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Knowledge dynamics, firm specificities and sources for
innovation
Jerker Moodysson CIRCLE, Lund University
Presentation at the seminar ”Regional innovation in a global economy”,
University of Stavanger, Norway, December 11, 2012
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, 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
• Compatibility of knowledge (either through similarity or 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
Basic assumptions • Knowledge is important 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)
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
• Explanations based on two main dimensions – Sector specificities (e.g. the SIS approach) – National context (e.g. the NIS approach)
• Well known explanatory models – the ”Pavitt taxonomy”, ultimately building on and
further aggregating traditional sector classifications
– the ”Varieties of Capitalism” approach, taking national institutional specificities into account (LME vs CME)
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
– Manufacturing, agriculture, housebuilding, financial/commercial services. In-house R&D/engineering capabilities weak, most innovation from suppliers
• Production-intensive – (1) Mass production. Technological lead maintained by know-how, secrecy – (2) Small-scale equipment and instrument suppiers. Firm specific skills, ability
to respond sensitively to users’ needs
• Science-based – Industries aiming to exploit scientific discoveries. R&D activities of firms in
sector, underlying sciences at universities. Patents, secrecy, technical lags
• 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 • 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 the 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?
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
Disclaimer
• 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)
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 different industries
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, co-patents 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
Example
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
time Reveal the mechanisms of antibodies. Formalised, rational,
scientific process.
Example
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
time Learn how to control, select, and reproduce antibodies. Experimentation in the lab,
trial and error.
Example
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
time 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’.
Example
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
time 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.
Understanding and defining (analytical): DBF in collaboration with
New Jersey firm. Selection (synthetic): spinn-off DBF in
collaboration with old univ dept in Lund
Example
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
time Highly formalised. DBF in
collaboration with hospitals and research institutes in Stockholm
and Great Britain.
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
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
25 Source: own draft.
regional
global
less formalized
more formalized
Synthetic
Analytical
Symbolic
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
27
Monitoring
28
Table: relative importance of various sources for gathering market knowledge through monitoring. Source: own survey.
Mean Std. Deviation Nmoving media 3.00 1.29 36food 3.11 1.40 28life science 2.72 1.39 29moving media 3.19 1.39 36food 3.07 1.27 28life science 2.83 1.34 29moving media 2.44 1.25 36food 2.86 1.30 28life science 3.31 1.51 29moving media 2.31 1.21 36food 1.86 1.08 28life science 3.31 1.31 29
fairs
magazines
surveys
journals
Analytical firms rely more on formalized knowledge sources than symbolic and synthetic firms.
Mobility
29
Table: relative importance of various sources for recruitment of highly skilled labour. Source: own survey.
Mean Std. Deviation Nmoving media 2.94 1.45 35food 2.11 1.23 28life science 3.93 1.55 30moving media 2.26 1.15 35food 1.89 1.20 28life science 1.90 1.40 30moving media 4.36 .93 36food 3.96 1.04 28life science 3.87 1.41 30moving media 2.61 1.13 36food 2.93 1.30 28life science 1.77 1.04 30
university
technical college
same industry
other industries
Analytical firms recruit primarily from universities and other firms in the same industry; synthetic and symbolic firms recruit primarily from other firms.
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 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
Knowledge sourcing through collaboration
36
54,8% 42,2%
29,4%
24,4% 33,3%
23,9%
20,7% 24,5%
46,8%
moving media food life science
internationalnationalregional
Table: share of regional, national and international linkages between actors Source: own survey.
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)
• How to deal with the challenge moving beyond qualitative approach and work with larger datasets?
Contact details
www.circle.lu.se
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
The KB typology