Networked. How networked business can bring agility and innovation across organizational boundaries in the knowledge industry.

NETWORKEDHow networked business can bring agility and innovation
across organizational boundaries in the knowledge industry.
by Sylvain GRISOT
Supervisor: Christine NASCHBERGER, AUDENCIA Business School, Nantes.
Master Thesis Submitted in Partial Fulfilment of the Requirements for
the Degree of European Master of Business Administration (EuroMBA)
Maastricht University School of Business and Economics (Netherlands)
IAE Aix-Marseille. Graduate School of Management (France)
EADA. Escuela de Alta Dirección y Administración (Spain)
HHL Leipzig Graduate School of Management (Germany)
AUDENCIA Business School, Nantes (France)
Kozminsky University (Poland)
2016

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For Lise and our sparkling children, Soline and Mayeul.
Acknowledgements
No fewer than twenty-eight people gave me hours of their time to answer my strange questions.
They cannot be cited here, but all must be warmly thanked. I hope this work will somehow help them.
Christine Naschberger must also be congratulated for her complete availability and her useful
comments on my work, Stuart for his yet-to-be-solved questions, and Diana Berdún Mingo for all
those things that make EuroMBA such a specific time in a life.
The whole A47 intake (A is for Anarchy) made this journey possible. I thank them greatly for their
support, and all the other things that cannot (thankfully) be mentioned here: Wikke Tuinhout,
Marjorie Testaniere-Gazado, Jennifer Pechloff, Stefan Hermanns, Bernd Stolzenberg, Nick Ryan, Paul
Murphy, Patrick Horstmann and Thomas Jung.
Emilie Ruprich-Robert, as an intern in my company, and Amy Cheshire, who corrected this document,
also took an active part in this work—thank you for that.
I have also to thank those who are building kumu.io, an amazing tool which I used extensively for this
project: Jeff and Ryan Mohr. They took the time to help me on different occasions, between surf
sessions, from their office in Hawaii.
Abstract
Networked business, defined as a state in which an interconnected system of people from different
organizations are working toward one or more common objectives, is an increasingly common way
of managing projects. This research is based on literature analysis and fieldwork, including mapping
of networks of business partners and interviews of people involved within networked businesses.
This work allows to precise that networked business is most adapted for innovative projects in the
knowledge industry, requiring team with diverse expertize. This research also makes it possible to
identify some practical guidelines for building a network of partners, managing projects as networked
business, and enabling learning and growth in this configuration.
Keywords: network, networked business, partner, partnerships, knowledge industry, agility,
innovation.

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Executive summary
Networked business, defined as a state in which an interconnected system of people from different
organizations are working toward one or more common objectives, is an increasingly common way
of managing specific types of projects. Like in the ancient “Hollywood model”, people from different
organizations group together as a team for a specific project, then disband at the end, without any
formal stable organization.
This thesis is therefore focused on networked businesses, with a few main research questions:
• What is a networked business and what makes this kind of business organization special
compared to traditional companies?
• In what conditions is a networked business gathering people from different organizations
more adapted than working within a single company?
The objective of this work is a practical one: to identify the conditions where networked businesses
can be efficient, and define some guidelines for building, managing and developing a networked
business.
The research process also makes it possible to test a lightweight methodology for network
exploration that could be useful for other research on networks, or for professionals who work with
loosely defined groups of players, organized in networks.
The empirical research work involved three different stages:
• The first stage was focused on literature analysis, and allows me to propose a first definition
of a networked business, to understand how a network works based on academic work, and
provide some background on the changes in the business world that tend to make networks
more interesting than ever. This stage was also an opportunity to propose a number of
hypotheses.
• The second stage involved extensive fieldwork, with the analysis of two networks. One on
those networks involved traditional businesses in the boating industry, whereas the second
one was focused on players working as networked businesses in the knowledge industry. This
stage required data collection from 25 business owners or managers involved in those two
networks, to represent and analyse these networks from a quantitative perspective, as well
as a more qualitative approach to complete the set of hypotheses to be tested in the last stage
of this research.
• The third stage was focused on the validation of the 11 hypotheses build during stages 1 and
2 through literature analysis and field work. An interview framework was organized around 4
key subjects. Nine interviews with key people highly involved in networked businesses were
conducted to validate or invalidate hypotheses, and to establish when this type of
organization is most valuable, how a network should be built and managed, and how it could
be an alternative to growth for organizations.
This work allowed to define several guidelines that could be practically applied by those wanting to
actively manage their involvement in networked business:
1. Working as a networked business is not efficient in all situations. Gathering people from
different organizations works best for non-repetitive projects in the knowledge economy,
where no shared tangible assets are required, and only an interdisciplinary approach can allow
the expected level of innovation to be reached.

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2. Maintaining a network of partners is time consuming, but new contacts can be efficiently
created and are tested during early business development stages, either via contacts of
existing partners or through professional associations or events.
3. To gain in efficiency and allow collective learnings, long-term relationships are required.
Networks of partners should therefore be managed by individuals as a personal asset, and
organized as dense clusters of close partners for efficiency, connected by brokers to the rest
of their network to maintain innovation.
4. Besides some practical requirements, working as a networked business requires some
common principles to be shared, such as: a common goal, the priority given to the common
interest, autonomy of team members and the transparency of exchanges.
Networked businesses may become an important form of organization in the future, with an
increasing need to manage both innovation and agility to adapt to fast market changes. This offers a
real chance for individuals that have expertise to share to be involved in projects with the denser
parts of their networks, sharing business as well as experience. The need for stable clusters could also
give a fresh view of the role of organizations within networks. Open companies could act as
facilitators between network members, providing a common ground (and acting as network
orchestrators) for people from different organizations to collaborate efficiently on projects.
This renewed version of the “Hollywood model” will definitively have a future, at least for those who
can develop highly personal expertise, based on implicit knowledge and creativity.

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Table of contents
Acknowledgements .................................................................................................................................... 2
Abstract ......................................................................................................................................................... 2
Executive summary..................................................................................................................................... 3
Table of contents......................................................................................................................................... 5
Table of illustrations ................................................................................................................................... 6
Introduction .................................................................................................................................................. 7
Context ...................................................................................................................................................................7
Subject & objectives ............................................................................................................................................7
Methodology & thesis organization .................................................................................................................8
1. What is a networked business?........................................................................................................... 9
1.1 What is a network? ............................................................................................................................................9
1.2 A (temporary) definition of networked business...................................................................................... 10
2. How does a network work? ...............................................................................................................13
2.1 Brokerage and closure.................................................................................................................................... 13
Brokerage initiates value creation ................................................................................................................. 14
Closure is required to create value................................................................................................................ 14
2.2 The power of trust........................................................................................................................................... 14
2.3 Solving the tension between brokerage and closure .............................................................................. 15
2.4 Does size matter? ............................................................................................................................................ 16
2.5 Data and graphical analysis of networks.................................................................................................... 17
Graphical representation & analysis.............................................................................................................. 17
Data analysis and network metrics ............................................................................................................... 18
3. Context: Are networks becoming alternatives to traditional organizations? ........................20
3.1 Companies struggle to adapt to new mind-sets....................................................................................... 20
3.2 The end of the office as a knowledge factory .......................................................................................... 21
3.3 The rise of the freelance economy and the porous borders of companies........................................ 22
3.4 Is a new era coming?....................................................................................................................................... 23
4. Building hypothesis: An analysis of two networks .......................................................................24
4.1 Methodology .................................................................................................................................................... 24
4.2 Network 1: Exploration of the boating industry in Nantes.................................................................... 25
Data collection process.................................................................................................................................... 25
Results.................................................................................................................................................................. 25
Methodology improvement ............................................................................................................................ 28
4.3 Network 2: A deeper exploration of a larger network............................................................................ 28
Methodology ...................................................................................................................................................... 28
Collected data .................................................................................................................................................... 29
Specificities of the knowledge industry ....................................................................................................... 30
The role of organizations................................................................................................................................. 32
The power of brokers: creating opportunities............................................................................................ 35
The real nature of connections: trust & collaboration.............................................................................. 37
Working, learning and innovating together................................................................................................. 38
Limitations and methodology improvement ............................................................................................... 39
5. Testing hypotheses: How can a networked business perform?................................................41
5.1 Methodology .................................................................................................................................................... 41
A qualitative approach...................................................................................................................................... 41
Hypotheses to be tested.................................................................................................................................. 41
Interview framework ........................................................................................................................................ 42
5.2 When is a network business valuable?....................................................................................................... 42
Networked business is most valuable for part of the knowledge industry.......................................... 43

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The main value of networked business is to bring innovation................................................................ 43
Networked business is relevant for both individuals and large organizations .................................... 44
Networked business does not fit any team................................................................................................. 44
5.3 How do you build a network?....................................................................................................................... 45
Indirect contacts are critical for building new relationships.................................................................... 45
Professional organizations and events tend to be more and more important.................................... 46
Relationships needs to be managed.............................................................................................................. 46
5.4 How do you work as a network? ................................................................................................................. 47
Practical requirements for working as a networked business................................................................. 47
The importance of shared values................................................................................................................... 48
The need for a specific kind of project management................................................................................ 49
5.5 How do you grow as a network? ................................................................................................................. 49
Learning as a network is a critical issue........................................................................................................ 49
Network as an alternative to growth............................................................................................................ 50
Conclusions: Best practices for networked businesses ..................................................................52
References ..................................................................................................................................................54
Books......................................................................................................................................................................... 54
Articles & online sources....................................................................................................................................... 54
Table of illustrations
Illustration 1: Graphical representation of a simple network (By the author) ..................................................9
Illustration 2: The “cloud model” (Source: Dilbert)............................................................................................... 10
Illustration 3: Different types of networked organizations (Source: Miles & Snow, 1992)........................ 11
Illustration 4: Brokerage & Closure (By the author)............................................................................................. 13
Illustration 5: Sociogram of a class (Source: Moreno 1936, p 35).................................................................... 17
Illustration 6: Modern view of the same sociogram (Source: Granjean, 2015) ............................................. 17
Illustration 7: Three different representations of the same dataset (By the author)................................... 18
Illustration 8: Degree, closeness and betweeness (By the author) .................................................................. 19
Illustration 9: Network 1 - All elements and connections (By the author)..................................................... 26
Illustration 10: Network 1 - Local elements and connections (By the author).............................................. 27
Illustration 11: Network 2 - The entire network (By the author) ..................................................................... 30
Illustration 12: Network 2 - Main activities (By the author).............................................................................. 31
Illustration 13: Network 2 - Size of organizations (By the author)................................................................... 32
Illustration 14: Network 2 - Network position of employees of CorpX (By the author) ............................ 34
Illustration 15: Network 2 - Index of two employees of CorpX (By the author) .......................................... 34
Illustration 16: Network 2 - Details on connections of interviewees (By the author)................................. 35
Illustration 17: Network 2 - Betweeness data (By the author) ......................................................................... 36

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Introduction
“The city wasn’t a skyline – it was a dance.”
Lehrer, 2010
Context
Stuart Dixon, as Director of the EuroMBA, has a very hard time trying to enforce a simple rule for
students of the program: never either get married, have a child or change of job during the MBA.
While my youngest child was already six months old when I started this journey, I have to admit that
I broke that rule, as I quit my job in the middle of my two years of study. Worse than that, it was not
for a new position, but to start my own company: dixit (www.dixit.net).
The fact that I decided to start an entrepreneurial journey has not been a surprise for anyone around
me. More surprising, however, was my decision not to grow, and to remain alone in the company.
Because yes, I am addicted to business development (my former position), and I am known for that.
However, I founded ‘dixit’ as a prototype of a networked business — a company where I would stay
officially alone, but always working with partners that vary according to the projects in hand.
After a year in the business, I can say that this works. Clients have agreed to sign contracts with teams
spanning across small and large organizations, gathering different experts according to their needs.
Projects are delivered on time with a high level of client satisfaction, and we are also rather efficient
economically.
Yet it is still difficult to define the way we are working for outsiders (I usually refer to myself not as
an independent worker, but as a dependant one, since I am voluntarily dependant on my network of
partners), and this kind of collaboration raises many an issue over the course of projects.
Subject & objectives
This thesis is therefore focused on networked businesses, with a few main research questions:
• What is a networked business and what make this kind of business organization special
compared to traditional companies?
• In what conditions is a networked business gathering people from different organizations
more valuable than working within a single company?

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The primary objective of this work is a practical one, for me as a business owner as well as more
broadly for any people involved in networks in their daily professional life: what are the best practices
for building and managing a networked business?
The research process also aims to test a lightweight methodology for network exploration that could
be useful for other research on networks, or for professionals who work with loosely defined groups
of players, organized in networks.
Methodology & thesis organization
The methodology I used for this research project might seem somewhat unusual for an MBA thesis,
but this is closely linked to my personal background. As a former student in political sciences and
anthropology, and later as a professional working more in the field than in offices, I am more used to
qualitative methods than quantitative ones. I am more capable of detecting weak signals during
interviews than extracting main trends from quantitative surveys.
From my studies in anthropology, I also used the participant observation approach, where the
observer is deeply embedded in the group and focuses on building trust and being able to observe
discrete facts, assuming the fact that his involvement can introduce some bias. This methodology can
be defined as a: “process of establishing rapport within a community and learning to act in such a way
as to blend into the community so that its members will act naturally, then removing oneself from the
setting or community to immerse oneself in the data to understand what is going on and be able to
write about it.” (Kawulich 2005). This distinction between the data collection process and its analysis
was important here, as I am a member of one of the groups observed and also the observer. This
allows me to gain access to people and data that are inaccessible to outsiders, but required some care
in the data collection process and analysis to mitigate bias and maintain objectivity.
This research empirical work implied three different stages that are reflected in the organization of
this document:
• The first stage was focused on literature analysis, and allows me to propose a first definition
of a networked business, to understand how a network works based on academic work, and
provide some background on the changes in the business world that tend to make networks
more interesting than ever. This stage was also an opportunity to propose a number of
hypotheses.
• The second stage involved extensive fieldwork, with the analysis of two networks. One on
those networks involved traditional businesses in the boating industry, whereas the second
one was focused on players working as networked businesses in the knowledge industry. This
stage required data collection from 25 business owners or managers involved in those two
networks, to represent and analyse these networks from a quantitative perspective, as well
as a more qualitative approach to complete the set of hypotheses to be tested in the last stage
of this research.
• The third stage was focused on the validation of the 11 hypotheses build during stages 1 and
2 through literature analysis and field work. An interview framework was organized around 4
key subjects. Nine interviews with key people highly involved in networked businesses were
conducted to validate or invalidate hypotheses, and to establish when this type of
organization is most valuable, how a network should be built and managed, and how it could
be an alternative to growth for organizations.
This work allowed to define several guidelines that could be practically applied by those wanting to
actively manage their involvement in networked business.

- 9 -
1.
What is a networked business?
1.1 What is a network?
With the explosion of the presence of social networks in our daily life, the definition of a network
may seem obvious, but it is not. As we will see later, graphical representations of networks are
required to help us understand such complex systems. Let us therefore start with a drawing:
Illustration 1: Graphical representation of a simple network (By the author)
There is no common agreement describing the different ingredients that compose a network. I
therefore choose to focus on only some of them, based mainly on academic literature on network
analysis (Burt 2007).
All grey dots in illustration 1 can be defined as elements, linked by connections (lines). It is in fact the
existence of these connections that define the network itself, as a set of relations between elements
(Halévy 2014). The blue dot is therefore simply not part of the network, in the absence of any
connections with other elements.
These sets of elements and connections create different patterns depending on network
composition, with areas of higher density of connections between elements that we will define as
clusters. The spaces between clusters may appear empty, but in fact play a critical role in networks

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that will be analysed later. We will use Ronald Burt’s concept of structural hole (2007) to define them,
and connections that span across these structural holes (like the red one in illustration 1) will be
referred to as bridges. The importance of these bridges will be fully explained in the following pages.
1.2 A (temporary) definition of networked business
Based on this first description of networks, let us build a first definition of networked businesses, at
the core of my analysis. This definition will be refined later, based on field work results.
Business organizations have taken many forms in history, and are increasingly diverse. There is a
somewhat desperate search for the ideal organization in many companies, but despite the high level
of diversity of these organizations, most fit within three major models (Miles & Snow 1992).
The functional organization is the early stage of modern firms, but remains the model of many
organizations. Built for efficiency, it is a special-purpose organization that is centrally organized and
highly integrated (Miles & Snow 1992). Divisional organizations appeared later and grew after the
Second World War, with products divisions that operate as nearly autonomous companies (Miles &
Snow 1992). This model is in fact a simple evolution of the functional model, which adds different
functional divisions within the same firm, with centralized performance evaluation and resource
allocation.
Matrix organization appeared later. This model is much more complex and diverse, and tries to mix
both previous models, with permanent coordination between functional departments and product
teams, or project sub-organization and regional locations. Introducing complex (and sometimes
messy) organization charts with multiple reporting relations for the same position (Stamps & Lipnack
2008), this model appears to be a mostly unsuccessful attempt to manage complexity within firms
struggling with global localization, more complex project management and faster evolving markets.
Illustration 2: The “cloud model” (Source: Dilbert)
Since the 1980s, the landscape is even more diverse, with many new and trendy ways of defining the
growing complexity of business organizations. A growing number of businesses tend to be organized
as networks, with stable or more dynamic relations between suppliers, producers and distributors
working together. This trend is linked to a more fluid perception of firms’ boundaries and the
disaggregation of vertically integrated structures in favour of loosely coupled external components
(Miles & Snow 1992). It also gives more importance to contracts, alliances and agreements as opposed
to plans, schedules and transfer prices.

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But can networks by themselves be considered as new organizational models? Raymond Miles and
Charles Snow (1992) tend to think that they are the fourth model of organization, but the three types
of networks they describe are so different that this definition of network organization seems too
broad to be useful. For them, networked organizations include three different types:
Stable network Internal network Dynamic network
Illustration 3: Different types of networked organizations (Source: Miles & Snow, 1992)
A Stable network is defined as a network involving a core firm with a limited number of upstream or
downstream partners (suppliers or distributors for example), with long-term market-based links
between them. This type of organization seems to be more an evolution on the vertically integrated
functional firm than a networked organization specifically. The long-term relationship certainly
involves trust and mutual respect (as opposed to short-term interest-based relations), but maintains
a high level of dependency on the core firm, and is not by itself network specific. The internal network
would be an evolution on the matrix organization, doing away with administrative coordination
processes and internal transfer prices in favour of buying and selling relationships based on market
prices of autonomous units within the same firm (Miles & Snow 1992). This type of networked
organization does not fit either with the objectives of this study, which focuses on relations that span
across organizational boundaries. The last type of network, dynamic networks, seems more useful
for this study, as it is defined as a dislocation of traditional organizations in clusters of independent
potential partners that make temporary alliances for short term contracts (Miles & Snow 1992).
These three types of “networked organizations” are therefore too different to constitute a proper
definition. Even the definition of dynamic networks needs to be refined to be useful for the following
work, but this approach provides a better understanding of what the “networked business” at the
core of this research is and is not:
• I wish to focus only on networks involving different organizations. This definition should
therefore exclude “internal networks” as previously defined, and focus only on business
relations between organizations. I will therefore keep only the term “networked business” in
this study, and, in order to exclude ambiguous terms such as “networked organization” or
“connected company” frequently used in academic or managerial literature, will define it as
being focused mainly on the evolution of existing firms toward more open and agile models.
• A networked business should therefore not be defined as a stable organizational model, but
as a temporary state of collaboration between two or more autonomous organizations. This
is close to the definition of “dynamic networks” by Miles & Snow (1992), but excludes “stable
networks”.

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This dynamic is at the core of what Dave Gray (2012) defined as a “connected company”: “(…) a
complex, adaptive system that functions more like an organism than a machine”, and is consistent
with the definition of networked business proposed by Larry Hawes (2012), which I will embrace
temporarily:
“(A networked business is) a state in which an interconnected system of organizations and
their value-producing assets are working toward one or more common objectives.”
This definition underlines some key aspects:
• Networked Business is a temporary state of relations between different entities, and not an
internal organizational model or a stable set of connections between different business
partners.
• The absence of centralized power within a network involves the existence of a particular
mind-set of collaboration, where different organizations accept to cooperate for their mutual
benefit with a state of interdependency between them.
• These temporary relations are initiated by a “common objective”, focusing this networked
approach on projects with a clear beginning and end. For Marc Halévy (2014), this common
objective should be understood as the “real leader” within a network, as no other explicit
regulation exists. This tends to focus networked business only on project-based work.
• At this stage we have identified no limitations on the type of organizations involved in these
networked businesses.

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2.
How does a network work?
2.1 Brokerage and closure
Two powerful concepts of “brokerage” and “closure”, introduced by Ronald Burt (2007), are especially
useful to understanding value creation within a network. He focused his research mainly on networks
within organizations, however his analysis can also be useful to understand how a network involving
different organizations works.
His work is based on the analysis of flows of information within networks, defined as “an information
Polynesian which the clusters are islands of opinions and behaviour.” (Burt, 2007, p. 15) Within
clusters, information tends to be homogeneous, and the closure effort tends to limit variation
between the group, implementing an invisible barrier between insiders and outsiders. On the
contrary, brokerage is about variation increase, creating bridges between clusters above structural
holes, where information is more diverse.
Illustration 4: Brokerage & Closure (By the author)

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Brokerage initiates value creation
For Ronald Burt (2007), brokers who stand between structural holes are in a strategic position that
allows them to initiate value creation through innovation. Located at a crossroad, they have early
access to a wider variety of information from different clusters, and can diffuse information, best
practices, or initiate synthesis better that people located only at the core of a dense and close cluster.
Brokers can be seen as creative, but in a narrow definition of creativity where new ideas are mainly
old ones brought from one group to another (Burt, 2007). This pragmatic vision of innovation, as a
process of connection between disconnected pools of ideas from different markets, industries,
geographical locations or business units, gives brokers a crucial position. They are able to draw
analogies between different worlds and to use old ideas as powerful solutions to new problems
(Hargadon & Sutton 2015).
Within firms, brokers also act outside of the strict organization chart, and create bridges between
separated parts of the organization. They also play a critical role to maintain contacts with outsiders.
They can be highly efficient at gathering and disseminating information and their critical role during
a change process is often underestimated (Cross et al. 2007).
In this sense, brokerage can be seen as the exact position of some “network entrepreneurs” (Burt
2007). Brokerage is a risky investment in relations that could lead to rewarding outcomes that are
based on a form of trust before any real social or personal precedent. In this sense it is an initiative
required to create value through new connections and innovation. But if it initiates value creation,
closure is required to deliver it.
Closure is required to create value
Where brokerage accentuates information diversity, closure reduces it and amplifies strong relations
within the network to facilitate collaboration within a cluster (Burt, 2007). Closure is essential for
building efficient and long-term relations, allowing a group of people to be involved in a continuous
improvement process, in which they learn together how to be efficient (Burt, 2007). In closed
networks no behaviour goes unnoticed, reducing the risk of inconsistent beliefs or behaviour, and
implementing alignment through social control. This decrease in variation within the group is a
necessary step in order, in the long run, to generate trust between people, which is required for an
efficient collaboration.
Companies are in themselves an efficient way of implementing closure, but within networked
business gathering people from different organizations, this effect would tend to be less powerful, in
favour of stronger brokerage. Following Ronald Burt’s (2007) analysis, networked business would
therefore be more valuable in more innovative and less routine work, where uncertainty is important
and where there is no prescribed way of doing the work. Routine work would require more closure
to implement formal procedures that would lead to efficiency. Based on this, I will build a first
hypothesis to be tested later:
Hypothesis 1: Networked business is more valuable for innovative and/or non-repetitive projects than
for traditional business.
2.2 The power of trust
Both brokerage and closure effects are trust based. Brokerage requires trust to initiate relations, even
in the absence of precedent, and closure is an efficient way of reinforcing this trust in the long run.

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But what exactly is trust? If we define it as “a psychological state comprising the intention to accept
vulnerability based upon positive expectations of the intentions or behaviour of another.” (Rousseau
et al. 1998, p. 395), we clearly see that brokerage is related to risk acceptance (of non-expected
behaviour of the other), and closure about the reduction of this risk.
Trust is, in this sense, clearly “the Achilles heel to the brokerage argument” (Burt 2007, p 162), as
brokerage is all about committing to new relations before knowing how the other person will behave,
in the absence of precedent. This risk is even more important for non-repetitive tasks, where if the
objective may be clear, the process is not defined in advance. In this case, the terms of the relation
cannot be precisely specified in advance, increasing the risks involved.
Some may be tempted to mitigate this risk with legal contracts, but while a legal framework can be a
useful tool in a relation, it does not implement the trust required for collaboration. It could even have
the opposite effect, as reminded by a businessman quoted by Ronald Burt (2007, p. 94): “You can
settle any dispute if you keep lawyers and accountants out of it. They just do not understand the
give-and-take needed in business.”
In fact, all relations within the network exist only if trust is constantly renewed (Halévy 2014)
between its connected elements. No one tool alone can enforce this trust in the long run, and, on the
contrary, commitment to the relationship should be explicit, and the freedom to withdraw ensured
(Miles 1992). On this basis, I propose a new hypothesis:
Hypothesis 2: To be sustainable, relationships between parties in a networked business must be based
on an explicit commitment, and ensure the freedom to withdraw. Legal constraints cannot create the
required trust.
We see that networked businesses are in this sense based on fragile and evolving relations. The
relation is permanently put at risk by the behaviour of the partners, in the absence of a way to
maintain the relation other than trust, any misbehaviour leads to a breakage in the relationship. This
puts a crude light on traditional organizations, and on the destructive effect of such misbehaviour
within companies where a strong closure effect is implemented, and where relations must last (due
to the appurtenance to the same organization) even if they are inefficient or even destructive (Burt
2007). In a networked business, relations are highly visible and transparent (Miles 1992), as they are
not taken for granted, and can be interrupted anytime in the event of misbehaviour. This would
therefore have a very powerful effect:
Hypothesis 3: As business partners have the ability to withdraw from the relationship, there are no
lasting negative relationships within networked businesses.
2.3 Solving the tension between brokerage and closure
This exploration of the concepts of brokerage and closure shows what could seem to be an
irreconcilable opposition between these two effects: brokerage is about building new valuable
relations, which is risky in the absence of trust built on precedent, and closure is about trust
implementation by social control within existing relations. In this sense, how can a network be

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organized to both expand relations in order to initiate value creation by innovation, and at the same
time deliver this value efficiently?
Ronald Burt (2007) suggests the concept of structural autonomy to reconcile both aspects and
ensure value creation: “A structurally autonomous group consists of people strongly connected to
one another, with extensive bridge relations beyond the group” (Burt 2007, p. 165). Around a cluster,
where closeness implements strong relations and alignment required for efficiency, brokers initiate
bridges with other parts of the network to initiate innovation.
Expanding this idea further, building an efficient network would therefore require:
• Contrast of density within the network, with denser clusters separated by structural holes,
and not only a homogeneous set of equal connections between elements.
• Two different types of elements within clusters, with some closely connected as a core, and
others at the border acting as brokers with other parts of the network.
Based on that, we can suggest a hypothesis:
Hypothesis 4: Value creation is maximized in a networked business acting as a structurally autonomous
group, where both brokerage and closure are implemented by different people.
2.4 Does size matter?
If working as a network is all about collaboration in the absence of centralized power or hierarchical
organization, does the number of players in the game represent an issue? Moreover, is there an upper
limit to the number of people that should be involved in collaboratively managed projects?
There are good theoretical arguments in favour of this. Analysis of social networks has shown that
with the increase in the number of connections in platforms such as Facebook, relationships tend to
be less personalized (Guillaud 2016), and could be less favourable to collaboration. Marc Halévy
(2014) also pointed out that networks would appear to have an optimal size that should not be
exceeded to maintain efficiency.
Jean-Michel Cornu (2016) proposed a more detailed analysis, based on the observation that while
small communities of less than 150 people could be managed by one person, larger human groups
require some forms of hierarchical organization to work properly. He based his analysis on that fact:
one person could not be involved in more than 150 relationships efficiently. In a collaborative group
with no leader, where everybody should not only interact with all the other group members but also
keep track of the relationships between them, the upper limit should therefore be 12 team members,
involving them in a maximum of 144 relationships (12x12). At this scale of collaboration, all team
members are observed by the others, and non-hierarchical collective control means that there should
be no free riders.
Based again on observations, any group of more than 12 people—in the absence of efficient social
control or centralized power—would be constrained by the “90-9-1 rule”: 1% act proactively, 9%
react, 90% follow (Cornu, 2016). This would explain why groups between 12 and 100 members are
so difficult to manage: in groups of more than 12, free riders would remain unnoticed, and in those
with as many as 100 members, less than 10 people would really be involved (Cornu, 2016).

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Based on these analyses, it appears that there may be an upper level for networks or project teams
to maintain efficiency in the absence of centralized power. Based on this, I can suggest a hypothesis,
which will be further tested and refined later:
Hypothesis 5: Networked business is more efficient with projects that can be managed by teams of less
than 12 people.
2.5 Data and graphical analysis of networks
Graphical representation & analysis
Networks are complex datasets, especially when the number of elements and connections increase,
and tools and graphical representation are required to analyse them.
The first graphical visualization of connections between people, was developed by Moreno, in 1936,
to analyse relations between pupils in schools. Moreno’s sociograms (1936) allowed him to analyse
the network composed by relations between pupils, introducing a new way of representation and
analysis, which have evolved since that time, although still use the same principles.
The hand-drawn method of Moreno induces some bias, with the arbitrary separation of girls (circle
on the right in illustration 5) and boys (triangles on the left) for example. Computerized
representations now allow us to automatically position nodes depending on the individuals’ relations,
and to use graphical differentiations (such as colour, size, etc.) that make sociograms more easily
readable. Illustrations 5 and 6 below show the same dataset represented in its original version on the
left, and in an updated form produced with a modern computer tool (Gephi) on the right (Illustration
6).
Illustration 5: Sociogram of a class (Source:
Moreno 1936, p 35)
Illustration 6: Modern view of the same
sociogram (Source: Granjean, 2015)
These figures show that visual representation should simply be considered as subjective. Depending
on the tools and parameters, the same data could result in very different visual representations,

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producing possibly contradictory analysis. The following three drawings (Illustration 7) again
demonstrate this issue.
Illustration 7: Three different representations of the same dataset (By the author)
These drawings where produced with an online tool, kumu.io, which I used extensively for this
research project. With data describing both elements and connections between them stored in a
database, this tool allows us to automatically represent networks based on different criteria and
representation parameters.
The two sociograms on the left presented in illustration 7 are based on the same dataset and
represented using the same tool with the same parameters, however the automatic positioning led
to different graphical rendering, which could lead to a different interpretation. The last one on the
right re-uses the same dataset, but introduces slight changes in the representation parameters of the
same tool, which results, again, in another visual result. Using the same data, three different
representations were therefore produced.
Over the following pages, it should be kept in mind that while visual representation is required to
analyse networks, it is itself already an interpretation and not an objective representation of the real
situation.
Data analysis and network metrics
Computerized analysis allows us not only to represent, but also to measure different metrics based
on datasets. These metrics are useful for analysing positions of elements within a network. I will use
three main indicators in this study, to qualify the position of elements:
• Degree, which is the number of connections an element has with other elements. This allows
us to evaluate the ability of each node to interact with other nodes (Grandjean 2015). In a
networked business, a high degree means that an individual (as a node) is able to directly
connect with a high number of other people. However, the number of connections is not in
itself an indicator of the importance of an element: connections can be of poor quality, and
the element could be well connected, but in an isolated area.
• Closeness shows how easily an element is able to connect with others. A short distance (and
fewer ‘hops’) between two elements means that interaction between two individuals should
be easier. This is a good indicator of the ability of an element to spread information within the
network, but also of the visibility it has on what is happening within the network (Grandjean
2015).

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• Betweenness indicates the degree to which an element forms a bridge between elements.
The value of betweenness is therefore a measure of the ability of an element to connect two
separate clusters of the network, and therefore the power he has to block or grant access to
others (Grandjean 2015).
Illustration 8: Degree, closeness and betweeness (By the author)
As we will see, the use of these metrics is highly dependent on the way raw data is collected. There
is of course an issue with data consistency, but the stronger issue is that, in reality, networks usually
never end; the dataset is therefore a picture of only a part of the network, that accessible through
the data collection process, distorting measures in the absence of a complete view.
Therefore, these measures could only be used fully to analyse closed networks, which is certainly not
the case here. I took this fact into account in this research project, deploying a methodology which
always uses quantitative data in connection with a qualitative approach.

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3.
Context: Are networks becoming alternatives to
traditional organizations?
3.1 Companies struggle to adapt to new mind-sets
This is certainly an over-quoted survey by Gallup, but its results are still striking: in the 142 countries
studied, 13% of employees are engaged at work (Crabtree 2013). Only one in eight workers are
committed to their job, and are willing to make a positive contribution to their organization. There
must therefore be something wrong somewhere.
Historically, the existence of companies as the main organizational tool for business is directly linked
to efficiency issues: reducing transaction costs by internal management, companies are “more
effective than the sum of their parts” (Jessop 2016). In this logic, companies have traditionally
adopted a hierarchical model, as pyramidal organization is the best way to limit the number of
relations and reduce associated management costs (Halévy 2014). However, the world has changed,
and “volatile demand trajectory, complex interdependencies across channels and ambiguous
alternatives are not business as usual for enterprises optimized for efficiency.” (Sachs & Kundu 2015)
Efficiency is no longer the only issue for companies struggling for their existence in a fast-changing
world.
Digital technology has drastically reduced the cost of communication between parties within
companies, but also with outsiders. It has also given workers direct access to knowledge and
information, eroding the roots of the hierarchical system and managerial control (Powell & Snellman
2004). The lack of alignment of employees belonging to Generation Y (born between the early 1980s
and mid 1990s) is certainly more due to this digital revolution and its consequences on the traditional
system of legitimization of hierarchical power, than to any rejection of work itself (Duez 2015). And
where Gen Y questions hierarchy based traditionally on knowledge and information monopoly within
companies, qualified graduates from the younger Gen Z (born after the mid 1990s) would question
the existence of companies themselves, and massively declare that they want to become
entrepreneurs and not employees (Duez 2015).
It is a matter of fact that employees increasingly want to (and have to) take control of their career,
and decide what they do and when. For 29% of them in China, Germany, India, the UK and the US,
this control is the first thing they expect from their work (PwC 2015). More than a technological

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change, this is a cultural one that could be a key explanation for the decline in employees’
engagement, and companies will not be able to adapt to it just “by bringing in a couple of football
tables and a fridge full of snacks into the office” (Sachs & Kundu 2015). People are now questioning
the basics of work organizations within companies (especially when group work is associated with
splitting work in parts, executed following formalized processes), in a search for contact with reality
and direct accountability (Crawford 2010). It is also a need for recognition of experience and implicit
knowledge, when formalized knowledge tend to be communalized and automated (Crawford 2010).
Many organizations have tried to adapt their organizations to these changes, becoming closer to a
networked model, however this model is in complete contradiction with the hierarchical one, based
on the monopoly of bottom-up connections by one (Krebs 2003). Companies have therefore to adapt
not to market changes, new generations or organizational issues, but to a shift in the world from
subordination and hierarchical power, to collaboration and horizontal networks (Duez 2015) and a
requirement to permanent innovation.
3.2 The end of the office as a knowledge factory
Digital tools that allow remote communication and collaboration, have also triggered a shift in the
material organization of companies. Offices are still mostly designed as the traditional ‘knowledge
factories” (Heinemeier & Fried 2013) for white collars: a common place and time to use and share
required material assets such as network access, meeting rooms and coffee machine. This implies
that all resources are still mostly designed to be shared only with people from within the
organization’s offices.
But while offices can take decades to adapt, the way people are working has shifted in just a few
years. Mobility led to ubiquity, and employees now collaborate internally without sharing the same
space. Common time also expanded with longer and more diffuse hours of work, with mobile devices
spreading work within personal times.
Adaptation to these new practices allowed by digital tools is taking many forms, from expanded
access to digital resources from any place and device, to flexible and anonymous desks within offices,
development of shared offices or remote working. These changes in the material organization of
companies are currently having a number of different effects:
• When a company provides a high level of services to employees such as cloud data for remote
access, flexible desks within the company’s offices, or access to shared offices or meeting
rooms off its premises, the material differences between employees and outsiders no longer
exist. In addition, many companies rely on services (notably in IT) from third-party
organizations that locate external employees within the company’s offices. It is now not
uncommon to have external workers working full time in an office, while the employees of
the company are working mostly remotely.
• While the services provided to employees have really solved issues of mobility, they can also
be used to provide an outsider with access to the company’s core resources (such as data and
spaces). Therefore, the changes required to take into account the growing importance of
remote working also lead to a smoother collaboration with non-employees, facilitating the
outsourcing of core activities.
• When offices are increasingly shared spaces for temporary collaboration between employees
(or even outsiders) and no longer the common spatial reference of working life of employees
only, they change status. Headquarters are now less the physical incarnation of the company,
and are treated more as only one of its material assets that should be optimized like the others.

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All of this is changing the perception of the borders of the company: when insiders are outside,
outsiders can act like insiders and the difference between them is no longer a physical one. Something
is broken.
But while the physical existence of the company is being challenged by the digital revolution and its
consequences, work still requires physical contact: “What early digital commentators missed is that
even if we can work from anywhere, that does not mean we want to. We strive for places that allow
us to share knowledge, to generate ideas, and to pool talents and perspectives. Human aggregation,
friction, and the interaction of our minds are vital aspects of work, especially in the creative industries.
And that is why the quality of the physical workplace is becoming more crucial than ever” (Ratti &
Claudel 2016). Collaboration and creativity would therefore still require common place and time,
albeit periodically. The concentration of meeting rooms within headquarters will have to evolve into
spaces of creativity, spread everywhere and open to all.
3.3 The rise of the freelance economy and the porous borders of
companies
One third of US workers has done some freelance work in the last year (Freelancer union & Upwork
2015). Massive changes are occurring in the workforce organization, with the growing importance of
the freelance economy, which is expected to concern 50% of workers by 2020 (Wald 2014). Some
even predict that the coming decade will see the emergence of the first Global 2000 company with
no full-time employees outside of the C-suite (Accenture 2016). While there are many different
reasons to start freelancing, this shift is more and more a choice (60% in 2015, from 53% in 2014),
increasingly motivated by a desire for greater freedom and flexibility (Freelancer union & Upwork
2015).
But these numbers hide an extremely diverse picture in terms of type of contract, motivations, or the
existence of a traditional job alongside freelance work. Monique Dagnaud (2016) identified three
different types of freelancers that illustrate this diversity:
• Consultants, designers or start-up founders, who leverage rare competencies to gain
freedom, and choose the way they work. This type of freelancer can also typically be linked
with a search for meaning, a taste for collaboration and the rejection of large organizations.
• Uberized workers are the other face of the same collaborative economy, with a growing
number of people who cannot access traditional employment, and for whom independent
work is the only way to earn (scarce) resources.
• Slashers, who add different jobs to their days by choice, do not want to be confined to their
traditional job, and are freelancers by night or during weekends alongside a more traditional
job, to gain some freedom.
This shows the extremely diverse situations of these “workers with soles of wind” (Dagnaud 2016),
who are rapidly increasing in number, following the growth of the sharing economy, and the reduction
of friction in the contract labour market due to the growing importance of online platforms.
There were concerns that the contractor model, concerning mainly the first category of freelancers,
would not provide an efficient alternative to traditional employment. It appears that this is not the
case, and that on the contrary the formal process required to contract with a freelancer for a specific
task creates organizational clarity, and leads to commitment and focus (Libert et al. 2016). A recent
survey (Rasch 2014) even shows that freelancers are more engaged, have greater pride and
satisfaction than employees, and bring innovation to their clients. In fact, the contractor model is

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based on a fundamental requirement for a motivated workforce: autonomy, understood as autonomy
over when, where and how you do your work.
The rise of the freelance economy is therefore not only linked to the growth of the sharing economy
and the increasing number of unsecured jobs that those internet platforms create; it also brings into
companies the innovative and motivated workforce which they are desperately looking for.
Companies are slowly discovering that their borders are more and more porous, with a growing
number of non-employees at critical positions at the core of their business. This may even lead to
radical changes in companies’ governance, to invite those stakeholders around the table (Brugière
2013).
3.4 Is a new era coming?
For companies struggling to adapt to a fast-evolving world, more and more porous borders and a
changing physical existence, what will the future be like? Predicting the future is a dangerous game,
but consulting firms love to play to it. The Boston Consulting Group (2016) or PwC (2015) share a
common view of a future where firms will have to compete with new players loosely organized in
networks.
For the latter, the “blue world” of large corporations will soon compete with the “orange world”,
composed of collaboration networks of small organizations. This fragmentation of business is
empowered by technology, and based mostly on highly specialized experts that aggregate for
different opportunities, with high efficiency. For people involved in those networks on a contract
basis, work life will take a new perspective: “People are more likely to see themselves as members of
a particular skill or professional network (…) Orange pioneers will give a new lease of life to
professional guilds, associations and trade bodies—relying on them for training, development and
innovation.” (PwC 2015, p. 20).
This view of an irreconcilable fight between the big (bad) corporation and the (small and beautiful)
network is somehow in contradiction with authors that argue for a large adaptation to a networked
model of existing organizations. For Marc Halévy (2014), organizations are starting to adapt their
internal organization based on a networked perspective, leaving aside traditional hierarchy to adopt
a collaborative and horizontal mind-set, but also integrating themselves at a different scale into larger
networks of collaboration between companies. More advanced views state that agile companies will
combine both a stable backbone (values, structure, competences) with a highly adaptive part (Aghina
et al. 2015). This model states that networks are organized as platforms, with orchestrators that
structure them, and allows them to quickly grow and evolve in response to market opportunities
(Libert et al. 2016).
But is it really a new model? Certainly not. As Adam Davidson reminds us (2015), Hollywood has been
organized like that for decades: “This approach to business is sometimes called the ‘Hollywood
model.’ A project is identified; a team is assembled; it works together for precisely as long as is needed
to complete the task; then the team disbands. This short-term, project-based business structure is an
alternative to the corporate model (…). It’s also distinct from the Uber-style ‘gig economy’, which is
designed to take care of extremely short-term tasks, manageable by one person, typically in less than
a day.” (Davidson, 2015) So let us explore this old “Hollywood model” that it would seem might have
a future.

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4.
Building hypothesis:
An analysis of two networks
4.1 Methodology
The first stage of this research has allowed us to understand what a networked business is, how a
network works and to build some preliminary hypotheses. This second stage aims to complete this
set of hypotheses, based on the analysis of data collected through fieldwork, but also qualitative
information gathered during the process. All hypotheses will be tested later in stage three of this
research (Part 5).
Fieldwork consisted in data collection and analysis of two different networks:
• The first network analysed concerns a rather traditional sector, the boating industry, in the
region of Nantes. Nantes is located close to the Atlantic in the western part of France, but is
not a major harbour. However, the city is home to a specific network of boating industry
players, acting as a discrete and unknown back-office for the main harbours along the coast.
I had no previous knowledge of this industry, but had access to some direct contacts within
it. As we will see, although the network mapping method was useful, analysis showed that,
within this industry, local businesses are not operating as networked businesses.
• The second network analysed is partly composed of my own network of partners. It is
composed mainly of players in the urban development, consulting or design industries, based
in Nantes or elsewhere, working either as independent contractors, or in small or large firms.
This fieldwork started with my professional contacts, but quickly expanded far away from my
own network. The specific activities of the members of this network mean that most of them
work as networked businesses on a daily basis, which brought many learnings to the analysis.
This stage could be qualified as exploratory research, as no standard methodology for the analysis of
networks exists really. The data collection process was therefore improved with each network
analysis, to propose a more robust methodology for network analysis.
The collected data made it possible to map these two networks and produce some graphical and data
analyses. However, this approach is mainly qualitative, as the collection process itself was used as an

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opportunity to collect inputs through direct contacts with players, and to raise questions on the basis
of the collected data. These two network analyses are therefore based on a mixed qualitative and
qualitative approach.
4.2 Network 1: Exploration of the boating industry in Nantes
Data collection process
This first network analysis was designed to test the data collection methodology, as an early
prototype. The main objective was to have a clear picture of the boating industry in Nantes, based
on the representation of this network.
In addition to the research objectives, another aim was to identify whether any of these players would
be able to transfer their business to a specific area of Nantes, known as “Bas-Chantenay”, where an
urban renewal project is currently in its early stages. This aspect was not part of any official mission
from local authorities, but is a subject of interest for them. The final results of this work have
therefore been presented to some officials involved in this project.
I started this exploration with no specific knowledge of the industry, and only four names and phone
numbers given by one of the members of this network. All four accepted to be interviewed and,
during the discussions, described their different connections to other players of the industry. These
contacts enabled me to organize new interviews, and gradually to obtain an almost complete picture
of the boating network in Nantes, as described later.
During this first network analysis, 15 interviews allowed me to identify 108 organizations and their
connections. Interviewees were either business owners or managers of their organization, involved
in very varied ways in the local boating industry: naval design, ship-building, associations, retail, etc.
These organizations were the elements of the network that were stored in a database and categorized
by activity and location, to allow further analysis. Interviews allowed me to identify 167 connections
between these elements, that were drawn using the kumu.io tool, resulting in the following graphical
representations and analyses.
Results
Illustration 9 shows an analysis of the complete network. Elements defined as within the boating
industry (e.g. shipbuilding, specialized retail, ship design, etc.) are represented in blue; elements that
are related to but not part of this industry (printing services, raw material suppliers, unrelated services,
etc.) are in grey. Darker blue and grey elements are located within the larger Nantes’ region, lighter
ones are situated further away. All elements with a white dot are organizations that were interviewed.
Beside this representation, the first learning from the collection process was my inability to make
interviewed people talk about their contacts in terms of people, as initially planned. Except two
exceptions, all interviewees focused on relationships between organizations. The only noticeable
exceptions were two members of internet service start-ups, who were more prone to quote the
surname of their professional contacts than the name of their respective organizations.
Therefore, the elements represented here are organizations, and not people:

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Illustration 9: Network 1 - All elements and connections (By the author)
Most companies involved in this network are either producing goods (shipbuilding) or distributing
them (brick and mortar or online retail), or delivering traditional services (harbour management,
renting, etc.) Only a few of the interviewed players are involved in delivering high-end services, such
as naval engineering or online platforms. It appeared during interviews that the network observed is
mainly organized through contractor-subcontractor, or client-supplier relationships. These traditional
inter-organizational relationships are far from a collaboration of peers working for the same client,
even if the business relations of the network seem rather peaceful and even friendly.
Coming back to my preliminary definition of a networked business, although we clearly have an
“interconnected system of organizations and their value-producing assets”, they are not “working
toward one or more common objectives.” The high level of connections between players of the
boating industry in Nantes proves that a network exists, but it does not work as one or many
networked businesses, but more as businesses in network.
There seems therefore to be a link between the fact that this network focuses on tangible goods, that
it works based mainly on traditional inter-organizational relationships (and not inter-personal), and
that it does constitute a networked business. Later, based on these first facts, the second network
analysis will bring some further elements that will help to build formal hypotheses.

- 27 -
Illustration 10: Network 1 - Local elements and connections (By the author)
The database made it possible to exclude from this representation all elements that where either not
considered as part of the boating industry (the grey dots in illustration 9), or which were not located
in the Nantes region (lighter dots). Illustration 10 focuses only on members of the boating industry
located in the Nantes region, and shows the various categories of activities in different colours: in red
we see the production of goods (mainly shipbuilding, maintenance and refit), yellow represents
services (from design to boats rental), blue represents retail (targeting professionals or end-users) and
green represents official institutions, associations and clubs.
Network mapping and graphical representation demonstrates that the boating industry is highly
diversified in the area, with different clusters such as a production one (top-left in illustration 10) with
the associated services and retailers, and another one focused rather on services (bottom-right),
including an innovative one focusing on end-users. Those clusters are highly connected through the
local shipyard (located in the Bas-Chantenay area) and parts retailers.
This fact seems consistent with the specific role that Nantes plays in the regional boating market.
Too far from the sea and main boating harbours to host service suppliers aimed at end users, the area
gathers a large range of back-office activities that structure the whole industry.
The mapping methodology not only proved useful for characterizing this network, but also for
identifying players of the local boating industry that could be relocated within the “Bas-Chantenay”
area, where a large urban renewal project is currently being designed.

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The graphical representation showed a discrete player located at the core of the network, that none
of the interviewees identified as a key player. I decided to contact the business owner for an
interview, based only on its strategic location on the map. It appeared not only that this business acts
as a broker within the industry, due to the large number of business relations it has with different
parts of the industry, but that the business owner was potentially keen to relocate its activity to the
project area, and to host other players in rented spaces. This key discovery was made possible by this
network analysis, in a very short period of time, and may (hopefully) lead to real-world results in the
coming years.
Methodology improvement
The collection process, which used interviews to categorize the activity of the player, its connections
and their nature, brought many learnings that lead to improvements in the methodology used for the
second network analysis:
• First, this explorative methodology proved to be highly efficient, as with only a few interviews
it was possible to quickly draw an almost complete map of the network. The last interviews
appeared to bring only a few new connections between previously identified players, but no
new players to the dataset. This does not prove that the picture is complete, but that it is
certainly very close to it.
• There is a need to clearly define at the beginning of the collection process whether the focus
is on relations between people or organizations. This certainly depends on the type of
network analysed: business, friendship, etc. But, as we will see later, industries working as
networked businesses tend also to focus more on interpersonal relationships than traditional
ones.
• For this first analysis, I chose not to further categorize relations. To gain more precise content,
there is certainly a need to identify the intensity of relations and the type of dependencies
between elements.
4.3 Network 2: A deeper exploration of a larger network
Methodology
This second network analysis fully takes into account the learnings from the first one, and focuses
initially on two separate networks which I decided to analyse in parallel:
• Starting from my own professional network, I tried to draw the picture of a network mainly
focused (I thought) on urban planning, architecture and consulting.
• The other network I wanted to analyse was deliberatively outside of my professional circle,
as my objective was to analyse a network in which I would not be involved in order to avoid
bias. I therefore choose to focus on the design network, which is quite large in Nantes.
Both these networks are focused on high-end services, providing mainly expertise, design services or
consulting. These activities are rather different than those of the boating industry analysed
previously, and so I tried again to focus data collection on relationships between people and not
organizations, a strategy that failed for the first analysis.
Starting with two names for both networks, I built two network maps, collecting from every contact
his own partners’ details, and identifying his relationships with them. The process was then repeated

- 29 -
with the new contacts. For this second analysis, I used a slightly different approach to organize this
field work:
• The first contact was done by email, asking participants to fill in a table with their professional
contacts. These short questions where deliberately ambiguous on two points: “Please list the
people with whom you are working for end-clients, and qualify this relation in terms of
intensity (low, medium, strong)”.
• Based on the data collected, I systematically held an interview with the participants that
answered my email. The interviews were done either by phone or during live conversations,
and were focused on three things:
o verification of the collected data,
o explanation by the participant of the two deliberatively ambiguous elements of the
questions (“with whom” and “intensity”), to understand their own interpretation of
those terms,
o open discussion on the subject.
The aim was therefore not to collect only raw data from a clearly defined quantitative process, but
to gather learnings from the data collected, the collection process itself, and interactions with people
on this occasion.
Ten people, with an equal number of men and women, fully answered my questions (providing both
details on their contacts and participating in the subsequent interview). Adding my own personal
network to the collected data allowed me to identify 175 elements (people) from 123 different
organizations, with a total of 314 connections between them. Four of the ten interviewees were
working on a freelance basis, four in different small companies, and two in the same large corporation.
Collected data
Based on the data collected, illustration 11 shows the whole map of the network analysed.
Interviewed people are highlighted in red, and elements are automatically positioned depending on
the intensity of their relations with one another.

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Illustration 11: Network 2 - The entire network (By the author)
But why only one picture, as my aim was to analyse two different networks (including one in which I
would not be involved)? Those two networks merged during the research process, and even merged
due to the research itself. Discussions I had for research purposes only with two contacts from the
design industry lead to business collaborations with those people. Two separate networks therefore
merged, with new weak ties between them, and me (SG) as broker. This is a good example of the
dynamic characteristic of networks, a fact which many of those interviewed stressed. This picture is
therefore no longer accurate, as links between elements constantly evolve, and new elements are
added and disappear constantly. This is merely a snapshot, or a still picture, of a living organization.
The reality is a movie.
Specificities of the knowledge industry
During the first network analysis, focused on the boating industry, I tried without any result to focus
the interviews on inter-personal relationships. The resulting map therefore showed only
organizations and their relationships, in a rather traditional network focused mainly on tangible goods,
and where none were acting as a networked business.
This network analysis showed a clearly different picture. No participants cited any organization as a
contact, even larger ones, and all focused on only inter-personal relationships without ambiguity. The
only exception was very specific—a local think-thank was identified, but as a place where many
relationships are built with people, rather than as an organization with whom the participant had a
relationship.

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The analysis of the activities of the different companies and freelancers identified as contacts shows
a very high number of different competences, but also that nearly all elements in the network are
parts of what we will call the knowledge industry. Let us go deeper into this important point.
Illustration 12, below, shows all elements, located according to their activities. To analyse this aspect,
I coded one or several tags describing the work of the 175 elements, based on the description of their
contacts, or their web sites. Elements with the same tags appear closer to one another.
Except civil servants working in the public sector, and very few standalone elements with a unique
tag not represented here (like social-sciences, art or wood-working), the activities of all elements rely
only on intellectual capital of the people involved, and not on tangible assets. This is consistent with
a commonly accepted definition of knowledge industry, which differentiates it from a traditional one
as it produces and distributes ideas and information, rather than goods and services (Drucker 1969).
It is a type of industry that relies mostly on intangible assets, even if very few are on the balance
sheet, where “you are an expense, and your chair is an asset” (Libert et al 2016).
Illustration 12: Network 2 - Main activities (By the author)
Therefore, does the knowledge industry rely more on people to make connections within networks?
This tends to be confirmed by interviewed persons, which all confirm that relations between
organizations are people-based, and usually do not survive a change of person. This focus on people
is consistent with the specific nature of this industry, which relies mostly on the knowledge and

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expertise of people, and less on tangible assets (such as the boating industry) or accounting intangible
assets (such as brands or patents).
It is also consistent with the fact that the relations between players in the network were never
described as hierarchical (such as in the boating industry), but rather as horizontal and collaborative
which is consistent with our definition of the networked business, even if dependencies exist and are
assumed between players. Based on the facts from the first network analysis and these new elements,
I can therefore formulate a new hypothesis:
Hypothesis 6: Networked business is more adapted to the knowledge industry, and less for industries
that rely mostly on tangible assets.
At this point we may fine tune our preliminary definition of networked business to bring it closer to
these facts, as although elements of the network are “working toward one or more common
objectives” (which was not the case with the first network analysis), they are in fact not an
“interconnected system of organizations and their value-producing assets”, but more an
“interconnected system of people from different organizations”.
My final definition will therefore be:
(A networked business is) a state in which an interconnected system of people from
different organizations are working toward one or more common objectives.
The picture composed by the set of elements and connections in the previous pages are therefore
not a networked business, but a snapshot of many interconnected different ones.
The role of organizations
In terms of organization size, the second network analysed is highly diverse. As shown in table 13
below, extracted from the collected data, if we exclude people from different public authorities, we
have a range going from 23 people working solo, to 39 people working for 11 different companies of
more than 100 employees.
Size People Organizations
Public organization 24 24
1 person 23 23
2-20 people 67 52
21-100 people 22 13
More than 100 people 39 11
Total 175 123
Illustration 13: Network 2 - Size of organizations (By the author)
From interviews following the data collection process, it appears clearly that within this network
people think about relations only in interpersonal terms, independently of organizations.

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I already stated that organizational relationships are defined as people-dependant by interviewees.
However, people employed by large organizations also cited contacts from within or without their
own organization, with no clear distinction between them, and the intensity of relationship with
colleagues was not specifically higher. There is therefore no correlation identified between the
organization size and the tendency to work as a networked business.
This would tend to demonstrate that networked businesses are not specifically adapted to a specific
size of organization, and especially not only to freelancers or small businesses. This also means that
networked businesses tend to span across organizational borders. A new hypothesis can be proposed:
Hypothesis 7: Networked businesses span across organizational borders, and are not adapted to any
specific size of organization.
But the fact that networked businesses are not reserved to a specific kind of organization and cross
their borders, does not mean that organizations do not play a specific role within those relationships.
If we follow the closeness concept (Burt 2007), organizations should increase closeness between
their own employees, and generate better efficiency through both trust and social control.
To understand the effect that a company has on the network, I made a specific analysis of the data
collected relating to the presence of a large company within the network. As this company is my
former employer (we will call it CorpX), many employees of this company appear in the network,
either as one of my direct or indirect contacts. All 18 blue circles in illustration 14, below, identify
employees of CorpX, even though only two of them were interviewed directly.
This illustration shows that large organizations, such as CorpX (200+ employees), create many
connections between their own employees, but are not necessarily composing isolated clusters
composed of their employees only. The graphical repartition of CorpX employees is clearly diffuse,
and in many cases the shorter path between two employees is through an external element.

- 34 -
Illustration 14: Network 2 - Network position of employees of CorpX (By the author)
But even if the network does not replicate organizational borders in the composition of clusters, it
clearly tends to increase closeness of members of large organizations, by the multiplication of
relations it generates naturally. The following table show the closeness index (in the middle) of all
elements of the network:
Illustration 15: Network 2 - Index of two employees of CorpX (By the author)
CorpX’s employees occupy ranks 3, 4, 6 and 7 in the top ten index of closeness. More significantly,
employees that occupy ranks 6 and 7 were not even interviewed. Their closeness index would have
been far higher if they had been directly involved in the data collection process.
However, although belonging to a large organization seems to increase closure (and should therefore
increase efficiency), is this at the expense of a lower ability to connect with the external world?
Rank Name Value Rank Name Value Rank Name Value
#1 AS 50 #1 SG 0.594 #1 SG 0.580
#2 MM 46 #2 MM 0.584 #2 MM 0.384
#3 SG 38 #3 AS 0.572 #3 AS 0.379
#4 JF 26 #4 GH 0.492 #4 JF 0.316
#5 GH 20 #5 JF 0.480 #5 VP 0.110
#6 VV 15 #6 MD 0.467 #6 VV 0.103
#7 VP 14 #7 MJ 0.467 #7 EB 0.091
#8 YG 12 #8 VV 0.448 #8 GH 0.058
#9 MD 11 #9 VP 0.437 #9 GC 0.054
#10 MJ 11 #10 HS 0.423 #10 HS 0.048
BetweenessDegree Closeness

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The analysis of the specific case of AS and GH, two employees of CorpX, tends to show that a high
level of closeness does not have to be related to a lack of betweeness. As shown in the previous table
(Illustration 15), both have a very close closeness index (ranks #3 and #4), but a very different degree
index (an index score of 50 for AS, with a higher connection number than GH who has an index score
of 20). This difference results in a far higher betweeness index for AS. The explanation is that if both
are highly connected within the organization (resulting in close and high closeness scores), one
reports a higher number of external connections that leads to a higher betweeness index score.
Taking a closer look, it appears that not only does AS have more connections with people from
outside the company, but also with more different competencies. He is in fact situated at the border
of the company, and acts as a broker between CorpX and the rest of the network. This would tend
to confirm that while people from large organizations apparently benefit from a stronger closure
effect, they can still act (or not) as brokers with the outside.
The power of brokers: creating opportunities
If we look in more depth at the data collected, some interesting trends appear. The following table
(illustration 17) shows a breakdown of the intensity of the different connections reported by people
who participated directly in the data collection process. It would appears, apart from one exception,
that the interviewed people only reported from 2 to 5 connections of high intensity, even for those
who reported many.
The exception is AS, who reported 14 such high connections, however it appears that he was the
only one to have thoroughly understood the question “with whom you are working for the end-client”,
including administrative staff and hierarchical managers. Therefore, the data collected from him are
not directly consistent with those collected from the other interviewed people.
High
intensity
Medium
intensity
Low
intensity
Number of
connections
MM 4 16 24 44
SG 4 10 20 34
JF 5 8 10 23
EB 4 4 1 9
GC 5 3 0 8
AS 14 22 10 46
GH 2 5 9 16
VP 4 4 3 11
AB 2 1 4 7
VV 2 3 8 13
HS 3 2 2 7
Illustration 16: Network 2 - Details on connections of interviewees (By the author)
The distinction between the three levels of intensity does not in fact give rise to many learnings, apart
from the fact that only a handful of connections can be considered as high by the same person. The
differentiation between medium and low intensity appeared unclear to interviewees, and the data
collected does not bring us much more in terms of learnings. A simpler scale of two levels of intensity
(strong and weak) would have brought more consistent results.

- 36 -
In fact, the number of connections does not appear to be relevant by itself, and the degree index
which reflects this number, is not a proxy of efficiency or power within the network, but is still a
prerequisite for brokerage.
A focus on the betweeness index (reflecting brokerage power of elements) brings much more in terms
of learnings. In the network analysed, the first four people in the betweeness index are responsible
for much of the brokerage effect, with a huge drop taking place between rank 4 and 5 (from 0.316
to 0.110) as shown in table below.
Illustration 17: Network 2 - Betweeness data (By the author)
Targeted questions during interviews showed that some common characteristics are shared by all
four of these people:
• The projects they are working on require a high number of different competencies, and that
these competencies often vary depending on the project. They are therefore in regular
contact with a large number of different people with various expertise, to have all
competencies available to answer business opportunities.
• During these projects, partners usually perform a significant part of the required work, and
not only one-off tasks.
• They also rely on partners for part of their business development, for projects where they are
not directly contracting with the end-client and are not performing the main part of the
assignment.
These four examples constitute a first category of players engaged in networked businesses, highly
connected to a large number of people with a wide range of different expertise.
There is another category of less connected people (with a lower betweeness index), but still engaged
in networked businesses. For this second category, partners usually provide specific competencies
for well-defined tasks. Regular partnerships with a reduced range of required experts act as an
extension of their own competencies and often of their large or small organizations, as an alternative
to full-time employment. It appeared during the interviews that these kinds of relationships, work on
a more long-term basis, with usually only one exclusive partner for each competency, whereas the
first category of player tends to multiply partnerships even in the same area of expertise.
Interestingly, it was not possible to represent the connections of one of the persons interviewed in
the network. As the leader of a community in the design business, he both has too many connections
to be represented graphically in a readable manner, and those connections are in fact not really part
Rank Name Value Rank Name Value Rank Name Value
#1 AS 50 #1 SG 0.594 #1 SG 0.580
#2 MM 46 #2 MM 0.584 #2 MM 0.384
#3 SG 38 #3 AS 0.572 #3 AS 0.379
#4 JF 26 #4 GH 0.492 #4 JF 0.316
#5 GH 20 #5 JF 0.480 #5 VP 0.110
#6 VV 15 #6 MD 0.467 #6 VV 0.103
#7 VP 14 #7 MJ 0.467 #7 EB 0.091
#8 YG 12 #8 VV 0.448 #8 GH 0.058
#9 MD 11 #9 VP 0.437 #9 GC 0.054
#10 MJ 11 #10 HS 0.423 #10 HS 0.048
Degree Closeness Betweeness

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of any networked business—his work is to facilitate connections between players of the industry, but
not to really take part in the collaborative actions that subsequently arise. He is in fact a pure broker.
We could make a hypothesis on these three different brokerage positions within networked business:
Hypothesis 8: Different brokerage levels exist within networked business: from low and high intensity
brokers, to pure brokers.
The analysis of these relationships was also an opportunity to understand how contacts are initiated.
It appears that with only very rare exceptions, nobody proactively seeks to extend their range of
partners, except for specific business opportunities requiring new competencies. Business
development is a critical time where networks tend to extend, but is also identified as good ways to
test viability of a partnership. Producing a business proposal for an end-client involves, in a short
space of time, different critical aspects of a relation, from the disclosure of personal references and
the fulfilment of deadlines to financial negotiations. It is therefore considered by some as a first test.
But while the first moments of a partnership are a time when both partners are attentive to clues to
judge whether the relation can last, initiating the contact, in the first place, requires some kind of
presumption of trust. None of the interviewed people initiated any contacts without a prior indirect
contact. For once, Google searches and social networks are not considered as relevant tools, as such
partnerships are too critical not to be based on some kind of solid ground. Only two distinct channels
were described:
• First of all, new direct connections are mostly based on existing indirect ones. Partners from
my partners can become mine, if my contact accepts to act as a broker. This kind of indirect
relationship acts as a minimum risk reduction tool, and is considered by most as a definitive
requirement to initiate new relationships.
• The only exception to contacts based on indirect connections are those that are made through
specific occasions and circles such as professional associations, specific events or social
groups like local think-tanks. These specific circles can create occasions to initiate a relation
outside of a direct business context that could evolve on that ground to common involvement
in a networked business.
Hypothesis 9: New connections are mostly initiated at the early stages of a business and rely on indirect
but existing connections to benefit from a minimum of trust, or through professional events or
organizations.
The real nature of connections: trust & collaboration
So a minimum level of trust is a requirement to build new relationships, and this prerequisite is mostly
managed trough indirect contacts. But trust is not only required at the initiation of the relation, it is
the base of the relation during its entire life span.
When asked for details on their personal definition of the “intensity” of connections, the persons
interviewed all interpreted it as synonymous of trust. Some even had a more precise view of the
gradation of connections, based on a first circle of well identified highly trusted partners, and a more
blurred outer circle of relevant ones, where there was less confidence in the relation.

- 38 -
When going into more detail during the conversation, it appeared that people’s trust was not only
based on good professional relations, but on a broader range of aspects such as long-term common
experience, ability to discuss subjects outside of the projects themselves, ability to learn from the
other, and ultimately friendship. If common history seems to be a good way to access this first circle,
frequency of contact is not that relevant. The network is reported to be a highly evolving material,
and some connections may not be active for years due to projects specificities, but somehow trust
and the close connection can be maintained.
In addition to being based on trust, connections within a networked business are also of a specific
nature. Even though some players are clearly relying on partners, either for critical competences or
business development, none of the interviewed tried to introduce any kind of hierarchy in the
relations. In this network people define relations between them as horizontal.
This seems fully consistent with the requirements for collaborative projects highlighted by Coplin
(2013), which besides shared infrastructure and project processes (explicit definition of objectives,
tasks and responsibilities), require a collaborative culture where all team members are allowed to
speak up, and are highly involved. This specific collaborative culture that seems to be present in this
network is based on horizontal relationships where even the team leader acts as a coordinator and
not a boss, in a culture of transparency. This type of relationship seems to be required for networked
businesses:
Hypothesis 10: Relations within networked businesses require trust and a collaborative culture based on
high involvement of team members, freedom of speech and transparency.
The last specific aspect of relations within a networked business was the striking absence of negative
relationships. Most literature on social networks—Ronald Burt (2007) wrote tens of pages on that in
his book—are focused on the negative effects of closure within organizations, or the risk of free-
riders in networked organizations. Networked businesses are somehow specific in the fact that their
highly evolving architecture quickly excludes elements that behave poorly. Most tell stories of
inefficient new relationships, overly individualistic behaviours or even betrayals during projects.
However, all such stories are told to explain that such relationships do not last, and that within
networked businesses, connections can stop (almost) without notice, which is not the case in stable
organizations. This efficient but somehow brutal social regulation certainly has negative effects that
cannot be analysed from the collected data.
Working, learning and innovating together
But if common history is essential to building trust, it is also required for efficiency. Working
efficiently is directly related to learning—learning collectively to execute new commons tasks and
projects, but also learning to work together. Interviews reflect that efficient collaboration requires
common language to avoid misunderstandings and thoroughly understand the competencies of the
others, but also shared practice in terms of tools and methodologies. New relations are often
described as inefficient in the beginning, mainly because communication needs to be fine-tuned, but
common practice on long or many common projects increases the efficiency of the collaboration.
This seems to give a huge advantage to long-term relationships, located certainly in the first circle of
close connections. But it also advocates for structured organizations that can enforce standardized
processes and provide common tools that simplify communication between members, even without
previous shared practice. However, a distinction is certainly required between tasks requiring explicit

Networked. How networked business can bring agility and innovation across organizational boundaries in the knowledge industry.

Networked. How networked business can bring agility and innovation across organizational boundaries in the knowledge industry.

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