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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)

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 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








trials









time Learn how to control, select, and reproduce antibodies. Experimentation in the lab,

trial and error.

Example








trials









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








trials









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








trials









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












collaboration in food



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

[email protected]

www.circle.lu.se

mailto:[email protected]

http://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

Knowledge dynamics, firm specificities and sources … for Innovasjonsforskning... · Knowledge dynamics, firm specificities and sources for ... continuously born ... food 3.96 1.04

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