Comparing knowledge bases: on the
geography and organization of
innovation
Jerker Moodysson
CIRCLE, Lund University
Lecture at the Norwegian Research School in Innovation; Program in Innovation and
Growth; Course on Innovation Systems, Clusters and Innovation Policy, Kristiansand, October
25, 2012
Background
• Theoretical development, specification and application of
the ”knowledge base” approach/typology
• Publications 2004-2012, today’s presentation will focus
particularly on three:
– Moodysson, Coenen, Asheim 2008, Environment and Planning A
– Moodysson 2008, Economic Geography
– Martin & Moodysson 2012, European Urban and Regional
Studies
• Collective work, influenced by many (e.g. Gertler, Isaksen,
Tödtling, Boschma, Manniche etc)
• Roman Martin’s dissertation: Knowledge Bases and the
Geography of Innovation (successfully defended Oct 2)
Ambition
• Better understand innovation processes in different
types of economic activities
• Specify when geography matters for interactive
learning/innovation, in what respect, and why
• Move beyond dichotomies of local/global,
tacit/codified, high-tech/low-tech etc
• Transcend sector classifications – less relevant for
many (emerging and transforming) industries (c.f. life
science, cleantech, ICT, new media etc). Low
explanatory value for heterogeneity of innovation
practices (also in traditional/established industries).
• Combine qualitative and quantitative approaches
Basic assumptions
• Proximity contributes to reduced transaction costs and
more efficient knowledge exchange. Spatial and
relational proximity
• Compatibility of knowledge (either through similarity
or through relatedness) is one key aspect of relational
proximity
• Firms conduct routinized behaviour → they search in
proximity to their existing knowledge → transcending
cognitive domains requires absorptive capacity
• More effective to exchange knowledge with others
who share knowledge space, but only to a certain
degree – optimal cognitive scope, related variety
Basic assumptions
Applicability of knowledge
Novelty
Effectiveness = novelty x communicability
(non-redundant cognition)
Communicability
Cognitive distance
Basic assumptions
• Knowledge is important for innovation in all sectors,
high-tech as well as low-tech. Most innovations are not
”high-tech” or ”science-based” (but still knowledge
based)
• Knowledge is composed by two intertwined
dimensions
– Codified knowledge – information. Easy to transfer over
spatial distance
– Tacit knowledge – we know more than we can tell.
Embedded in people and organizations. Impossible to
transfer over spatial distance
– Knowledge always has a tacit dimension (you need tacit
knowledge to interpret information)
Basic assumptions
Research
Development
Production
Marketing
Basic assumptions
Research
Knowledge
Invent
Potential and/or
Market produce
analytic
design
Detailed
design
and test
Redesign
and
produce
Distribute
and
market
Heterogeneity
• Innovation processes differ in many respects
according to the economic sector, field of
knowledge, type of innovation, historical
period and country concerned. They also vary
with the size of the firm, its corporate strategy
or strategies, and its prior experience with
innovation. In other words, innovation
processes are ”contingent” (Pavitt, 2005, p.
87).
Basis for heterogeneity
• Majority of research on innovation up till the mid
2000s based explanations on two main dimensions
– Sector specificities (e.g. the SIS approach)
– National context (e.g. the NIS approach)
• Among the most famous explanatory devices has been
– the ”Pavitt taxonomy”, ultimately building on and further
aggregating traditional sector classifications (Standard
Industrial Classification)
– the ”Varieties of Capitalism” approach, taking national
institutional specificities into account (main categories
LME vs CME etc)
Pavitt’s taxonomy
• Describe and explain similarities and differences
among sectors in the sources, nature and impact of
innovations
• Focus on industry level – firms grouped together into
an industry on the basis of their main output. Builds on
traditional sector classification system (SIC/NACE etc)
• Two step classification: firms firstly attributed to an
industry according to their main product, and
subsequently the whole industry is attributed to a class
of the taxonomy (see next slide)
• Empirically based (inductive) classification based on
2000 innovations in the UK 1945-1979
Pavitt’s taxonomy
• Supplier dominated firms
– Manufacturing, agriculture, housebuilding, financial/commercial services. Inhouse R&D/engineering capabilities weak, most innovation from suppliers
• Production-intensive firms
– (1) Mass production industries. Technological lead maintained through knowhow and secrecy
– (2) Small-scale equipment and instrument suppiers. Firm specific skills, ability
to respond sensitively to users’ needs
• Science-based firms
– Industries aiming to exploit scientific discoveries. R&D activities of firms in
sector, underlying sciences at universities. Patents, secrecy, technical lags,
firm-specific skills
• Differences explained by sectoral characteristics: sources of technology
(inside firms, R&D labs), users’ needs (price, performance, reliability), and
means of appropriating benefits (secrets, technical lags, patents)
Problems with Pavitt/sectors
• The existence of multi-product and multi-technology firms
• Platform technogies and emerging sectors – new ”sectors”
continuously born (e.g. ICT, life science, new media etc)
• Modes of innovation differ substantially between firms within
sectors (Leiponen & Drejer, 2007)
• Large categories of firms with very similar modes across countries
and sectors (Srholec & Verspagen, 2012)
• Most varience (83-95%) given by heterogeneity at firm level.
Sectoral specificities explain 3-10%, national specificities 2-11%
Study based on 13 035 innovating firms covering 26 sectors in 13
European countries (Srholec & Verspagen, 2012).
• Alternative explanations?
Knowledge bases?
• (How) can the KB approach help us better understand the
relation between knowledge content, modes of innovation,
interaction, and relative importance of spatial and
relational proximity between firms, universities and other
actors in an innovation system context?
• (How) can the KB approach help us better understand
innovation processes carried out by firms and related
actors working with different types of economic activity?
• (How) can we better specify firms/activities according to
the KB approach? Better than sector taxonomies? Better
than the VoC-approach?
The KB typology
Analytical
Synthetic
Symbolic
Understand and explain
features of the (natural)
world by application of
scientific principles
Construct solution to
functional problems/
practical needs by
combining knowledge
and skills in new ways
Trigger reactions (desire,
affect etc) in minds of
beholders by use of
symbols and images
Focus on the process rather than the outcome
• Dimensions represent theoretically derived concepts rather than empirical cases
• Deliberately accentuates certain characteristics (not necessarily found clear cut in reality)
• Heuristics aimed to provide a systematic basis for comparison
The KB typology
Analytical (science based)
Synthetic (engineering based)
Symbolic (artistic based)
Developing new knowledge
about natural systems by
applying scientific laws
Applying or combining existing
knowledge in new ways
Creating meaning, desire,
aesthetic qualities, affect
Scientific knowledge,
models, deductive
Problem-solving, custom
production, inductive
Creative process,
communication
Collaboration within and
between research units
Interactive learning with
customers and suppliers
Experimentation, in studio,
project teams
Strong codified knowledge
content, highly abstract,
universal
Partially codified knowledge,
strong tacit component, more
context-specific
Interpretation, creativity,
cultural knowledge, sign values,
strong context specificity
Meaning relatively constant
between places
Meaning varies substantially
between places
Meaning highly variable
between e.g. place, class,
gender
Disclaimer
• We are fully aware that all real cases (firms,
industries, activities) draw on combinations of
all three knowledge bases
• Nevertheless it is possible to specify the
crucial KB of a firm (or activity) i.e. the KB
upon which those actors ultimately build their
competitiveness (through innovation), the KB
which they cannot do (innovate) without (and
neither outsource)
Illustration: The Astonishing Tribe
Empirical illustrations
• Processes and activities
• Firms and industries
• Discussion: next steps
Application: processes and activities
• Aim: Decompose innovation processes, identify
and understand modes of innovation. Address
the dichotomy of ‘proximate’ and ‘distant’
knowledge sourcing by looking specifically at the
characteristics of the knowledge creation process
• Approach: ‘innovation biographies’. Combining
insights from studies of clusters and innovation
systems with an activity-oriented focus
• Objects of study: innovation processes in life
science (pharmaceutical and functional food
applications)
Initial observation
• Strong concentration in a few nodes.
Agglomeration of (seemingly) similar firms in
close proximity to Lund University
• Global network connections are indispensable for
novel knowledge creation among those firms
• After mapping the spatial patterns of innovation
(measured through formal partnerships, copatents and co-publications) we applied an
intensive research design with particular focus on
the actual content of the knowledge generation
and collaboration
Approach
• Combination of theoretical reasoning,
readings of the innovation literature, in-depth
studies of innovation projects
• Used both for theory development (i.e.
further specifications of the KB approach) and
for empirical analysis (i.e. explaining different
spatial and organizational patterns observed)
• First step of this project focused exclusively on
analytical and synthetic KB
Modes of knowledge creation
Analytical
Synthetic
Understand and explain features of
the (natural) world
Design or construct a solution to
human problems/practical needs
Discovery and application of
scientific laws
Apply or (re)combine existing
knowledge in a novel way
Deconstruct natural systems
Construct functional systems
Know-why
Know-how
Formalized, scientific, standardized
experimentation and abstraction
Less formalized, practical
experimentation and trial-and-error
Analysis
You start with theory. You create theoretical models with a
reasonable potential to succeed in practice […] or put
differently, you believe it will succeed. You then take it to
the lab to test if it works. If it doesn’t work, theory is
useless.
Synthesis
We construct and operate […] systems based on prior
experiences, and we innovate in them by open loop feedback.
That is, we look at the system and ask ourselves ‘How can we
do it better?’ We then make some change, and see if our
expectation of ‘better’ is fulfilled.
The life science value chain/
problem sequence
2-4 years
I
2-4 years
II
III
4-6 years
IV
V
1-3 years
VI
DBFs
Academia
Pharma
I: Identification and validation of target structure (cause of disease)
II: Identification and validation of biotech application (possible treatment)
III: Pre-clinical tests
IV: Clinical tests, phase 1
V: Clinical tests, phase 2
VI: Clinical tests, phase 3
VII: Registration and commercialisation
VII
Example 1
Project phase
Research to
understand human
antibodies
Development of
antibody library
(platform
technology)
Research to
discover
antibody based
HIV drug
Pre-clinical
and clinical
trials
Dominant mode
of knowledge
creation
Analytical
Synthetic
Analytical /
Synthetic
Analytical
Actors involved
Local: researchers
at university
department
Local: University
and spinn-off
DBF
Local: DBF
Global: DBF
Local: DBF
Global: PRO
Reveal the mechanisms of
antibodies. Formalised, rational,
scientific process.
time
Example 1
Project phase
Research to
understand human
antibodies
Development of
antibody library
(platform
technology)
Research to
discover
antibody based
HIV drug
Pre-clinical
and clinical
trials
Dominant mode
of knowledge
creation
Analytical
Synthetic
Analytical /
Synthetic
Analytical
Actors involved
Local: researchers
at university
department
Local: University
and spin-off DBF
Local: DBF
Global: DBF
Local: DBF
Global: PRO
Learn how to control, select,
and reproduce antibodies.
Experimentation in the lab,
trial and error.
time
Example 1
Project phase
Research to
understand human
antibodies
Development of
antibody library
(platform
technology)
Research to
discover
antibody based
HIV drug
Pre-clinical
and clinical
trials
Dominant mode
of knowledge
creation
Analytical
Synthetic
Analytical /
Synthetic
Analytical
Actors involved
Local: researchers
at university
department
Local: University
and spinn-off
DBF
Local: DBF
Global: DBF
Local: DBF
Global: PRO
Create a medical treatment of this tool. HIV was the selected application. A
combination of analytical and synthetic mode of knowledge creation. The
antigens causing HIV had to be understood; the antibodies that could block
these antigens had to be defined; then they had to be selected from the
’library’.
time
Example 1
Project phase
Research to
understand human
antibodies
Development of
antibody library
(platform
technology)
Research to
discover
antibody based
HIV drug
Pre-clinical
and clinical
trials
Dominant mode
of knowledge
creation
Analytical
Synthetic
Analytical /
Synthetic
Analytical
Actors involved
Local: researchers
at university
department
Local: University
and spinn-off
DBF
Local: DBF
Global: DBF
Local: DBF
Global: PRO
Create a medical treatment of this tool. HIV was the selected application. A
combination of analytical and synthetic mode of knowledge creation. The
antigens causing HIV had to be understood; the antibodies that could block
these antigens had to be defined; then they had to be selected from the
library.
Understandingtime
and defining
(analytical): DBF in collaboration with
New Jersey firm.
Selection (synthetic): spinn-off DBF in
collaboration with old univ dept in
Lund
Example 1
Project phase
Research to
understand human
antibodies
Development of
antibody library
(platform
technology)
Research to
discover
antibody based
HIV drug
Pre-clinical
and clinical
trials
Dominant mode
of knowledge
creation
Analytical
Synthetic
Analytical /
Synthetic
Analytical
Actors involved
Local: researchers
at university
department
Local: University
and spinn-off
DBF
Local: DBF
Global: DBF
Local: DBF
Global: PRO
time
Highly formalised. DBF in
collaboration with hospitals and
research institutes in Stockholm
and Great Britain.
Example 2
Project phase
Development of
probiotic in medical
context
Development of
probiotic in
commercial food
context
Pre-clinical and
clinical trials
Dominant mode of
knowledge creation
Synthetic
Synthetic
Analytical
Actors involved
Local: various
departments at
university
Local: DBF and food
company
Local: DBF
Global: PRO
Medical problem: how to cure a leaking gut after surgery. Three
reserchers from different disciplines (surgery, food technology,
applied microbiology). Combined their skills and developed a
ferment nutrient solution that could be administered by tube.
time
Example 2
Project phase
Development of
probiotic in medical
context
Development of
probiotic in
commercial food
context
Pre-clinical and
clinical trials
Dominant mode of
knowledge creation
Synthetic
Synthetic
Analytical
Actors involved
Local: various
departments at
university
Local: DBF and food
company
Local: DBF
Global: PRO
A related application on the commercial
market: functional food. Combine
knowledge about function with
knowledge about food production
time
Example 2
Project phase
Development of
probiotic in medical
context
Development of
probiotic in
commercial food
context
Pre-clinical and
clinical trials
Dominant mode of
knowledge creation
Synthetic
Synthetic
Analytical
Actors involved
Local: various
departments at
university
Local: DBF and food
company
Local: DBF
Global: PRO
A related application on the commercial
market: functional food. Combine
knowledge about function with
knowledge about food production
The functional part: time
a local DBF.
The food part: a local food company.
Very much trial and error to make these
systems work togehter.
Example 2
Project phase
Development of
probiotic in medical
context
Development of
probiotic in
commercial food
context
Pre-clinical and
clinical trials
Dominant mode of
knowledge creation
Synthetic
Synthetic
Analytical
Actors involved
Local: various
departments at
university
Local: DBF and food
company
Local: DBF
Global: PRO
Highly formalised. Primarily a mattertime
of getting scientific certification and
support by researchers and
physicians. DBF in collaboration with
research institutes globally.
Findings
• Innovation processes involve elements of both
analytical and synthetic knowledge
• The characteristics of ”the core of the matter” in terms
of KB differ (not only between firms and industries, but
also within those)
• Dominant KB (in quantitative terms) ≠ crucial KB (what
the activity cannot do without)
• A number of case studies in different sectors used as
preliminary classification basis (this could be further
developed and maybe used for more accurate “sector”
classifications? Will come back to this)
Application: firms and industries
• Aim: Examine the geographical and organizational patterns
of knowledge sourcing among firms with different crucial
KB (classification of firms based on sample of case studies
similar to those described above)
• Research questions
– What is the role of regional/global knowledge sources (for firms
drawing on different crucial KB)?
– What is the role of less/more formalized knowledge sources (for
firms drawing on different crucial KB)?
• (parts of) life science, (parts of) food, (parts of) moving
media in Skåne. NB. Selection of cases not based on sector
statistics.
Expected patterns of knowledge
sourcing (based on theoretical
reasoning)
global
Analytical
Synthetic
Symbolic
regional
less
formalized
Source: own draft.
more
formalized
38
Expected patterns of knowledge
sourcing
• Knowledge sources in geographical proximity
are particularly important for synthetic or
symbolic firms, whereas analytical firms tend
to be less sensitive to geographical distance
• Formalized (scientific, codified, abstract and
universal) knowledge sources are more
important for analytical firms, whereas
synthetic and symbolic firms rely on less
formalized knowledge sources
Knowledge sourcing through…
• Monitoring refers to search for knowledge
outside the firm, but without direct interaction
with these external sources
• Mobility refers to retrieving knowledge input
through recruitment of key employees from
other organizations (e.g. firms, universities)
• Collaboration refers to exchange of knowledge
through direct interaction with other actors
• Network analysis based on data generated
through structured interviews
40
Monitoring
Mean
fairs
magazines
surveys
journals
moving media
food
life science
moving media
food
life science
moving media
food
life science
moving media
food
life science
3.00
3.11
2.72
3.19
3.07
2.83
2.44
2.86
3.31
2.31
1.86
3.31
Std. Deviation
1.29
1.40
1.39
1.39
1.27
1.34
1.25
1.30
1.51
1.21
1.08
1.31
N
36
28
29
36
28
29
36
28
29
36
28
29
Table: relative importance of various sources for gathering market knowledge through monitoring.
Source: own survey.
 Analytical firms rely more on formalized knowledge sources
than symbolic and synthetic firms.
41
Mobility
Mean
university
technical college
same industry
other industries
moving media
food
life science
moving media
food
life science
moving media
food
life science
moving media
food
life science
2.94
2.11
3.93
2.26
1.89
1.90
4.36
3.96
3.87
2.61
2.93
1.77
Std. Deviation
1.45
1.23
1.55
1.15
1.20
1.40
.93
1.04
1.41
1.13
1.30
1.04
N
35
28
30
35
28
30
36
28
30
36
28
30
Table: relative importance of various sources for recruitment of highly skilled labour.
Source: own survey.
 Analytical firms recruit primarily from universities and
other firms in the same industry; synthetic and symbolic firms
recruit primarily from other firms.
42
Figure: Knowledge sourcing through
collaboration in
media
Source: own survey. Graphical illustration
inspired by Plum and Hassink (2010).
Figure: Knowledge sourcing through
collaboration in
media
Source: own survey. Graphical illustration
inspired by Plum and Hassink (2010).
Figure: Knowledge sourcing through
collaboration in
media
Source: own survey. Graphical illustration
inspired by Plum and Hassink (2010).
Figure: Knowledge sourcing through
collaboration in
media
Source: own survey. Graphical illustration
inspired by Plum and Hassink (2010).
Figure: Knowledge sourcing through
collaboration in
food
Source: own survey. Graphical illustration
inspired by Plum and Hassink (2010).
Figure: Knowledge sourcing through
collaboration in
life science
Source: own survey. Graphical illustration
inspired by Plum and Hassink (2010).
Knowledge sourcing through
collaboration
20.7%
24.5%
46.8%
24.4%
33.3%
international
23.9%
54.8%
moving media
national
regional
42.2%
food
29.4%
life science
Table: share of regional, national and international linkages between actors
Source: own survey.
49
Conclusions
• Symbolic firms retrieve knowledge from less formalized
sources and recruit primarily from other firms of similar
type. Knowledge exchange through collaboration takes
place in localized networks
• Synthetic firms retrieve knowledge from less formalized
sources and recruit primarily from other firms. Intentional
knowledge exchange takes place on the regional and
national level
• Analytical firms rely on knowledge stemming from scientific
research and recruitment from higher education sector.
Knowledge flows and networks are very much globally
configured
• Findings support theoretically derived expectations
Discussion: next steps
• The KB approach/typology helps us do alternative and
better industry classifications(?)
– Compare similar industries with different KB in same
regional setting (e.g. traditional vs functional food,
forestry, specialty chemicals, ICT etc)
– Compare different industries drawing on same KB, for
verification of the robustness of the KB approach (this is
partly what we have done, but could take this further)
– Ultimately skip industry classifications based on
characteristics on the output side (e.g. producs) and
instead focus on the process side (knowledge base)
– Better understaning of related variety (e.g. Asheim,
Boschma, Cooke 2011)
Discussion: next steps
• The KB approach will benefit from cleaning the
typology, avoiding circular arguments(?)
– Mode and rationale of knowledge creation is the core of
the matter
– Spatial and social configuration of networks are
expectations/empirical questions
• How to deal with the challenge moving beyond
qualitative approach and work with larger datasets?
– Occupation data?
– Professional background of entrepreneurs?
– Other ideas?
Contact details
jerker.moodysson@circle.lu.se
www.circle.lu.se