Comparing knowledge bases: on the geography and organization of innovation

1. 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

2. 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)

3. 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

4. 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

5. Basic assumptions Applicability of knowledge Novelty Effectiveness = novelty x communicability (non-redundant cognition) Communicability Cognitivedistance

6. 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)

7. Basic assumptions Research Development Production Marketing

8. Basic assumptions Research Knowledge Invent and/or produce analytic design Distribute and market Redesign and produce Detailed design and test Potential Market

9. 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).

10. 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)

11. 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

12. Pavitt’s taxonomy • Supplier dominated firms • Manufacturing, agriculture, housebuilding, financial/commercial services. In-house R&D/engineering capabilities weak, most innovation from suppliers • Production-intensive firms • (1) Mass production industries. Technological lead maintained through know-how 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)

13. 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?

14. 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?

15. The KB typology 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

16. The KB typology

17. 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)

18. Illustration: The Astonishing Tribe

19. Empirical illustrations • Processes and activities • Firms and industries • Discussion: next steps

20. 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)

21. 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

22. 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

23. Modes of knowledge creation

24. 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.

25. 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.

26. DBFs Pharma Academia The life science value chain/problem sequence 2-4 years 2-4 years 4-6 years 1-3 years

27. Example 1 time Reveal the mechanisms of antibodies. Formalised, rational, scientific process.

28. Example 1 time Learn how to control, select, and reproduce antibodies. Experimentation in the lab, trial and error.

29. Example 1 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’.

30. Example 1 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 oldunivdept in Lund

31. Example 1 time Highlyformalised. DBF in collaboration with hospitals and research institutes in Stockholm and Great Britain.

32. Example 2 time 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.

33. Example 2 time A related application on the commercial market: functional food. Combine knowledge about function with knowledge about food production

34. Example 2 time A related application on the commercial market: functional food. Combine knowledge about function with knowledge about food production The functional part: a local DBF. The food part: a local food company. Very much trial and error to make these systems work togehter.

35. Example 2 time Highly formalised. Primarily a matter of getting scientific certification and support by researchers and physicians. DBF in collaboration with research institutes globally.

36. 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)

37. 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.

38. Expected patterns of knowledge sourcing (based on theoretical reasoning) Source: own draft.

39. 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

40. 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

41. Monitoring 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.

42. Mobility 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.

43. Figure: Knowledge sourcing through collaboration in media regional national international Source: own survey. Graphical illustration inspired by Plum and Hassink (2010).

44. Figure: Knowledge sourcing through collaboration in media regional national international Source: own survey. Graphical illustration inspired by Plum and Hassink (2010).

45. Figure: Knowledge sourcing through collaboration in media regional national international Source: own survey. Graphical illustration inspired by Plum and Hassink (2010).

46. Figure: Knowledge sourcing through collaboration in media regional national international Source: own survey. Graphical illustration inspired by Plum and Hassink (2010).

47. Figure: Knowledge sourcing through collaboration in food international national regional Source: own survey. Graphical illustration inspired by Plum and Hassink (2010).

48. Figure: Knowledge sourcing through collaboration in life science regional national international Source: own survey. Graphical illustration inspired by Plum and Hassink (2010).

49. Knowledge sourcing through collaboration Table: share of regional, national and international linkages between actors Source: own survey.

50. 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