two
separate classes; knowledge spillovers and knowledge transfers. We note that “at every possible
interaction, there is a potential for knowledge exchange. If knowledge is exchanged with the
intended people or organizations, it is knowledge transfer, while any knowledge that is
exchanged outside the intended boundary is spillover” (Fallah and Ibrahim 2004). Firms in a
cluster are in a good position to exchange both types of knowledge flows, while knowledge flows
to/from actors outside the cluster are mainly limited to knowledge transfers due to the lack of
social capital. In the following, we will first explore the two types of knowledge flows as they
occur in cluster, and next discuss knowledge flows to/from actors located outside the cluster.
2.1.3.3.1 Knowledge spillovers (unintended knowledge flows)
Knowledge spillovers occur because “proximity and equal conditions for the firms make
benchmarking easy, at the same time peer pressure based on pride forces companies to perform”
(Porter 2000b). Maskell (2001) complements; “co-localized firms undertaking similar activities
find themselves in a situation where every difference in the solution chosen, however small, can
be observed and compared. While it might be easy for firms to blame the inadequate local factor
market when confronted with the superior performance of competitors located far away, it is less
so when the premium producer lies down the street.” Hence co-localization of like firms displays
the weaknesses of the individual firms. Or in other words, co-localization provides for
observability which refers to the fact that “spatial proximity brings with it the special feature of
11
The regional nature of social capital is reflected in the name, since capital implies that we are dealing with an asset
and social tells us that it is attained through membership of a community
12
It can be argued that modern inventions like state-of-the-art communication equipment allow for long-distance
communication and that fastened transportation allows for frequent face-to-face contact. However building shared
norms, values and beliefs between geographically and culturally distant firms takes long time, possibly limiting the
tacit knowledge diffusion process. As we shall see later unintentional flows of tacit knowledge still mainly occur in
geographical proximity through shared experience, random observations, comparisons and worker movement.

23. 17
spontaneous automatic observation” and comparability where “each firm in the horizontal
dimension of the cluster is provided with information about the possibilities to improve and the
incentives to do so” (Malmberg and Maskell 2001).
Since observability and comparability work best between similar firms with equal factor
conditions it most frequently occurs between functionally proximate firms and along the
horizontal dimension. Maskell (2001) adds to this that; “if the firms operating along the
horizontal dimension of the cluster were to be spread thinly throughout a large city among many
unrelated businesses their ability to monitor and subsequently learn from each other’s mistakes
and successes would be severely restricted.” This explains why a concentration of interdependent
firms is important for the workings of clusters.
Since firms acquire knowledge by observing and comparing their own solutions to those of their
competitors, most of the knowledge that spills over is tacit. As mentioned earlier, it is believed
that social capital and untraded interdependencies aids knowledge spillovers inside clusters. At
the same time, since knowledge spillovers are unintentional, in the majority of cases they do not
involve formal relations between the firms involved. This makes knowledge spillovers very hard
to identify empirically.
2.1.3.3.2 Knowledge transfers (intended knowledge flows)
Most knowledge-sharing relations inside clusters build on informal relations, mainly based on
personal contacts (Baptista 2000). These can originate from supplier or customer (vertical)
relationships, shared place of work, fellow students as well as other forms of social ties fostered
by the spatial proximity of firms and workers in clusters.
An important type of knowledge transfer is vertical relationships, which mainly occur in clusters
where more parts of the value chain are represented. As noted earlier, over time such formal trade
relations among cluster members in many cases develop into informal ties, where shared culture
and frequent interactions, create a basis for knowledge sharing on both the traded product and
possibly also the production process for the benefit of both (Wolfe and Gertler 2004). Another
possible knowledge transfer can be sourced from local institutions, such as universities, research
centers, etc. (Porter 2000a). In line with Porter (2000a), who argues that cluster firms obtain
value from increasingly specialized factor inputs, Breschi and Lissoni (2000) note that local
universities and research institutions are valuable to cluster firms because “… local universities

24. 18
provide critical inputs for firms’ innovative activities even without producing any research which
is directly relevant for firms’ current innovation projects, namely training and consultancy.”
Intentional cooperation between competitors (horizontal ties) may in some cases violate written
law due to functional proximity (Johansson 2005). However, in broad clusters cooperation can
occur between firms belonging to different, but related industries. Cooperation in these cases
does not hold the same jurisdictional limits and can therefore entail many different types.
Finally, the most important source of knowledge transfers seems to be the movement of the local
work force. Since tacit knowledge is confined to individuals, according to Breschi and Lissoni
(2000) “it is suggested that high, but localized labor mobility and firm spin-offs ensure both fast
diffusion inside the area, and no diffusion outside it.” One might suspect that this type of
knowledge transfer resembles more that of knowledge spillovers, however Breschi and Lissoni
(2000) argue that as workers move from one firm to the other, they help diffuse knowledge
through a certain region production complex, thus creating a local manufacturing environment in
which firms build cumulatively upon a common stock of technological successes and failures.
Apparently, this outcome resembles local knowledge spillovers (LKS), but it does not require any
face-to-face, inter-personal or inter-firm sharing of tacit knowledge. Another reason why work
force mobility should not be viewed as a knowledge spillover is that it can be assumed that
people are hired and paid on the basis of their knowledge, which makes the knowledge transfer
intentional. One final note is that, worker mobility is mainly intra-regional. Feldman and Martin
explain it by noting that “labor is less mobile than capital and workers become more skilled as
they age but then correspondingly become more immobile as they form relationships, raise
families and become members of communities.”
2.1.3.3.3 Formal relationships with actors outside the cluster
Unlike many of the linkages that cause knowledge flows inside clusters, “the processes behind
the establishment and maintenance of global pipelines must be pre-designed and planned in
advance, and they require specific investments13
” (Bathelt et al 2002). Furthermore, the trust that
naturally exists between firms inside the cluster does not exist between cluster firms and outside
firms and has to be built up over time. Consequently there is a limit to the number of pipelines a
firm can hold. However, it is suspected that “a large number of related independent firms in a
13
The term ‘global pipelines’ covers formal and informal relationships with actors outside the cluster

25. 19
cluster can manage a larger number of pipelines than one single large firm alone. If this is true,
this could provide a possible explanation why spatial clustering gives rise to competitive
advantage”, as knowledge sourced via pipelines often is absorbed and spills over to other firms
inside the cluster (Bathelt et al 2002). In fact, Rosenfeld (1996) sees this spillover process as an
indicator of how well a cluster functions; “If the firm is operating in an effective cluster, the
learning it acquires through relationships outside of the cluster is more apt to be rapidly diffused
to other firms, multiplying its impact.” In this way cluster firms benefit from the shared number
of pipelines to the extent that the cluster is effective, which we shall later see depends on the total
amount and strength of relations between actors.
To sum up how knowledge accumulation works, please see the model below. As can be seen,
innovation builds on two knowledge sources; internal knowledge and external knowledge. In
general knowledge created inside firms unavoidably flows to other firms in the cluster. At the
same time the process of creating knowledge also creates absorptive capacity which can be used
for exploiting external knowledge. What is particular to firms located in clusters is that social
capital and untraded interdependencies allow for rapid and costless diffusion of valuable and
distance sensitive tacit knowledge inside the cluster. Knowledge stems either from relationships
with external actors or from knowledge transfer or knowledge spillovers inside the cluster. Of
particular value seems to be the mobility of the local work force which is highly mobile inside
the cluster, but much less so outside the cluster. This knowledge accumulation results in both a
pressure- and a platform for constant upgrading, causing cluster firms to be competitive vis-à-vis
firms outside the cluster.

26. 20
2.2 Cluster typology
At this point, we have gained an understanding of what clusters are and how they work. As such,
we are ready to construct a typology of clusters, which will allow us to distinguish between
clusters based on their composition of actors and their relations, and make theoretical inferences
about how a given cluster works, and what its strengths and weaknesses are. The typology will
consist of three parts. First, the overview and consequences of relations in a cluster is made more
concrete. Secondly, we consider the composition of actors in a cluster, and finally, we add a
dynamic dimension to the discussion as we investigate how clusters develop over time.
2.2.1 Relations
From our discussion of the interplay between relations and benefits from industrial clustering we
found that relations work to produce most of the firm specific benefits from clustering14
. Since
none of the relations discussed had any negative effect for the cluster firms, it follows that a high
degree of relations in a cluster is equivalent to more benefits for its members.
Enright (1998) considers this issue in his typology of latent clusters and working clusters, where
he argues that “latent clusters have a critical mass of firms in related industries sufficient to reap
the benefits of clustering, but have not developed the level of interaction and information flows
necessary to truly benefit from co-location.” The reason Enright (1998) argues is “a lack of
knowledge of other local firms, a lack of interaction among firms and individuals, a lack of a
common enough vision of their future, or a lack of the requisite level of trust for firms to explore
and exploit common interests”. Working clusters on the other hand “tend to have dense patterns
of interactions among local firms that differ quantitatively and qualitatively from the interactions
that the firms have with those not located in the cluster” (Enright 1998). Naturally firms’ ability
to reap benefits from being in a cluster is not only a function of the number of relations, but also
the distribution of these relations with respect to strength and type, e.g. the benefits from a
vertical relation might be different from those gained from a cooperative relation. Also, whether
the relation is between organizations or between individuals has an impact on the formalization
and strength of the relation.
14
Regions can also benefit from the presence of clusters in that clusters among other things work to create jobs and
promote the region internationally, as is the case with Champagne, Bordeaux (wine) and Parma (ham).

27. 21
2.2.2 Composition of actors
In this section, we consider how a cluster is affected by its size, the functional proximity between
firms and whether the entire value chain of the constituent industries is included.
2.2.2.1 Absolute size and Density
When we consider the size of a cluster we need to acknowledge that size can either be absolute
(the overall size of the cluster) or relative (the size of the cluster compared to the size of the
region it is located in), and that size depends on the unit of analysis (number of firms, number of
employees, a financial measure such as total turnover, etc.). If we allow ourselves to consider
size in terms of number of firms we can make some inferences from our existing knowledge
about clusters.
We have previously established that firm specific benefits from clustering to a large extent stem
from the relations that exist between firms. If we consider the absolute size of a cluster we find
that as the number of firms increases so does the incentive and pressure for specialization. This is
based on the notion that firms competitiveness depends on their core competencies; as
competition increases firms must increasingly focus on activities that build on core competencies
and in this process outsource or shut down activities where they hold no competitive advantage.
Based on this observation we find it likely that large clusters (measured in number of firms)
display high specialization levels among the organizations. If this is indeed the case, firms in
large clusters are more likely to reap benefits than similar firms located in small clusters, as there
are many different suppliers and partners to chose from as well as competitors and related firms
to learn from.
But is there a threshold where efficiency declines? One such threshold could stem from mere
information overload caused by the many actors’ knowledge outflows. Bathelt et al. (2002) argue
that information in clusters is constantly subject to filtering processes so that each piece of
information which is transmitted face-to-face already has been filtered for relevance and
customized to the receiver. In this way mere size of a cluster, measured in number of firms, is not
perceived to constitute any disadvantage to the quality of knowledge flows, on the contrary; the
larger the cluster the better.
So far we have only considered absolute size, but we must not forget that clustering deals with a
concentration of interdependent firms, which in return makes the relative size important. Enright

28. 22
(1998) notes that the geographic scope of a cluster refers to the territorial extent of the cluster,
and accordingly divides clusters into localized clusters which “…are tight groupings found in a
small geographic area, often a single town” and dispersed clusters, which “…are spread across
wider geographies.” Acknowledging that relative size depends on the geographical scope allows
us to formally define relative size or density as the number and economic weight of firms in a
cluster compared to the cluster's geographical scope (Enright 1998)15
. Applying the concepts of
dense and sparse clusters to our existing knowledge, we find that increased density brings with it
a better ability to monitor and learn from others’ mistakes, paving the way for knowledge
spillovers (Maskell 2001). It allows for frequent interactions which foster trust and build social
capital. Yet, although dense clusters are from a theoretical viewpoint more apt to provide benefits
for cluster firms than sparse clusters, the comparison needs to include the absolute size also. This
is because specialization-levels in organizations, the number of potential trading partners,
competitors, institutions, etc. depend on the absolute size of the cluster. Consequently a dense,
but small cluster may not function better than a large and sparse cluster, while a dense cluster of
the same absolute size as a sparse cluster can be expected to function better.
Contrary to the absolute size of clusters, the density is subject to an upper limit with respect to
efficiency. Bekele and Jackson operate with two opposing forces, namely centripetal and
centrifugal forces. In essence the centripetal forces are the benefits from clustering we have
discussed above, while centrifugal forces, on the other hand; “…include immobility of labor,
increases in land rent and external diseconomies such as congestion and environmental problems
that develop within increased concentration” (Bekele and Jackson 2006). Thus, the benefits firms
obtain from industrial clustering have to be balanced against inevitable negative forces, such as
increasing land and labor prices. As clusters become more and more dense, negative externalities
increase in importance. The centrifugal forces described in the above quote are in most cases
equal to or dependent on the cluster’s frame conditions. In this way dense clusters put pressure on
scarce and immobile factors and cause them to be bid up. This in turn raises costs and ultimately
limits the attractiveness of the region.
15
Enright (1998) measures density in terms of market shares. However as we have already discussed density is
subject to the unit of measurement, and also depends on the unit of comparison, e.g. the cluster’s market share
compared to the world market or the cluster’s total employment compared to national employment. For consistency
and explanatory reasons we here consider it in terms of number of firms compared to a given region.

29. 23
The following figure shows that as a the concentration of firms becomes denser it initially works
in a positive direction, however, when density passes some cluster specific limit, negative
externalities set in and reduce the collective benefits from clustering (a cluster should be able to
produce more benefits as it moves from dark to white areas in the figure). From our earlier
discussion of relations in the cluster, we also know that as the number of relations increase, more
benefits should accrue to the cluster members. Finally, the absolute size of a cluster has an effect
on both axes, as it augments both the number of relations possible and it allows for higher
specialization-levels and in general raise the benefits from any concentration of firms. However,
a larger absolute size also increases the risk of subjecting the cluster to centrifugal forces as it has
a greater effect on the frame conditions in a specific area. The following figure shows a cluster,
which performs poor on all three dimensions and one that is optimally positioned in terms of
concentration of firms, relations and absolute size (size of the circle).
2.2.2.2 Breadth
According to Enright (1998) “the breadth of clusters refers to the range of horizontally related
industries (industries related by common technologies, end users, distribution channels, and other
non-vertical relationships) within the cluster. Hence “narrow clusters consist of one or a few
industries and their supply chains. Broad clusters provide a variety of products in closely related
industries” (Enright 1998).
The breadth of clusters presents a trade-off between commonality and diversity. More
specifically, this trade-off can be traced back to the different mechanisms pertaining to
knowledge flows to the cluster and within the cluster. Knowledge flows to the cluster increase as
the cluster becomes broader; with respect to benefits from knowledge accumulation and

30. 24
innovation, Feldman and Martin (2004) note that “diversity is important for innovation, and so as
a local economy becomes too dependent on one firm or one industry it may drive out new ideas.”
Feldman and Martin (2004) supplement this by stating “that diversity across complementary
economic activities sharing a common science base is more conducive to innovation than is local
specialization.” The optimal breadth depends on the specific cluster, yet we can conclude that as
breadth increases, so does the ability of the cluster to draw knowledge to it from different areas
and innovate in different directions. On the other hand, knowledge flows within the cluster
increase as the cluster becomes narrower; Malmberg and Maskell (2002) note that as diversity
(functional distance between firms) increases a shared culture will be more difficult to develop
and maintain. At the same time it becomes more difficult to constantly observe and interpret
other firms, which can be seen as a function of a firm’s absorptive capacity (Cohen and Levinthal
1990). This implies that firms’ ability to apply external knowledge is reduced when the cluster is
very broad because the external knowledge cannot spillover- nor be absorbed effectively. Enright
(1998) further questions the quality of very broad clusters, such as tradable business services,
engineering, technology, tourism and agriculture. A cluster at either of the two extremes with
regards to breadth has downsides, and the specific breadth of a cluster is thus an empirical
question, which depends on the nature of the industries involved, and several other factors.
Especially, we note that the efficiency in applying external knowledge (absorptive capacity)
depends not only on technological proximity between firms, but also on relational-,
social/cultural and geographical proximity between actors sending and receiving information.
2.2.2.3 Depth
“Cluster depth refers to the range of vertically related industries within the cluster” (Enright
1998). Deep clusters (or traded industry clusters, applying Monitor Group’s (2004) terminology)
contain nearly complete supply chains of an industry or a set of related industries, whereas
shallow clusters “are those that rely principally on inputs, components, equipment, technology,
and support services from outside the region” (Enright 1998).
Increasing depth leads to a larger number of potential trading partners. This in turn provides for
the existence of a dense web of vertical linkages, which can potentially produce pecuniary
benefits for the firms involved. At the same time these trade linkages can evolve into informal
linkages which facilitate knowledge transfers.

31. 25
Assuming first a fixed number of firms in a cluster reveals that as depth increases, breadth
decreases. This consequently results in a trade-off between the previously mentioned benefits
from depth, and those stemming from breadth, namely horizontal linkages in the form of co-
operation and knowledge spillovers. However, allowing the absolute size of clusters to vary
allows for a preferred distribution of breadth and depth. Consider the figure below for an
illustration (the size of the circle reflects the absolute size of the cluster).
2.2.2.4 Cluster archetypes
Based on type of firms and linkages, Markusen (1996) divides clusters into 4 different types;
Marshallian (Italianate) industrial districts, hub-and-spoke districts, satellite industrial districts
and state-anchored industrial districts.
Marshallian (Italianate) industrial districts are dominated by small locally owned firms, which
have limited economies of scale. These firms have a high level of intra-district trade based on
long-term contracts works. Largely the only linkages that exist with actors outside the cluster are
in the form of sales, which do not entail cooperation of any sort. Workers are committed to the
district and are highly flexible and mobile, allowing for a high degree of knowledge transfer
between the firms. Since workers are so highly committed to the region a strong culture develops,
which allows for the transmittance of tacit knowledge. In addition, trust between firms works to
promote frequent exchanges of personnel between customers and suppliers and causes
competitors to share risk, stabilize markets and share innovations. Excellent examples are the
Northern Italian industrial districts, hence the name.

32. 26
Hub-and-spoke districts are dominated by one or several large vertically integrated and
international firms (hubs),”…with suppliers and related activities spread out around them like
spokes of a wheel” Markusen (1996). Two forms exist. A strongly linked form in which the
smaller firms are highly dependent on the hub either as a supplier or as a market, and a nucleated
form in which the smaller firms enjoy the externalities created by the hub, such as specialized
factors in the form of skilled labor, infrastructure, research institutions, etc. Usually there is a
development from the former type towards the latter over time as entrepreneurs benefit from the
externalities created by the hub, but not necessarily from linkages to the hub. Hub-and spoke
districts may display cooperation, but generally on the terms of the hub. Cooperation among
competitor firms is strongly lacking. Strategic alliances on behalf of the hub occur mainly with
partners outside the district. Exchange of employees may take place, yet employees are mainly
loyal to the hub. This is also why the culture tends to develop around hub activities. Hub-and-
spoke district often lack specialized venture capital. Finally, dominant firms may be actively
involved in “…issues that affect their work force and their ability to do business – especially in
improving area educational institutions and the provision of infrastructure” Markusen (1996). All
in all hub-and-spoke districts are strongly dependent on the hubs and their presence in the region.
Examples are Toyota in Toyota City, USA or Boeing in Seattle, USA.
Satellite platform districts are dominated by branches of large and externally headquartered firms
and are often located in outer regions due to lower costs of doing business. Firms here enjoy
moderate to high level of economies of scale and have almost no linkages inside the area.
Consequently although satellite platform districts might look like clusters they certainly do not
function as working clusters, and therefore benefits from clustering cannot be observed.
Finally in state-anchored industrial districts public or non-profit organizations are central players
and function much like the hub-and-spoke district mentioned above.
The four types of cluster should not be seen as either-or types, but rather as arch-types that can be
blended and co-occur in clusters. With respect to linkages, it seems that the composition and
workings of Marshallian (Italianate) districts produce more cluster benefits than for instance
satellite platform districts which suffer from almost a complete lack of linkages. State-anchored-
and hub-and-spoke districts are likely to be found somewhere in between.

33. 27
2.2.3 Cluster development over time
Cluster development over time has thus far not been properly explored. We noted earlier that a
cluster might change from a latent to a working cluster and that Markussen’s archetypes might
develop over time. In this section, we adopt a dynamic view on clusters, and thereby explain
more in detail how and why clusters develop over time.
The number of ways in which researchers have tried to explain and generalize the growth
patterns of clusters reflects the fact that no two clusters are alike. Even the ‘birth’ of a cluster is
strongly discussed. Porter (2000a) argues that; “a cluster’s roots can often be traced to parts of
the diamond that are present in a location due to historical circumstances. One prominent
motivation for the formation of early companies is the availability of pools of factors, such as
specialized skills, university research expertise, an efficient physical location, or particularly
good or appropriate infrastructure”. Xu and McNaughton (2003) support Porter’s view and add
as an explanation; unusual local demand and the existence of related industries. Malmberg and
Maskell (2002) propose that “research on origin and developments of clusters usually find three
things; they often originate in a series of events leading to the start of a new firm at the place of
residence of the founder, they develop through spin-offs and imitation within the local milieu,
and they are sustained by various forms of inertia, meaning that firms rarely relocate once they
have been reproduced in a place.” Porter (2000a) supplements; “The early formation of
companies in a location often reflects acts of entrepreneurship not completely explainable by
reference to favorable local circumstances. These companies, in other words, could have sprouted
at any one of a number of comparable locations.” This supports the view that chance plays a
prominent role in describing the ‘birth’ of clusters (Rosenfeld 2002, Bekele and Jackson 2006).
Once established Porter (2000a) argues that “in a healthy cluster, the initial critical mass of firms
triggers a self-reinforcing process in which specialized suppliers emerge; information
accumulates; local institutions develop specialized training, research, infrastructure and
appropriate regulations; and cluster visibility and prestige grows. Perceiving a market
opportunity and facing falling entry barriers, entrepreneurs create new companies. Spin-offs from
existing companies develop, and new suppliers emerge.” Martin and Feldman 2004 note that this
occurs because “the cumulative nature of innovation manifests itself not just at firm and industry
levels, but also at the geographical level, creating an advantage for firms locating in areas of
concentrated activity. These factors can generate positive feedback loops or virtuous cycles, as