KNOWLEDGE BASES, OPEN INNOVATION AND CITY REGIONS

Transcription

1 Paper to be presented at the Summer Conference 2009 on CBS - Copenhagen Business School Solbjerg Plads 3 DK2000 Frederiksberg DENMARK, June 17-19, 2009 KNOWLEDGE BASES, OPEN INNOVATION AND CITY REGIONS Heidi Wiig Aslesen Norwegian School of Management [email protected] Knut Onsager NIBR [email protected] Abstract: The increasing economic globalisation and the emergence of more knowledge-based economies has strengthened the attention towards industrial and regional advantages promoting learning and innovation, and further, how policy can support development of such advantages. The main elements in the concepts of regional advantages are said to be agglomeration economies, industrial knowledge bases and distributed knowledge networks (Asheim et al., 2007: Cooke and Leydesdorff, 2006). The notion of industrial knowledge bases has further on been linked to three typologies of principally different knowledge bases characterized by distinct types of knowledge flows and innovation processes. Based on register and survey data from Norway we analyse and describe important characteristics of knowledge creation and flows, innovation processes and networks in main industrial sectors after their supposed knowledge base affiliation according to international literature. We find empirical validity for the relevance of the typology as a rough categorization of different industrial knowledge bases in the private sector of Norway. In the article we also discuss what kind of implications this may have for policy and further research. It is argued for the need of further development of a more differentiated knowledge base concept, and more rigorous criterions for the classification of industrial sectors in this respect. This is important if the concept should be more useful in informing innovation policy in different kinds of regions. Key words: Regional advantage, city regions, industrial knowledge base, innovation, knowledge flows. JEL - codes: R19, -, -

2 1 Title: Knowledge bases, open innovation and city regions 1. Introduction Alongside the increasing economic globalisation and the development of a more knowledgebased economy there has been an increasing interest in the relationship between firms innovation and competitive capabilities on the one hand, and territorial embedded resources and capabilities on the other hand. This relationship is at the core in the theories of competitive advantage (Kitson et al 2004, Porter 2001) and constructed advantage (Asheim et al. 2006), the former focusing on the endogenous capacity of firms and clusters to create and sustain advantages in particular fields and the latter focusing on how regional advantages are created over time by emphasising policy support, preferably in public-private partnerships. One of the main building blocks in the theory of regional advantages is industrial knowledge bases, and the assumption that industries are relying on specific knowledge and innovation input that affect the innovation processes. One distinction has been made between three ideal types of knowledge bases; analytical, synthetic and symbolic (Asheim and Vang, 2005). As such the knowledge base concept illuminates the increasing complexity of innovation regarding different sectors use of specific internal and external knowledge sources and channels. As such is the knowledge base concept related to the concepts of open innovation (Chesbrough 2003, 2006) and distributed innovation (Von Hippel 1988). The first one gives attention to firms increasing use of external resources in their innovation activities as well as their effects through spin-offs of new firms, investments in new firms and via licence agreements. The second one is focusing on companies means and measures allowing them to capture the distributed knowledge within a wide network of actors (users, manufacturers, suppliers, research centres, and others) to solve problems and develop innovations. The increased use and need of external knowledge resources is said to give advantages to firms located in urban agglomerations, as well as it give additional regional growth effects outside the firms in these milieus (Cooke 2005, Frenken et.al. 2007). Though, spatial proximity is by no means a necessary precondition for open innovation strategies. But the tendency of firms to open up their innovation process through directing their business models so as to incorporate and manage the external knowledge relations may transform the spatial organisation of innovation. With departure of the descriptions of the knowledge based sectors in the literature, one can assume that these have different spatial prerequisites, scopes and effects, following their specific knowledge inputs and innovation processes. Put in other words, spatial proximity matters to different degrees and in different ways amongst the distinct knowledge bases. One might suggest that firms belonging to symbolic knowledge bases will be those that are most dependent on local buzz and informal knowledge flows preferably within a diversified urban agglomerations. The analytically-based sectors would be more dependent on scientific knowledge inputs from formal cooperation and collaboration with the best knowledge organisations on a global scale. Finally, the synthetic based sectors may be the most dependent on knowledge inputs from formal and informal channels to actors in their own value chains, and as such most often characterised by a more multilevel spatial scope.

3 2 In the literature it is also maintained that the concept of industrial knowledge bases has far reaching implications with respect to the role, type and relative importance of clusters and regional innovation systems (RIS), and thus provide arguments for pursuing dedicated and specific innovation support policies, sensitive to existing as well as desirable regional milieus (Asheim and Coenen 2005). The concept of industrial knowledge bases, and the trefoiled divide, has been developed on the basis of theory linked to case studies of different regional clusters. There have been less comprehensive covering studies of knowledge based sector properties in regions and nations based on surveys and register data. This paper may be an important supplement in the evaluating of the relevance of the knowledge base concept, as well as informing the development of a more specific regional differentiated innovation policy. With departure from these aspects the article focuses on the following main questions: 1) How can industrial knowledge bases and related innovation properties be conceptualized and empirically investigated? 2) What kinds of knowledge based sectors (KB-sectors) are most important in different kinds of city-regions in Norway? 3) What kind of functional and spatial innovation networks, knowledge sources and flows characterizes different kinds of knowledge based sectors in Norway? 4) What are the implications of our findings for theory, innovation policy and further research? The remainder of the paper is structured as follows. The next section presents the theoretical framework of regional knowledge bases, related functional and spatial knowledge flows and innovation networks. Section 3 defines and describes the data used, followed by section 4 where survey results are presented. The main focus is on the internal knowledge generating processes and their output across knowledge bases, followed by an analysis of functional and spatial knowledge networks, sources and flows. Finally, the last section sums up results and discusses theoretical lessons from the empirical study. 2. Theoretical perspectives and concepts Alongside the increasing economic globalisation and the assumption of emergence of a more knowledge-based economy, there has been a renewed interest of the territorial foundation of the economy in politics and science. An increasing interest has been directed towards the assumed important relationships between territorial embedded resources and capacities on the one hand, and firm s and industry s innovation capabilities on the other. Such general relationships are cornerstones in the theories of competitive advantage (Kitson et al 2004, Porter 2000) and constructed advantage (Asheim et al. 2007). While the former theory focuses on the endogenous capacity of firms and clusters that create and sustain competitive advantages in particular fields, the latter focuses on elements that create regional advantages and emphasises to a larger degree the role and impact of the public sector and policy support, preferably in public-private partnership.

4 3 2.1 Knowledge bases and open innovation One of the main building blocks in the theory of regional advantages is industrial knowledge bases (KB), which is assumed to dominate different industries (Dosi 1988, Laestadius, 1998; Tödtling et. al. 2006; Asheim and Gertler, 2005). The knowledge base is the set of information, knowledge and capabilities that inventors draw on when looking for innovative solutions (Dosi, 1988: 1126). Different industrial knowledge bases are assumed to have specific types of innovation logics, innovation processes, dominating knowledge flows and interaction, and finally innovation outcome. The knowledge base distinction takes account of sectors knowledge creating processes as well as its socio-spatial implications as it looks at the interplay between actors and the knowledge that is created, transmitted and absorbed. A distinction has been made between three ideal types of industrial knowledge bases; analytical, synthetic and symbolic (Asheim et.al. 2007). The analytical knowledge base (science based) includes R&D-intensive industries and activities relying highly on scientific knowledge. Knowledge input and output are often codified and networks between industry and knowledge-organisations are important. New knowledge is created and much learning is based on the interaction with the knowledge infrastructure and on the use of new scientific or technological, codified knowledge. Industries mainly relying on analytical knowledge bases are said to be less sensitive to geographical distance and to benefit from global networks as well as local collaboration since important knowledge inputs are codified. The synthetic knowledge base (engineering based) dominates in more traditional industries, relying mostly on practical skills. Knowledge results from experience gained through learning by doing, using and interacting. Learning and innovation thus occurs mainly trough application of existing knowledge, applied research and development, and trough new combinations of knowledge. Knowledge is only partly codified, and tacit knowledge is more important than it is in the analytical knowledge base. Since tacit knowledge is of great importance, these industries are more sensitive to geographical proximity and more dependent on local collaboration than more analytical knowledge based industries. The symbolic knowledge base is related to creative industries that are characterized by image creation and knowledge involved and transmitted in aesthetic symbols, images, sounds etc. This involves a kind of highly complex, dynamic and tacit form of knowledge. Learning mainly takes place as learning-by-doing on the job. Innovations can be seen as market oriented innovations, where the innovative element may be hard to identify. Since tacit knowledge is of importance it is maintained that these industries are more sensitive to geographical proximity and more dependent on local collaboration than more analytical knowledge based industries. In spite of that also codified knowledge are used in these industries, it is often maintained that because of the importance of tacit knowledge in creative industries these are also more sensitive to geographical proximity and more dependent on local collaboration than analytical knowledge based industries. The knowledge base concept illuminates the increasing complexity of innovation and focuses on sectoral differences in firms use of external knowledge to complement their in-house innovative activities and on how their innovation processes are distributed among various partners. This is related to distributed innovation processes as a concept focusing on means and measures allowing companies to capture the distributed knowledge within a wide network of actors (users, manufacturers, suppliers, research centres, and others) to solve a technical problem and develop innovations (Von Hippel 1988). The concept of distributed knowledge is

5 4 related to the concept of open innovation (Chesborough 2003, 2006) directing attention to the fact that firms to an increasing extent base their innovation activity on research and development activities, technical change and competence from external resources (inputfactors), as well as carry out much of their innovation activities through spin-offs of new firms, by investments in new firms and via licence agreements (output-factors). The open innovation strategy is interpreted as the use of external knowledge and external commercialisation (Chesborough 2006). The open innovation model as a firm strategy means more use of the environment to bring in research results, ideas and knowledge, as well as to commercialise internal knowledge outside the firms. The claimed trend towards increased use of open innovation strategies would entail an intensification of the firms external knowledge relations and dependencies on available knowledge resources. The trefoil of knowledge bases is a typology which describes principally different forms of open innovation strategies and outcomes. Firms relying on analytical knowledge are supposed to be most dependent on input of scientific knowledge distributed through formal collaboration with R&D-organisation as well as through recruitment of higher educated people from related knowledge organisations and firms. Product innovations, patents and spin-offs are supposed to be important outputs from these sectors. Firms relying on synthetic knowledge are supposed to be most dependent on different kinds of experienced based knowledge like know-how and know-who, and learning-by doing, using and interacting related to specific kinds of engineering, crafts etc. Beside embodied knowledge coming into the firms by new machinery and recruitment of personal, important inflows of knowledge are distributed within networks and alliances with value chain partners. Incremental product- and process-innovations are supposed to be the most important output factors, which may give productivity gains but weak or no growth within the sector. Finally, firms relying on a symbolic base on the other hand, are supposed to be more dependent on non-codified and experienced based knowledge distributed among a heterogeneous group of actors and industries in temporary project organisation and local buzz institutions. Ample supply of creative workers and entrepreneurs are import input factors, while product-and service innovations, new firm formations as well as new sector employment may be important outputs. 2.2 Spatial prerequisites and scopes The increasing complexity of knowledge input and innovation, and the supposed trend towards more use of open innovation strategies, may have some spatial implications. Spatial proximity between actors is by no way a necessary precondition for open innovation strategies; however the trend towards more use of open innovation may give some innovation and growth advantages to firms and clusters located within agglomerations (Teirlinck, P. and A. Spithoven 2008, Cooke 2005). In general the theory of agglomeration economies focuses on the advantage of co-localization for knowledge spill-overs and innovation, as well as reduced transactions costs and unity cost in infrastructure (Marshall 1938, Hoover 1948, Malmberg and Maskell 1997). Two main types have received attention. On the one hand is the large city regions characterized by a high diversity of industries and a large pool of knowledge resources. By possessing diversity and different specializations, urban areas have a main advantage in offering possibilities for picking and mixing knowledge inputs as and when they are needed and the economic and social diversity packed into limited space may facilitate random and serendipitous contact among people (Simmie 2003, Jacobs 1969). Firms in such regions may have the possibility to

6 5 draw on several local knowledge sources of related firms, suppliers and customers, knowledge organisations and services, skilled labour and venture capital, as well as the possibility to easily find new commercialisation possibilities locally. The possibilities for face to-face-contacts creates an important advantages of urban areas and labour mobility among highly qualified professional and technical workers which contribute to the sharing and diffusion of knowledge, is also more likely to occur within urban labour markets (Storper and Venables 2003, Florida 2002). On the other hand, smaller city-regions do not have the same diversity and volume of industries and knowledge resources, but often localisation economies related to a specific agglomeration or cluster of one/few industries. Local knowledge sources within these specific industries and milieus may be substantial, but also significant limitations regarding the local offer of specialised knowledge providers, suppliers, demanding customers and markets. These regions may also face limitations as a result of lock-in- problems. Innovative firms in these regions may compensate local deficits of knowledge sources with more use of extra-regional channels either by closed or open innovation models. The concept of open innovation strategies has been developed with departure from large firms innovation strategies with particular focus on their external networks (Chesbrough 2006). These aspects should not a priori be limited to spatial proximate relations or firms in agglomerations. But the tendency of firms to open up their innovation process through directing their business models so as to incorporate and manage the external knowledge relations may transform the spatial organisation of innovation. Cooke (2005) maintains that open innovation is one of the key concepts to explain how regional innovation systems, and clusters within them, have to be organized to be globally competitive. But one recent analysis from Belgium found that firms in the less urbanized areas have more open business models for innovation reflected in higher presence of co-developing innovators and out-sourcing (Teirlinck and Spithoven 2008). But the association between urban structure and open innovation seems to need more investigations regarding the importance of input and output factors, as well as different kinds of formal and informal knowledge networks and channels, respectively. What then are our expectations with regard to urban structure (type of agglomeration) and open innovation strategies among our KB-sectors? One could assume that the different KBsectors have different spatial prerequisites and scopes as they have different knowledge inputs, channels and endowment opportunities. One could suppose that spatial proximity matters in different ways and to different degrees amongst the distinct KB-sectors. Also, if the KB-sectors have systematically different localisation patterns, knowledge inputs and innovation networks, one hypothesis may be that their dependencies of agglomeration economies in large and small city regions respectively will be different. One might suggest that firms belonging to the symbolic knowledge base will be the most dependent on local buzz and of informal knowledge flows in their own diversified urban agglomeration, while the analytically-based firms would be more dependent on knowledge input through formal collaboration with knowledge organisations on a global scale. Finally, synthetic firms may be more dependent on knowledge inputs from formal and informal networks with actors in their value chains, and as such will be characterised by a multilevel spatial scope. Based on these arguments, the subjects for further investigation is what degrees and kinds of open innovation strategies firms in the different KB s mainly use, i.e. what kind of external

7 6 knowledge sources and channels are important, and further what kind of spatial scopes are most important. 2.3 Analytical framework The theoretical perspectives and concepts presented above are integrated in our analytical framework (table below). The trefoil of knowledge bases is used as a main divide in analysing different groups of firms knowledge sources, partners, networks and channels of importance for innovation, as well as what kind of spatial scope these networks and channels do have. Table 1. Analytical framework. Theme Analytically based industries Synthetic industries Symbolic industries Types of knowledge and innovation processes Functional linkages; Important sources and channels of knowledge; Spatial linkages: Important spatial scopes and milieus and Sensitivity to distance and the notion of open innovation Scientific codified knowledge given through patents or other forms of documentation from the academic world. Dominated by Know-what and Know-why Knowledge comes into the industry through formal relations linked to R&D collaboration and other types of transfer of analytical knowledge. Urbanisation economies and global R&D-pipelines. Is to a larger degree dependent on national/international relations than regional Especially non-codified knowledge where much is linked to craft and traditional engineering based knowledge. Dominated by Knowhow and Know-who. Learningby doing, using and interacting is important. Much of the knowledge that comes into the industry is embodied in person and new machinery. Knowledge also flows through strategic alliances with other industrial actors. Actors along the value chain (customers and suppliers) and competitors are most important. Localisation economies and spatial multilevel learning networks. Is to a larger degree dependent on regional resources than international Non-codified knowledge and practical experienced based knowledge with some kind of aesthetic and artistic form of expression is important. Dominated by Know-how and Know-who. Learning-by doing, using and interacting is important. Learning through projects consisting of different creative occupations, knowledge transfer also happens through more informal channels. Urbanisation economies and local buzz- spillovers. Is to a larger degree dependent on regional resources than international The trefoiled distinction should not be regarded as a strict categorization but an analytical tool in order to capture assumed significant differences between knowledge bases. But in empirical terms, it is reasonable to expect a more complex pattern, i.e. that firms and industries may be relying on different combinations of more than one knowledge base. Moreover, these typologies have been developed on the basis of several case studies of industrial clusters in different regional and national contexts, but not tested systematically on more comprehensive covering datasets in different national contexts. In general empirical evidences on industrial knowledge bases in different national and regional contexts would be important in order to develop more suitable national and regional differentiated innovation policies.

8 7 3. Defining knowledge base sectors and presenting the city-regions 3.1 Definitions and delimitations of knowledge base sectors and regional units In international literature the trefoiled type of knowledge bases are often illuminated by cases of different industries and clusters (Asheim et.al. 2007, Asheim and Coenen 2005). Initially we have divided almost the entire private sector (exclusive construction, energy/water supply, transport) of NACE-industries in four main Knowledge Base Sectors (KB-sectors). In general our division follows the trefoiled types of knowledge bases reported in international case literature, but we made a further divide of the synthetic KB-sector into one synthetic manufacturing sector, and one synthetic service sector (see table 2 below). The reason for this is a supposed substantial difference in the knowledge and innovation forms and networks regarding manufacturing and service respectively. What we denote as analytical KB-sector and synthetic KB service are synonymous with the often used term of knowledge intensive industries after international classification standards (OECD 2001) based on indicators of high R&D-intensity and employment of higher educated workers respectively. Additionally we have also a large reference sector consisting of a diverse selection of synthetic industries (primary industries, low-tech manufacturing, retailing, hotels and restaurants). In analysing the KB-sectors spatial scope and properties we use functional regions as the main territorial unit. This consists of a central place and its local labour marked. Initially we distinguish between five classes of these functional regions according to their number of inhabitants: metropolitan regions (more than 1.0 mill. ihb), large city-regions others ( ihb.), medium-sized city-regions ( ihb.), small city-regions ( ihb.), small place regions (less than ihb). In general this is in line with international classification standards (United Nations Centre for Human Settlements) except for the metropolitan region and small place regions which we have separated as distinct categories Data foundation The main data source is a web-based survey (CKI 1 ) carried out in the end of The questionnaire was sent to a random selection of firms (with more than 1 employee), though mainly to firms in the knowledge intensive sector. The sample consists of firms with approximately complete answers (26%), leaving us with 1302 complete answer (see table 2 below). The questionnaire includes 20 questions on different topics regarding the firms establishment, knowledge bases, innovation forms, networks and barriers. The sample is fairly representative of the knowledge intensive firms regarding sub-branches, firm-sizes and localisations. Because of the voluntariness to answer, the self selection process may have resulted in a somewhat biased sample (compared to a survey based on an obligation to answer as in public statistics by Statistics Norway). If answering the respondents will have a summary of the results free of charge. This may imply a possibility of overrepresentation of the most motivated respondents and/ or knowledge searching or monitoring firms. 1 The CKI-survey is a part of the project: Cityregions, knowledge bases and innovation support systems (CKI), which has been founded by the Research Council of Norway in the DemosReg program.

9 8 Table 2.The division of KB-sectors and numbers of respondents in the survey (Source: Statistics Norway and CKI/NIBR-survey 2007) Number and shares of firms in the: Main KBsectors: Analytical KBsector Synthetic KB- Survey (CKI/NIBR- 2007) Public firm register (Stat.Norway) Our divide of KBsectors: Industries (nace): Frequency Percent Frequency Percent Analytical High-tech and mediumhigh tech manufacturing , ,7 Synthetic - Manu Medium-low-tech and low ,8 tech manufacturing ,3 Symbolic KBsector Diverse KBsectors others Symbolic Div.synth.others Creative services Primary industries, oil/gas, hotell/rest, retailing. sector Synthetic - Serv KIBS (incl.ict, consulting) , , , , , , ,0 Additionally we also use full covering register data from Statistics Norway in describing our KB-sectors localisation patterns and representation in different regional classes. 3.3 Localisation and regional specialization Norway is a small and open resource-based knowledge economy with a modest high-tech sector but a large low-tech sector and related exports compared to other Nordic countries. However, about 20 percent of employees works in the knowledge intensive sector consisting of synthetic services of KIBS (17%) and a minor part in the analytical high-tech (1%), and medium high-tech manufacturing (2%). It is the synthetic service sector which has got the highest growth rates in employment in Norway the last ten years. The knowledge intensive sector is concentrated in the urban regions. The synthetic services are overrepresented in the larger city regions, and all of these regions are specialised and have high diversity and related variety in these industries. The synthetic manufacturing and analytical KB-sectors in particular, are overrepresented in the small- and medium sized cityregions (table 3). One third of these city-regions are specialized in analytical manufacturing. Synthetic manufacturing and diverse synthetic industries is overrepresented in the small place regions, which for the most is specialized in resource-based industries. Table 3. The KB-sectors representation in the main classes of regions - Norway 2008 (LQ>1 is overrepresentation and indicates regional specialization) (Source: Statistics Norway/Firm-register) Metropolitan region Large city-regions Medium city-regions Small city-regions Small place regions Analytical 0,6 0,9 1,5 1,1 0,7 Synthetic - Manu 0,4 1,1 1,2 1,4 1,4 Synthetic - Serv 1,6 1,1 0,8 0,6 0,4 Symbolic 1,5 0,8 0,9 0,7 0,9 Div.synth.others 1,0 1,0 1,0 1,0 1,1 4. Knowledge creation and relations among KB-sectors This section will present the analysis of the survey focusing especially on what characterises the different knowledge bases with regard to ownership, sales and innovation (4.1), how they

10 9 develop their competence (4.1.1), and the types and scopes of their knowledge networks (4.2, 4.3) Characteristics of KB-sectors The organisational structure and size of firm differ among the KB-sectors (Table A1, A2) as firm in synthetic manufacturing have the largest firms, the smallest firms are found among the service firms, especially industries belonging to the symbolic KB-sectors.. In average 22,3% of the firms report to be part of an enterprise group, the highest share is found among firms with an analytical knowledge base followed by the synthetic manufacturing firms. It seems to be less common for synthetic services and symbolic KB-sectors to be part of an enterprise group. The different organisational structures (and organisational design) between industrial knowledge bases can be influenced by the nature of tasks and industrial process being carried out (Woodward, 1965). Type of start-up differs significantly between the KB-sectors as almost all firms representing a symbolic knowledge base were established by single individuals, while the share is significantly lower for analytical firms (92,0%, 78,7%)(Table A2). As much as 21,3% of the firms representing analytical firms have been established as a strategic outsourcing/separation from another institution/firm, possibly suggesting that the firms represents something new (an innovation) that have been placed outside the boundaries of an established institution/firm. These differences can point to the variety and complexities in the type of knowledge and thereof the necessary organisation and management structure that follows the specific KBsectors. Table 4. New firms and innovations in the period ?(N=1217). In percentage. KB-sectors Share of firms established after 2004* Introduced new or substantially improved products/servic es* Taken into use new or substantially improved working or production processes* Taken into use new or substantially changed strategies for the firm Carried out new or substantially changes in the organizational structure Analytical 15,3 78,7 63, ,3 24,9 Synthetic - Manu 17,2 65,8 52,7 45,2 41,8 11 Synthetic - Serv 44,0 70,5 56,5 49,4 37,5 6 Symbolic 37,6 63,5 55,1 44,9 39,9 2,2 Div.synth.others 26,1 65,3 49,5 44,6 38,3 4,3 Total 31,4 68,8 55,1 46,4 39,9 8,2 * Significant at the 0.05% level Share of firm with reported patents in the period * The table above shows the results of the KB-sectors innovation efforts by presenting the share of new firms, shares of new products, processes, strategies, organizational change and patents in the period Looking at the distribution of young or relatively newly established firms, the share is marked higher for the service based KB-sectors than for analytical and synthetic manufacturing. This can be explained by the fact that new firm formation in the service industries demands lesser start-up costs as much is linked to a type of intangible service offered (especially then for the knowledge intensive service industries where consultancy dominates). Analytical and synthetic firms often demand a more complex structure around its main activities. In general innovation activity is high among the respondents, as can be expected due to that only knowledge intensive firms are represented in the survey. Firms in the analytical KB-

11 10 sector have the highest shares of firms reporting activity on all innovation indicators. Servicefirms in synthetic KB-sector also have very high rates of product and process innovations, however much lower rates of patents. This can be due to the possibility to patent the services they provide, or that the liability to think in terms of patents is less developed in this industry KB-sectors competence strategies Different knowledge bases are supposed to have different ways of exploring and exploiting knowledge and as such differ with regard to how they approach learning and competence building. The table below gives background information that can give some indication of how these processes are carried through in the different KB-sectors. Table 5. Average share of R&D as percentage of total sales, share of employees with higher education, and share of firms with R&D units. (N=1226). In percentage. KB-sectors Average R&D share as percentage of total sales Share of firms with own R&D department Share of employees with higher education Analytical 9,9 39,4 21,3 Synthetic - Manu 3,7 20,6 25,3 Synthetic - Serv 11,0 18,4 13,6 Symbolic 4,7 3,1 4,0 Div.synth.others 2,1 7,0 6,7 Total 6,8 17,3 12,8 The table shows large differences between the KB-sectors when it comes to R&D activity, the organisation of this activity as well as the share of employees with higher education. KBsectors in knowledge intensive services have the highest R&D share, closely followed by the analytical firms. Synthetic manufacturing and symbolic knowledge bases have much lower shares, however also reporting R&D activity. The share of firms with an R&D unit differs among the KB-sectors, suggesting different forms of organising developmental activity. The findings show that KB-sectors with high R&D shares do not necessarily organise these activities in an R&D unit. Synthetic services have the highest R&D share, however, only 1/5 th report having an R&D unit, whereas analytical firms report to have a lower R&D share but close to 40% have an R&D unit. Research and developmental activity are organised and managed differently according to industrial knowledge base. The human capital aspect, understood here as the share of employees with higher education, also differ between the industries. Synthetic manufacturing and analytical industries have the highest shares, in the symbolic knowledge base the formal education of the employees are much lower, suggesting that other human capital factors than formalised higher education is valued in these KB-sectors. There are significant differences between the KB-sectors with regard to what they see to be the most important factor with regard to developing core competence (Table A3). Developing competence in the daily work is seen as very important by a larger share of firms from industries representing services than for the manufacturing industries, suggesting different modes of competence building taking place, where learning by doing and interacting in the daily work is more prevalent. Developing core competence can also be linked to demanding customers or important suppliers, this is reported as being very important for a much higher share of analytical firms (65,7%) than any other of the knowledge bases. These firms are characterised by knowledge based on know why and how suggesting that their products are not off the shelf but rather new knowledge that is developed over time in close collaboration with demanding customers and specialised suppliers. This group also have a

12 11 higher share of firms emphasising systematic R&D internally and collaboration with R&D institutions, emphasising the importance of research and development at the core of the knowledge formation process. Among firms with a symbolic knowledge base, competence is gained Individually in the daily work, suggesting a process of learning-by-doing at the work place linked to a type of complex and tacit form of know-how typical for this group of firms. External formal collaboration as a factor in competence development are to a lesser degree important for this knowledge base compared to the others, however, as much as 48% of the firms say that Collaboration with demanding customers/important suppliers are very important. 4.2 FunctionaI knowledge linkages In order to map to the degree of openness in KB-sectors knowledge acquisition, we were interested to look into the type of external sources they related to for knowledge input. Less than half the firms in the survey (43,7%) have carried out innovation activity in collaboration with external actors the last 5 years (Table 6). Close to 2/3rds of firms in the analytical knowledge base have taken part in such collaboration, the lowest share of firms is found among the symbolic knowledge bases. This shows that formal innovation collaboration varies among KB-sectors and that these formal linkages is to a lesser degree used by some KBsectors than others, possibly suggesting other forms of knowledge exchange, and possibly also other channels than formal linkages. Table 6. Share of firms with innovation collaboration and share of firms with different innovation partners. In percentage. (N=1207). - with Share with - with firms in Technology/ innovation - with - with - with R&D - with same innovation collaboration customers suppliers institutes consultants enterprise centres - with the pulic support system - with competitors KB-sectors Analytical 62,7 80,2 77,4 65,4 51,4 50,5 48,6 36,4 24,3 Synthetic - Manu 46,9 82,4 73,5 62,3 50,0 54,4 55,1 34,8 42,0 Synthetic - Serv 44,7 81,7 60,2 52,2 46,2 41,4 37,6 38,2 30,6 Symbolic 38,4 75,0 70,6 23,5 44,1 44,1 13,2 23,5 38,2 Div.synth.othe rs 33,0 77,8 81,6 45,5 57,6 64,6 34,3 45,5 47,5 Total 43,7 79,9 70,7 51,2 49,6 49,4 38,4 36,9 35,0 Regardless of KB-sector, customers and suppliers are the most relevant collaboration partner. Synthetic manufacturing firms differ in that they have more collaboration within the enterprise, with technology centres/innovation centres and with competitors than any of the other KB-sectors. This KB also has relatively high shares of collaboration with R&D institutes, however, the highest share is found among the analytical KB-sectors, which also use a much more diversified set of innovation partners than any other KB-sector. For the symbolic KB-sectors it is mainly customers and suppliers that are relevant as collaboration partners, smaller shares of firms have external consultants as partners. Firms in the survey where asked to report the different types of sources of relevance to innovation and channels of information and knowledge of importance for innovation. The most relevant sources for innovation for all KB-sectors are clients, sources within the enterprise and suppliers (table A4). Sources within the enterprise and R&D/higher education institution is a more relevant source for analytical KB-sectors than any of the other KB-sectors. Synthetic manufacturing have the highest share reporting competitors as relevant

13 12 sources. The symbolic KB-sectors differ in that a larger share of firms emphasise internet, other informal meeting places and associations/conferences as important sources for innovation, much in line Florida s thoughts on the relevance of a peoples climate and the importance of the buzz of the city as important for the innovation activity in the creative industries (Florida 2002). Table 7. Share of firms using different relevant kinds of channels for information and knowledge. (N=1120). In percentage. KB-sectors Informal contact with suppliers/client s Purchase of goods and services Observation of competitors, branch or market Formal arrangements with suppliers/client s Recruitment of creative and qualified personnel Informal contact with R&D/higher education inst. Ownership or board representation in other firms Formal contact with R&D/higher education inst. Analytical 92,5 90,0 85,6 86,3 84,4 60,0 35,0 52,5 Synthetic - Manu 93,3 91,9 88,9 82,2 75,6 52,6 39,3 43,0 Synthetic - Serv 89,3 77,2 83,2 81,5 76,6 51,3 42,1 36,8 Symbolic 91,1 91,1 88,0 82,3 75,3 43,0 45,6 27,2 Div.synth.other s 93,4 93,0 88,6 89,7 78,0 44,0 49,8 33,7 Total 91,5 86,6 86,3 84,4 77,8 49,7 43,1 37,7 The most important knowledge transfer mechanisms among all KB-sectors is the informal contact with the suppliers/clients, followed by purchase of goods and services and observation of competitors/branch or market. Analytical KB-sectors have the highest share of firms reporting that recruitment of creative and qualified personnel is important as channel for information and knowledge for innovation. Analytical firms also differ in their emphasis on informal and formal contact with R&D/higher education institutions. This suggests that the formal and informal relations between the partners are somewhat interlinked and that the process probably can be started either from an informal relation moving towards a formal business relation or vice versa, and that trust is an important part of the relations. Synthetic manufacturing firms also report that an important channel of importance to innovation is observation of competitors, branch or market suggesting that being able to benchmark own innovations or line of business with competitors, with the branch in general or getting a feel with market trends is of great relevance as innovation input. 4.3 Spatial linkages This section will present the spatial linkages of the different KB-sectors, by first focusing on their formal linkages to head offices, then have a look at the reach of their sales, for thereafter present the spatial distribution of their innovation networks, both formal linkages and the more informal relations Spatial linkages in ownerships and sales Firms being part of an enterprise group that do not have their main office locally, choose the Oslo region for locating their head offices (Table A1), in line with research stating that many of the largest companies head offices seem to concentrate in large metropolitan regions (Aslesen and Jakobsen, 2007). Synthetic manufacturing have a relatively large share of firms being located in other parts of Norway, outside the large city centres (21,1%), possibly due to

14 13 that this is where the majority of their activity is located. For the service industries (both synthetic services and symbolic) other Nordic countries seem to host their mother companies. Analytical firms in Norway seem to be part of organisations with a much more international orientation since as much as 24,3% of those being part of an enterprise group have their head offices in Europe (outside the Nordic countries) or rest of the world (13.5%). Table 8. Share of firms sales to different markets. (N=1302). % average share of % average to rest of national sales to local market market % average Nordic countries % average international market Analytical 22,7 39,4 9,2 28,6 Synthetic - Manu 35,4 52,8 5,3 6,5 Synthetic - Serv 47,7 42,5 3,7 6,0 Symbolic 60,1 33,4 2,8 3,7 Div.synth.others 52,1 36,9 3,7 7,3 KB-sectors serve different geographical markets. KB-sectors within services (synthetic services, symbolic) have the largest average sales to local markets followed by rest of the national market. On the other hand, analytical firms have the greatest reach of their sales in that their largest share of sales goes outside the region, to other markets in Norway and to rest of the world suggesting an orientation of products beyond the domestic market. Synthetic manufacturing have in average their largest share of sales outside the region to rest of the national market (52,8%), however also reporting more than 1/3 rd (35,4%) of sales locally Spatial knowledge linkages Table 9. The location of innovation partners for firms in different KB-sectors. Own region (municipality+neig KB-sectors: hbour municipality) Metropolitan region Other large city regions Rest of the country Other parts of the Nordic countries Rest of the world Analytical 28,3 16,7 13,6 13,3 10,9 17,3 Synthetic - Manu 28,0 15,4 22,1 18,6 6,9 9,0 Synthetic - Serv 28,5 22,0 12,9 15,3 8,4 12,9 Symbolic 36,4 30,5 8,5 11,4 5,9 7,3 Div.synth.others 35,1 20,7 11,4 20,4 5,3 7,1 Total 30,5 20,6 13,5 15,9 7,9 11,6 The spatial configurations of formal innovation collaboration amongst KB-sectors differ (Table 9). The most common innovation partner is found locally (30,5%), regardless of KBsector, being especially evident for symbolic firms (36,4%). The greatest difference among the KB-sectors is found with regard to the share of firms reporting innovation partners in Rest of the world, where analytical firms have the highest share, followed by synthetic services. Symbolic industries have the lowest share of innovation collaboration with global partners, besides own region, the metropolitan region are the most used location for these industries innovation partners. Synthetic manufacturing collaborate mostly with partners outside the metropolitan region.

15 14 Table 10.Share of firms reporting to use different innovation sources (most important) by location. (N=1124). In percentage. KB-sectors Own region Oslo-region Bergen/Tr.h/Stava Rest of the country Rest of the world nger Nordic rest Analytical 24,6 15,1 16,0 25,6 11,6 11,1 Synthetic - Manu 27,3 17,0 21,5 13,3 14,8 6,2 Synthetic - Serv 31,3 23,7 16,0 15,3 11,4 7,5 Symbolic 31,3 28,3 15,2 13,1 7,0 5,6 Div.synth.others 34,0 19,8 20,0 9,1 8,0 5,3 Total 30,5 21,3 17,5 14,7 10,4 7,1 Looking at the spatial distribution of sources of information and knowledge of relevance for innovation for the different knowledge bases, there are certain patterns that evolve. Firms belonging to the analytical KB-sector have the lowest share of firms reporting that such sources are found locally in own region, however, almost every fourth firm report this. This KB-sector emphasise Rest of the world as a source of information of importance to innovation, suggesting that they are able to combine both local and global innovation sources. For synthetic manufacturing industries own region is reported to be the most important location for sources of innovation input followed by other regions outside the large cities. In this group only 13,3% of the firms emphasise the rest of the world as location for innovative sources. In this knowledge base firms have a stronger national orientation with regard to information and knowledge of relevance to innovation, possibly related to the mode of learning and innovation that is dominant in these industries (learning by doing and the emphasise on tacit knowledge). Among the service industries relying on synthetic and symbolic knowledge bases the location of sources also differs from the analytical KB-sectors, having the highest share of relevant sources in own region (as for synthetic manufacturing) and in the Oslo region. This suggests that these knowledge bases again is dependent on the local milieu for innovation input, but at the same time is oriented towards the main capital of Norway (not the other city areas to the same degree) as an important source of information and knowledge for innovation, highlighting the importance of the metropolitan region as important for innovation input. 5. Summary of findings and some implications for policy In general our empirical analysis shows that there are differences between the KB-sectors on many of the dimensions introduced in the analytical framework. The KB-sectors differs both with regard to type of industrial location, internal knowledge formation and organisation, innovation output and with regard to innovation linkages and their spatial configurations. In this section we will summarise some of these empirical findings in light of the analytical framework, and close the section with some words about what kind of policy implication this may give. Localisation patterns and regional agglomerations The different knowledge base sectors consist of industries with systematically different localisation patterns and regional distribution in Norway. The symbolic and synthetic service sectors are concentrated and overrepresented in the larger city-regions, and in particular the metropolitan city-region. All the larger city-regions are specialised in symbolic KB-sectors and/or synthetic services. These regions have the last ten years ( ) reinforced their

16 15 role as growth centres for these types of industries and related knowledge bases. The analytical KB-sectors are concentrated and overrepresented in the small and medium sized city-regions, while the synthetic manufacturing KB-sectors are over-represented in smaller city-regions and small place regions. Medium- and small city-regions have during the last ten years reinforced their role as employment and development centres for the analytical and synthetic manufacturing industries in Norway. The territorial distribution of KB-sectors follows in general the regional division of labour in Norway, between the knowledge intensive service sectors concentrated in the larger urban regions, while manufacturing in general is concentrated in the smaller urban regions. However, the analytical manufacturing sectors are for the most located in medium- and smaller city-regions though with geographical proximity to the metropolitan and the larger city regions, which they are partly integrated into. KB- characteristics and output The knowledge base approach assumes differences between KB-sectors with regard to the type of competence and learning that goes into the knowledge formation process. Our analysis show that start-up type and organisational design differs between the KB-sectors suggesting differences in complexity and the type of industrial processes being carried out. The difference between the KB-sectors in their emphasis on R&D and formal education illuminates the essence of the knowledge based approach; sectors can be knowledge intensive without necessarily using these inputs. The difference is also shown in the organisation of the knowledge formation process; more than twice as many of the analytical firms report to have R&D departments compared to firms in synthetic manufacturing even though the R&D intensity is higher in the synthetic manufacturing firms. This shows that R&D indicators as a gives little real understanding of how the knowledge formation process actually happens. The knowledge formation process in synthetic manufacturing often happens on the shop floor, continually transforming highly integrated systems, while for analytical firms this process often have a focus on more isolated phenomena, and therefore can be organized differently also possibly making in easier to target. The share of new firms (established after 2004) differs among the KB-sectors, possibly suggesting a difference in the complexity of the knowledge bases, different barriers to entry (capital and human resources requirement) and as such a more comprehensive gestation period between the knowledge bases. Using patents as an indicator of the newness of the innovations, the analytical KB-sectors have by far the highest share of firms reporting this as well as the highest share of firms reporting innovation in almost all the other innovation output indicators. The analytical firms have then a broader set of innovation outputs and a higher share of new innovations than the other KB-sectors, much in line with our initial thoughts. Functional knowledge linkages What have then the analysis showed with regard to dependence on a distributed set of knowledge resources? Do some of the KB-sectors show signs of more open innovation strategies than the others? Analytical firms have the highest share of firms with innovation collaboration as close to 2/3 of the firms report this. This form of interaction can be seen as important for knowledge exchange between actors and are usually more durable and interactive than other modes of knowledge exchange. The results show that analytical firms rely on a distributed set of

17 16 partners, potentially providing complementary knowledge into their innovation processes. Synthetic manufacturing firms report a broader set of relevant partners than the above mentioned including also collaboration within the enterprise and with technology/innovation centres. The service based sectors have much fewer innovation linkages, the most important being customer and suppliers. Looking at the transfer mechanisms and knowledge relations that the different KB-sectors are involved in, there are also differences between the KB-sectors. The most important exchange relation for all KB-sectors are the informal relations with suppliers and clients, observation of competitors, followed by the more traded and formal relations with suppliers/clients. Since both the traded and untraded forms of knowledge exchange are important for the KB-sectors, it could mean that the relationships goes beyond a more static knowledge transfer between the actors to a more collective learning process and as such more durable and targeted innovation network built on factors such as trust and reciprocity in the relationships. These types of linkages are reported by a higher share of analytical firms, suggesting differences between the KB-sector with regard to the closeness of knowledge relations. Overall, the analytical KBsectors differ from the others in that a larger share of firms report to use more channels, suggesting distributed knowledge transfer mechanism as well. Observation of competitors as channels for innovation input is valued highest by the service based KB-sectors, possibly suggesting that being in proximity to competitors, being able to benchmark and see what others do, have impact on own knowledge formation processes among the service providers. Spatial configurations of links There are different spatial delineations and interdependencies among the KB-sectors and their external knowledge sources. The predominant knowledge source for all the KB-sectors is found in own region linking importance to proximate actors where relations and knowledge have been built up over time. However, the study has revealed that the regional level, in combination with various forms of networks and innovation systems, is necessary to remain innovative and competitive as the KB-sectors differ with regard their extra-regional reach. The article has shown that analytical firms knowledge and information for innovation can travel further ; internal knowledge formation together with interaction with regional knowledge suppliers is supplemented by specialised knowledge from global actors. Synthetic manufacturing has a greater dependence on localized learning processes stimulated by geographical, social and spatial proximity without much interaction with knowledge organizations, this knowledge based being characterised as a territorially embedded regional innovation network (Asheim and Isaksen, 2002). The service based industries find their innovation sources in the metropolitan region of Oslo (as well as locally), suggesting the city region as a relay station in a world of flows structured around flows of people, images, information and money (Amin and Thrift 2002) being both local and global at the same time. These aspects of the metropolitan region of Oslo seem especially important for symbolic KBsectors and synthetic service firms. One main finding is that innovation sources are local, national and international and all these dimensions needs to be taken into account in order to understand the dynamics of the knowledge formation process in knowledge intensive industries in Norway. Further, knowledge can be transferred to the actors in many different ways through both formal and informal channels. The above indicates the differences in spatial delineations of the different KB-sectors having implications for how one is to work with regional innovation policy in city regions dominated by different KB-sectors.

18 17 Open innovation and urban regions The empirical survey validate that the open innovation model are of substantial importance for firm strategies irrespective of KB-sector or agglomeration size. In all the KB-sectors the firms base their innovation activity on external knowledge inputs in combination with intrafirm competencies which is of great importance for their capabilities. The analytical KBsector is multifunctional and multi spatial, including international links and markets to a much larger degree than other KB-sectors overrepresented. These KB-sectors are mostly located in smaller city regions with limited knowledge resources relevant for their specific industrial base. Their very open innovation strategies can be a way to compensate for the lack of regional resources though national and international monitoring, networking and collaboration. On the other hand it is important to note that the analytical based firms, and partly the synthetic manufacturing firms, which mostly are located in the smaller city regions, use open innovation strategies, however, the regional growth effects related to new firms formations and new employment are much weaker than in the synthetic services and symbolic industries, suggesting the need for strategic innovation policy. The symbolic service sector is to a much larger degree dependent on knowledge inputs, innovation collaboration and markets at the regional level. This is partly also the case for the synthetic sectors, but for these the national level is also very important. The symbolic and the synthetic service sectors consist of industries which are overrepresented and mostly concentrated in the large city regions, which accommodates substantial regional knowledge resources and demanding markets. It is obvious that these regions gives better conditions than smaller city regions for open innovation processes. When looking at the innovation outputs in general it is also obvious that the growth effects of open innovation measured in new products, radical innovations and new firms are significantly higher in the larger city regions than in the smaller city regions. Our study then support that larger city regions do have knowledge advantages and growth effects which exceed smaller city regions related to important aspects of inputs and outputs of the open innovation model.

19 18 Table 11. Main summary of findings and policy implications. KB-sector Important Industrial localities Characteristics Important competence for innovation Functional knowledge flow Spatial knowledge flows Open innovation and urban regions Relevant regional policy Analytical Synthetic - Manu Synthetic - Serv Symbolic Medium-sized and small cityregions Medium-sized and small city- Large cityregions Large cityregions - High product-, process- and patent- rates - Low new firm rates - Medium product- and process-rates - Low patentand new firm rates - High productand new firm rates - Medium process-rates - Low patentrates - High new firm rates - Medium process-rates - Low productand patent-rates High R&D share, high share of employees with formal education R&D units. Production, engineering and R&D competence Low R&D share, high share of employees with higher education. Production competence High R&D shares, lower shares with R&D unit and owe shares of employees with higher education Sales, client and marketing competence Low on R&D and formal education Sales, client and marketing competence - Internal of firm, clients, suppliers, competitors. Knowledge organisations and innovation centres - Internal of firm, clients, suppliers, competitors. Knowledge organisations and innovation centres - Internal of firm, clients, suppliers, competitors. Associations and informal meeting points - Internal of firm, clients, suppliers, competitors. Associations and informal meeting points - International, national and intra-regional levels. - National and intra-regional levels - National and intra-regional levels - Intraregional and national level Multi functional and multi spatial knowledge flows, located in spaces with limited knowledge resources Diversified functional and spatial knowledge flows, located in spaces with limited knowledge resources Diversified functional and spatial knowledge flows, located in diversified urban agglomerations with abundant knowledge resources Mono functional and spatial knowledge flows, located in diversified urban agglomerations with abundant knowledge resources -Basic/applied research - Industry-universitylinks - Commercialisation of knowledge - Support global networks and attractiveness in an international context - Upgrading of skills, existing firms and clusters. - Adjusting of knowledge org. to the needs of cluster firms - Support extra-local links. - Stimulate diversity - Support informal meeting points and attractive people climate - Stimulate attractiveness in an international context. - Stimulate diversity - Support informal meeting points and attractive people climate - Stimulate attractiveness in an international context.

20 19 Some policy implications Innovation support policies both should take into account the type of knowledge bases that dominates in different cities. Both the type of Knowledge base and characteristics of the agglomeration have important implications with regard to the role, type and relative importance of clusters and regional innovation systems (RIS), and thus provide arguments for pursuing specific innovation support policies, sensitive to existing as well as desirable regional milieus (Asheim & Coenen 2005). The implication of this is to develop a regional innovation strategy for each of the regional milieus and their dominant knowledge base. In industries and clusters primarily drawing on an analytical knowledge base an important challenge is related to the question of commercialising newly created knowledge as a basis for new economic activity. This may require support for close and systemic industry-university cooperation and interaction in the context of science parks, technical universities, creative knowledge environments etc. These firms clearly source least knowledge locally among the firms in the sample and as such have comparatively few local connections, suggesting that these industries can be in danger of relocating to areas with more dynamic R&D milieus. A possible strategy to meet such a challenge is to develop a RIS in lines of a more science based regional innovation system in order for the cluster to further evolve. As this system is more functionally integrated into national or international innovation systems, and as such a more exogenous development model exists, also national support of basic and applied research will be at the core. In industries and clusters primarily drawing on a synthetic knowledge base, the logic behind promoting a RIS is to support and strengthen localised learning of an existing industrial specialisation. This implies a need for innovation support policies dedicated towards the local industry and hence to make the RIS an extension of the cluster. However, one must also prevent lock-in situations for industries in these knowledge bases. There can be external changes in technologies and markets that need to be taken into consideration, and as such the development of more extra-regional functional linkages in order to identify and make more use of external competence (Cohen and Levinthal 1990) is necesarry. Engineers in synthetic industries tend to follow jobs, not vice versa, making business policy important and partly outrank people policy. Since activities drawing on a symbolic knowledge base is an urban phenomenon the policy challenge is not so much focused on cluster-ris relations as on the people climate of the region. This implies a need for broader urban policies promoting the people climate by being dedicated to the quality of place and to create an environment characterised by diversity and tolerance. 6. Concluding remarks One of the objectives with this analysis has been to test and illuminate the knowledge base concept with the use of survey data from Norway. From the theoretical literature we initially developed a framework for the empirical analysis of different industrial knowledge bases. The empirical analysis validated important differences between industrial sectors belonging to three main types of knowledge bases derived from the theoretical literature. The KB-sectors differs regarding innovation input and output, as well as in the functional and spatial structures of knowledge and innovation linkages. But the analysis also shows a demand for some specification of the concept. First of all is the trefoiled typology of knowledge bases a very rough categorization. In concret firms and industries one will find much more complex

21 20 types of knowledge inputs and innovation patterns, and many combination of the main typologies of knowledge bases. We also find that the industrial sectors related to different main types of knowledge bases had much in common regarding the firm s use of internal experience based knowledge as well as their large dependencies of extensive knowledge inputs from external sources in their innovation activities. Though, an empirical analysis based on a more narrowly delimitation of selected branches may have given a more distinct pattern of knowledge bases and innovation processes, though the representativity would have been much weaker. The KB-typology seems also in particular to be too rough for an apropriate charaterization of the service sector. However, in general it is a need for more comprehensive and comparative analysis across industries, regions and nations, to test and develop the concept of the industrial knowledge bases. In particular it is a need to further development a more differentiated knowledge base concept, and more rigorous criterions for the classification of industrial sectors in this respect. This is important if the concept should be more useful in informing innovation policy in different kinds of regions.

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