Socio-spatial foundations of knowledge
flow in the Irish biotechnology industry
and the role of digital ecosystems
Chris van Egeraat and Declan Curran
NUI Maynooth
CISC Seminar, January 2010
Process R&D in the Irish
Pharmaceutical Industry
Chris van Egeraat
Proposed Structure
• Background
– What is a Digital Ecosystem (Clusters / Networks)
– OPAALS / DEN4DEK
– NIRSA Research
• Case studies of knowledge flow in innovation processes
• Social Network Analysis
• Implications for Biotech DE
What is a Digital Ecosystem?
• Knowledge economies operating as ecosystems
– A situation of slowly changing networks of organisations will be
replaced by more fluid, amorphous and transitory structures based on
alliances, partnerships and collaborations
– Open Innovation
– Ecosystems are assemblages of interdependent institutions in which
the welfare of the component organisms is dependent on the
interactions between them
– Local Ecosystems (clusters; regional systems of innovation)
– Ecosystems evolve and survive due to gradual adaptation
– Evolution is accelerated by the promotion of higher and more
efficient levels of knowledge flow /sharing.
– Digital Ecosystems seek to exploit the benefits of new ICTs in terms
of faster, better and higher capacity information and knowledge flow
“Our” Digital Ecosystem
• An open source, peer-to-peer (loosely coupled server
system) digital environment
• Ideal for clusters of SMEs.
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open source, free
minimal ICT resources required
minimal investments required
adaptable to SMEs’ existing applications and business models.
Security, e.g. of IP (anything on proprietary systems has a chance to
leak)
– No central control, no single point of failure, no dominant position,
no pre-defined business model
• It is not only about software and infrastructure. It is an
architecture + an economic model + a shared enterprise
• Ideal for Regional Development. An integrated approach to
development
Integrated Approach to Development
Background
• Digital Business Ecosystem concept coined in the context of
the implementation of the eEurope 2002 action plan
• DBE research community established.
DBE Project
• Digital Business Ecosystems Project (FP6)
– FP6 Integrated Project funded under the Networked Business and
government strategic objective EC - DG Infso.
– Duration: 36 months
– 20 partners, 9 Member States
– 10.5 M€ funding
– The DBE is the largest EC research investment ever in F/OSS in ICT
for E-Business
– Regional Involvement: Aragon, Midlands, Tampere,
Piemonte,Trento, Extremadura, Lazio
– 3 DBE Pilots
– NUIM, Intel, EI developed a proposal for phase II but this fell
through due to EU cutbacks
OPAALS
• OPAALS (Open Philosophies for Associative Autopoietic
Digital Ecosystems) (FP6)
– Part of the FInES- Future Internet Enterprise Systems Cluster -in the
Information and Communication DG
(http://cordis.europa.eu/fp7/ict/enet/ei_en.html)
– 4 years, 18 partners world-wide; funding to May 2010
– Aims to develop an open-source, peer-to-peer information
technology system that can facilitate productive exchange among
businesses and communities of interest, such as SME networks or
academic research communities
– Theoretical foundation (social, natural and computer science) –
Enormous Depth of Research (frequent awards)
– Tangible output (Open Knowledge Space) – an OKS has multiple
users and communities that can interact with each other whenever
they are online and form a peer-2-peer network of spaces of
knowledge that can be semantically searched based on their
availability (i.e. if they are online) and the amount of mutual trust
they have acquired.
OPAALS and NUI Maynooth
• Phase II – Social and Spatial
Foundations of knowledge flow and
Innovation.
– Case studies of knowledge flow in
biotech and digital media project
– Supported by Forfás and facilitated by
EI
• Phase III – Knowledge networks in
the Irish biotechnology industry
Den4Dek
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CIP Competition and Innovation FP (Thematic network of regions)
9 partners, from 10 different Member States, involving directly 17
European regions.
Aim: “to share experience and disseminate all the necessary knowledge
that allows regions to plan an effective deployment of DEs at all levels
(economic, social, technical and political) in order to produce real impact
in the economic activities of European regions through the improvement
of SME business environments”
Knowledge exchange between regions with previous experience (e.g.
Aragon) and potential “enthusiast partners” (Dublin and Mid-East)
Main tools:
– International Partner Workshops and inter-active communication
platform
– Workshop in individual regions
NUIM: Regional Facilitator / DRA and MERA: Members
Output: Report on the contours of a DE in “learning region”: a DE for
the biotech industry in the Dublin/MERA Region.
http://www.den4dek.org/Sitio_web/DEN4DEK_Partners.html
Output
Case Studies of Knowledge Flow
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Phase II – Socio-Spatial Structures of Collaboration and Knowledge
Flow that Underpin Innovation among SMEs in Biotech and Digital
Media
In order to understand DEs and the contribution they could make to
competitiveness of SMEs and regional development, we need to
understand in detail the processes of knowledge flow and innovation.
Objective: to gain a detailed understanding of the socio-spatial
foundations of knowledge flow and innovation processes. Notably
the extent to which these are spatially bounded
The premise of the research: the social and spatial structures
underlying innovation vary by industrial sector.
We have explored these ideas through a series of case-studies of
innovation projects (innovation biographies tracing the genealogy) in
two sectors in the Irish economy, biotechnology and digital media.
Unit of Analysis = Projects. / 8 case studies. 43 interviews
“Modern” Biotech Inventory
• “Modern” refers to the post-genetic engineering era, that
is after scientists had developed the knowledge techniques
and tools to intervene directly at the gene level
• Companies that are “biotech enabled”
• Updated Circa Group List
“Modern” Biotech Inventory
“Modern” Biotech Inventory
Theory – Knowledge Bases
• Received theory: different industries are characterised by
different “modes of knowledge creation” with
implications for the geography of knowledge flow and
innovation (Asheim and Gertler, 2005; Asheim, Boschma
and Cooke, 2007
• Different knowledge bases are used in innovation
processes: Synthetic / Analytical /Symbolic
• Distinction takes account of
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Rationale for knowledge creation
The criteria for successful outcomes
Strategies of turning knowledge into innovation
The interplay between the actors involved
Different qualification and skill requirements
Modes of Knowledge Creation
Theory – Knowledge Bases
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The threefold distinction refers to ideal types
Most activities are in practice compromised of more than one
knowledge base
Different modes of knowledge creation are present in different phases
of innovation processes, but with different intensity – “dominant mode”
Received theory suggests that the different knowledge bases are
characterised by different sensitivities to geographical distance for
knowledge flow (Asheim et al., 2006; Gertler, 2008)
– Industries drawing on Analytical KB: rely on codified knowledge – face-to-face
contact less important (but clustering near universities)
– Industries drawing on Synthetic KB: more sensitive to geographical distance. Faceto-face of greater importance due to importance of customised solutions and partly
tacit nature of know how
– Industries drawing on Symbolic KB: most sensitive to geographical distance in
relation to knowledge exchange. Know-who type knowledge is augmented through
large gatherings which require f-t-f contact and tacit nature of know how call for f-tf communication
Theory – Buzz
• The concept of “local buzz”
• Intentional vs. unintentional knowledge flow.
• Target theory: local buzz important, particularly for
know who type knowledge
Hypothesis
• Target hypothesis: activities in biotech (analytical
knowledge base) tend to be relatively less sensitive to
geographical proximity then in digital media
(symbolic/synthetic). “know-how” and “know-who” kn.
flow is facilitated by proximity
• In reality,
– in Biotech and DM nearly all partners, clients and knowledge
sources are located overseas.
– no evidence that partner choice is influenced by distance decay
in know-who-type knowledge
BioPharma Case
Source: Van Egeraat et al. 2009
Animation Case
Source: Van Egeraat et al. 2009
Buzz
• Target theory: local buzz important, particularly for
know who type knowledge
• In reality: global and virtual buzz more important than
local buzz. Local communities do exist, but they are
“buzzing globally” and virtually rather than locally
• (maybe context specific result!)
Reflections on Knowledge Base
Framework
• Application of the concepts not straightforward
• Not all stages are clearly dominated by a single mode of
knowledge creation
• Some stages can involves several sub-innovation processes,
characterised by different modes, running alongside of each
other
• Individual activities can have characteristics of different
knowledge bases (e.g. Activities can be theoretically informed,
highly formalised and based on codified knowledge
(analytical) but clearly aimed at creating a new functional
system (synthetic)
• Not all types of knowledge are covered – crucial role of market
and industry knowledge (compare role of Scientific Advisory
Board)
Phase II – Social Network Analysis
• Phase II took the innovation project as the unit of
Analysis
• Phase III we change the unit of analysis to the network
and investigate the extent and structure of networks.
• Current territorial economic development concepts
generally recognise that networks are an important aspect
of innovation and clustering processes. Network analysis
can enhance our understanding of these processes.
• Social network analysis is based on the assumption of the
importance of relationships among interacting units and
that units influence each other. Relational ties between
actors are viewed as channels for transfer or flow of
resources.
Social Network Analysis
• SNA involves an analysis of extent and structure of social
networks
• Views relationships as actors and ties
• Key Concepts: e.g. Connectedness, Centrality and
Betweenness.
• UCINET
Irish Biotech Datasets
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Patents (companies and inventors)
Founders
Directors, Joint directorship
Serial Entrepreneurship
Spin offs (company and universities)
Any other ideas?
Datasets touch different types of
knowledge flow – know why; know who
“Modern” Biotech Inventory
• “Modern” refers to the post-genetic engineering era, that
is after scientists had developed the knowledge techniques
and tools to intervene directly at the gene level
• Companies that are “biotech enabled”
• Updated Circa Group List
Modern Biotech
Patent Dataset:
• Irish Patent Office
• US Patent Office
• Espacenet (European Patent Office)
Patent Search:
Directors and Founders
• Fame Database; Internet Search
• Verification in progress
Origin and Spin off:
Inventors and Companies:
Directors and Companies:
“Small World” Network Phenomenon
• Milgram (1967) “everyone is connected by a
chain of about 6 steps.”
• Most actors connected through small no. of
intermediaries/links
• “it’s a small world!”
• Interesting networks: (i) large (ii)sparse (iii)
decentralised (iv) clustered
Small World Network Structure
(Kogut and Walker, 2001)
“Small world” characteristics:
1. Observed ties represent only a small proportion of
all possible ties (low network density)
2. But a number of dense clusters, giving the network
structure and stability
3. Possible to get “from there to here” in few steps
Permits actors to strategise: Conduits of control and
information
Small world Characteristics (Watts, 1999)
1) The characteristic path length, L
The average length of the shortest paths connecting
any two actors.
2) The clustering coefficient, C
the average local density. That is, Cv = egonetwork density, and C = Cv/n
A small world graph is any graph with a
relatively small L and a relatively large C.
Irish Biotech Small World Analysis
1. Biotech Directors and Companies (via Directorships)
no. of directors: 302; no. of firms: 86; no. of connected
firms: 43
2. Biotech Researcher and Companies (via patents)
no. of researcher: 315; connected researchers: 307; no.
of firms (with registered patents): 40;
connected firms: 23
[Can also compare results with random network with the
same number of nodes and ties as the highly structured
observed networks]
Table 1: Biotech Directors and Companies (via
Directorships) Network Statistics
Variable
Density
Density (for all
directors/firms)
Total no. of ties
Average no. of ties
(between those
connected)
Clustering
Cluster coefficient
Random Cluster
coefficient
Path Length
Average Path length
among those connected
Random Average Path
Lenght
Directors
Companies
0.018
0.016
1,622
5.5
118
2.7
0.948
0.039
0.669
0.062
3.538
2.912
3.127
4.111
Table 2: Irish Biotech Industry Researcher and
Companies (via patents) Network Statistics
Variable
Density
Density (for all
researchers/firms)
Total no. of ties
Average no. of ties
(between those connected)
Clustering
Cluster coefficient
Random Cluster
coefficient
Path Length
Average Path length
among those connected
Random Average Path
Length
Researchers
Companies
0.163
0.041
16,110
52.5
64
2.78
0.975
0.570
0.439
0.099
2.091
2.256
2.013
3.264
Table 3: A Comparison of Small World Network
Statistics
Path Length
Clustering
Actual-to-Random
Ratio for:
Length Clustering
Network
Actual Random
Actual
Random
Irish Biotech
Directors
Irish Biotech
Companies (via
directors)
Irish Biotech
Researchers
Irish Biotech
Companies (via
patents)
3.538
3.127
0.948
0.039
1.131
24.31
2.912
4.111
0.669
0.062
0.708
10.79
2.091
2.013
0.975
0.570
1.039
1.711
2.256
3.264
0.439
0.099
0.691
4.434
German Firms1
German Owners1
Film Actors
network2
Power Grid network2
C. Elegans network2
5.64
6.09
3.65
3.01
5.16
2.99
.84
.83
.79
.022
.008
.001
1.87
1.18
1.22
38.18
118.57
2,925.93
18.70
2.65
12.40
2.25
.08
.28
.005
.05
1.51
1.18
16.00
5.60
Irish Biotech Small World findings
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Highly clustered, with short path lengths
- small world network structure
• Directors and companies connected via directors
relatively more clustered than researchers and
companies via patents
 “know-who” more networked than “know-how”
• Formal knowledge may flow in a slower, different
manner through Biotech network than informal.
2001-2005
NeutekBio
Azur
1996-2000
Flourocap
AGI
Merrion
Athpharma
Argutus
Archport
Triskal
Biotrin
Cambridge Bio
Marvo
1990-1995
Newport
Elan
Arqtech
DCU
Trinity_Biotech
Amarin
GMIT
Ultrasonic
Noctech_Cambridge
Alltracel
Biofuture
Surgen
NUIG
Microchem
RCSI
Trulife
TCD
Biosys
Aerogen
Genosynth
Cellix
Claymon
Deoxy
Genable
Mitest
Beocare
Topchem
Vysera
Westgate
Pollution_Control
LabCoat
Waterford_Clinical
UCD
UL
Eirgen
EiRx
Luxcel
Cytrea
Sigmoid
BioObservation
Enfer
Biancamed
Celtic Catalysts
Enzolve
Tridelta
Biorefineries
Eirzyme
Life Scientific
Ntera
BMR Neurotech
Topchem
Hibergen
Bio_industries
Megazyme
Haemoglobal
UCC
Plant Technology
Senith
Kora
Fastform
Opsona
Pharmatrin
Alimentary
Ash Tech
Real
Bioclin
Identigen
Icon
Audit D.
Meditec
Neurocure
Deerac
Java
Tristem
BioSensia
Berand
Immdal
Stamford
Crescent
Proxy
Stokes Bio
Shandon
Implications
• Increased theoretical understanding of structures of
knowledge flow, collaboration and innovation
• Applied Research Value: Enhance understanding of
structures of knowledge flow in two subsectors in Ireland
and role of potential biotech DE.
• In the Irish biotechnology industries, a DE is unlikely to
play a significant role in promoting regional development,
as a project management tool (although: CSETS!).
• It is more likely to stimulate regional development by
acting as a more general communication or knowledge
sharing tool and knowledge resource, connecting all
regional players in a sector
• The aggregation of projects can lead to important local
networks. A DE should exploit the potential for
knowledge flow that these networks offer
Applications Greatest Potential
• A forum for regional actors (in universities; research
institutions and private enterprise) to consult each other on a
reciprocal basis about the location of (regional and extraregional) actors and sources of knowledge (know who).
• A regionally-based science forum for biotechnology scientists
(know who) and technicians (know how). Here biotechnology
scientists and technicians in companies and universities can ask
for advice about, and interactively discuss, scientific and
technical problems.
• An biotechnology sector dedicated electronic interactive labour
exchange, matching skilled people to jobs
• A directory tool, providing information about and promote
regional actors, and promote Ireland as a biotech region.
• A project management tool for local biotech actors partners
involved in specific collaborative projects (e.g. CSETs)
Questions?