Knowledge dynamics, firm specificities and sources for innovation

Transcription

1 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

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, low explanatory value for heterogeneity of innovation practices (also in traditional/established industries). Combine qualitative and quantitative approaches

3 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

4 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)

5 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).

6 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)

7 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

8 Pavitt s taxonomy Supplier dominated Manufacturing, agriculture, housebuilding, financial/commercial services. Inhouse 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)

9 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 innovating firms covering 26 sectors in 13 European countries (Srholec & Verspagen, 2012). Alternative explanations?

10 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?

11 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 Focus on the process rather than the outcome Trigger reactions (desire, affect etc) in minds of beholders by use of symbols and images 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

12 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)

13 Illustration: The Astonishing Tribe

14 Empirical illustrations Processes and activities Firms and industries Discussion: next steps

15 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

16 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

17 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

18 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 Reveal the mechanisms of antibodies. Formalised, rational, scientific process. time

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

20 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 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

21 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 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 time and defining (analytical): DBF in collaboration with New Jersey firm. Selection (synthetic): spinn-off DBF in collaboration with old univ dept in Lund

22 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.

23 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

24 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.

25 Expected patterns of knowledge sourcing global Analytical Synthetic Symbolic regional less formalized more formalized Source: own draft. 25

26 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

27 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

28 Monitoring fairs magazines surveys journals Mean Std. Deviation N moving media food life science moving media food life science moving media food life science moving media food life science 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. 28

29 Mobility university technical college same industry other industries Mean Std. Deviation N moving media food life science moving media food life science moving media food life science moving media food life science 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. 29

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

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

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

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

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

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

36 Knowledge sourcing through collaboration 20,7% 24,5% 24,4% 33,3% 46,8% international 54,8% 42,2% 23,9% 29,4% national regional moving media food life science Table: share of regional, national and international linkages between actors Source: own survey. 36

37 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

38 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?

39 Contact details

40 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 Collaboration within and between research units Strong codified knowledge content, highly abstract, universal Problem-solving, custom production, inductive Interactive learning with customers and suppliers Partially codified knowledge, strong tacit component, more context-specific Creative process, communication Experimentation, in studio, project teams 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