Firm Collaboration and Modes of Innovation in Norway. Rune Dahl Fitjar, IRIS Andrés Rodríguez-Pose, LSE

Transcription

1 Firm Collaboration and Modes of Innovation in Norway Rune Dahl Fitjar, IRIS Andrés Rodríguez-Pose, LSE

2 Research questions How does industrial (DUI) and scientific (STI) collaboration affect the innovative capacity of firms? Does it matter whether industrial and scientific partners are located nearby or at a distance? Are there any differences between city regions in Western Norway and other city regions when it comes to the use of industrial and scientific partners? the geographical location of such partners? the effect of industrial and scientific partners on innovation? Are there any differences across sectors? Papers: Rune Dahl Fitjar and Andrés Rodríguez-Pose (2013): Firm Collaboration and Modes of Innovation in Norway. Research Policy 42 (1): Rune Dahl Fitjar and Andrés Rodríguez-Pose (2012): Interaction and Innovation across Different Sectors: Findings from Norwegian City Regions. Paper under review by Regional Studies.

3 Sources of innovation Scientific/technical knowledge Linear model of innovation (Bush 1945, Maclaurin 1953) Knowledge spillovers (Audretsch and Feldman 1996, Sonn and Storper 2008) Key variables: R&D investment, human capital, links to scientific partners Key skills: Know-why, know-what (Jensen et al. 2007) Learning by doing Regional innovation systems (Lundvall 1992, Cooke and Morgan 1998), industrial districts (Becattini 1987), learning regions (Morgan 1997), innovative milieux (Aydalot 1986) Key variables: Informal interaction, social capital, organisations, institutions, markets Key skills: Know-how, know-who (Jensen et al. 2007) STI mode of innovation DUI mode of innovation

4 Two types of interaction in DUI mode Within supply-chain With suppliers and customers Close complementary bonds within supply chain Clear economic purpose, joint aim of improving products Contractual links Externalities from specialisation (Marshall) or related variety (Frenken et al. 2007, Boschma and Iammarino 2009) Outside supply-chain With other firms, such as competitors Transfer of knowledge not the main purpose Unintended knowledge spillovers may happen Externalities from diversification (Jacobs), potential for excessive cognitive distance (Boschma 2005)

5 The geography of STI and DUI STI mode Costly search for knowledge requires purpose-built connections global pipelines (Bathelt et al. 2004) Analytical and codified knowledge travels well (Asheim and Gertler 2005) Geographical distance not necessarily a problem Top research centres often located far away DUI mode Based on shared problems and experiences Tacit knowledge More frequent in industries with synthetic or symbolic knowledge base (Moodysson et al. 2008) Local buzz (Storper and Venables 2004), informal interaction Being there (Gertler 1995) Strong value-added of local cooperation

6 Modes of innovation and knowledge bases Synthetic knowledge Analytical knowledge Interactions In-house Doing, using, learning Science, technology, innovation Characteristics Industries Characteristics Industries Characteristics Industries In-house problem solving Inductive learning processes Interaction between departments within the firm Aeronautics Aerospatial industry Petroleum and energy Based on frequent, often informal, interaction Important role for tacit knowledge Process innovations and incremental product innovations General manufacturing Construction Retail trade Role of R&D constrained to applied research Specialised manufacturing Biotechnology Nanotechnology Creation of in-house R&D labs or departments Large firms Capital intensive sectors Formal interactions between in-house R&D units and research centres Key role for codified knowledge Radical product innovations Formalised interaction Key role for scientific research and models Radical and complex product and process innovation

7 Data Tailor-made survey of firms with more than 10 employees in Norway Targeting the managers of those firms Conducted by telephone In the five largest urban agglomerations in Norway In the spring of 2010 Examining Innovation during the last three years The use of external partners in innovation processes The location of external partners used

8 Innovation in Norwegian city regions Bergen has the lowest share of innovative firms in all categories Product Process Oslo has the highest share of innovative firms in all categories (% yes) Total Radical Total Radical N Oslo 59.6 % 34.0 % 50.4 % 20.4 % 403 Bergen 46.4 % 25.1 % 42.4 % 16.5 % 401 Stavanger 54.0 % 33.8 % 46.8 % 18.8 % 400 Trondheim 52.3 % 29.0 % 48.7 % 19.7 % 300 Stavanger is mostly very close to the average Kristiansand 58.0 % 30.0 % 47.0 % 20.0 % 100 Total 53.4 % 30.5 % 46.9 % 18.8 % 1604

9 Innovation in different sectors Manufacturing There firms are have most the non-innovative highest level of product firms innovations construction Transport and communications has the lowest level of radical innovation % of firms Manufacturing Construction Trade Hotels and restaurants Transport and communication Other services Product innovation Manufacturing also has the No innovation highest share 35.8 of firms However, other services has a higher 49.2 share of 40.7 introducing (2.8) process innovations (2.8) (2.9) (4.4) radical process (4.5) innovation (2.4) Incremental Hotels and 25.6 restaurants innovation (2.4) (2.3) (2.6) have the (3.9) fewest radical (3.9) (2.1) process innovations Radical innovation (2.9) (2.0) (2.9) (3.4) (4.0) (2.3) Process innovation No innovation 37.5 (2.8) 62.4 (3.0) 65.2 (2.9) 62.8 (4.3) 63.7 (4.3) 43.5 (2.4) Incremental innovation 39.2 (2.8) 20.9 (2.5) 24.6 (2.6) 32.6 (4.1) 20.2 (3.6) 29.6 (2.2) Radical innovation 23.3 (2.4) 16.7 (2.3) 10.1 (1.8) 4.7 (1.9) 16.1 (3.3) 26.9 (2.1) N

10 60 50 Percent of companies using partner type Suppliers and customers are the most common partners at all scales Very few firms collaborate with scientific partners abroad Internal Suppliers Customers Competitors Consultants Universities Research inst. Regional National International

11 Percent using partner type, by city region Collaboration with suppliers and customers is equally common in all regions Internal Suppliers Customers Competitors Consultants Universities Research inst. Oslo Bergen Stavanger Trondheim Kristiansand Stavanger has by far the lowest level of collaboration with universities Stavanger and Bergen also collaborate the least with research institutes

12 DUI and STI collaboration across different sectors 90% 80% 70% 60% 50% 40% 30% 20% 10% Trade and manufacturing firms collaborate the least with competitors Construction firms collaborate the least with universities and research institutes, while other service firms collaborate the most 0% Internal Suppliers Customers Competitors Consultancies Universities Research institutes Manufacturing Construction Trade Hotels & rest. Transp. & comm. Other services

13 Innovation and collaboration with partner types Collaboration with universities has a significant effect on product innovation Within congl 0.39** (0.12) Suppliers 0.39** (0.14) Customers 0.36** (0.13) Competitors -0.39*** (0.12) Consultancies 0.15 (0.12) Universities 0.30* (0.16) Research inst 0.26 (0.16) Collaboration with customers has a significant Collaboration effect with on product competitors innovation has a negative effect on product innovation Product New to market Process New to industry 0.20 (0.13) 0.33* (0.16) 0.54*** (0.15) -0.55*** (0.13) 0.18 (0.13) 0.53*** (0.15) 0.20 (0.16) (0.12) 0.76*** (0.14) 0.03 (0.13) (0.12) 0.16 (0.12) 0.21 (0.15) 0.26 (0.16) Logistic regression models, N = Controls: Sector, region, education, age, board memberships, ownership, size Collaboration with suppliers significantly affect all forms of innovation 0.10 (0.15) 0.38* (0.19) (0.17) (0.15) 0.03 (0.15) 0.13 (0.18) 0.79*** (0.18) * p < 0.05, ** p < 0.01, *** p < 0.001

14 Fitted probabilities of innovation Product New to Collaborating market Process with a supplier New or to customer Collaborating increases with the a competitor likelihood industry of reduces product the innovation likelihood by by 9 %- 8 %- No partners points points compared 0.30 to not having 0.10 any partners Within congl Suppliers Customers Competitors Consultancies Universities Research inst

15 Now, let s run the model for firms in Bergen Within congl 0.39 Suppliers 0.29 (0.29) Customers 0.31 (0.30) Competitors -0.43* Consultancies (0.24) Universities 0.60* (0.32) Research inst 0.52 (0.36) Product New to market Process New to industry 0.08 (0.29) 0.57 (0.36) 0.86** (0.38) -0.61** (0.30) (0.28) 0.92*** (0.33) 0.71* (0.37) ** (0.29) 0.03 (0.24) (0.24) 0.25 (0.31) 0.23 (0.35) Logistic regression models, N = 401. Controls: Sector, education, age, board memberships, ownership, size Collaboration with consultancies is consistently negative for firms in Bergen 0.22 (0.34) 1.12*** (0.43) The coefficients 0.09 for universities -0.76* and research (0.29) institutes are twice (0.39) as high for firms in Bergen 0.28 (0.33) -1.12*** (0.34) 0.12 (0.41) 1.15*** (0.42) * p < 0.10, ** p < 0.05, *** p < 0.01

16 And the same model for firms in Stavanger Within congl 0.31 (0.24) Suppliers 0.81*** (0.29) Customers 0.66** (0.27) Competitors -0.83*** Consultancies (0.24) Universities 0.02 (0.34) Research inst 0.38 (0.33) Product New to market Process New to industry (0.31) 0.78*** (0.30) -0.61** (0.26) (0.24) 0.43 (0.32) 0.25 (0.32) (0.24) 0.50* (0.29) 0.51* (0.27) (0.24) 0.05 (0.23) (0.33) 0.36 (0.32) Logistic regression models, N = 400. Controls: Sector, education, age, board memberships, ownership, size The effects of inter-firm collaboration are stronger than in the full model 0.03 (0.30) (0.37) University collaboration has no effect on innovation for firms in Stavanger 0.43 (0.36) (0.31) 0.38 (0.30) (0.39) 1.11*** (0.35) * p < 0.05, ** p < 0.01, *** p < 0.001

17 Do the effects of collaboration differ across sectors? Generalised ordinal regression model Within conglomerate 0.11 Suppliers incremental Manufacturing Construction Trade Hotels & restaurants 0.16 (0.30) Suppliers radical Customers incremental 0.66** (0.30) (0.32) (0.28) 1.04** (0.48) (0.34) (0.67) (0.38) 0.39 (0.28) 0.22 (0.41) Customers radical 0.62 Competitors increm (0.27) (0.34) Competitors radical -1.41*** Consultancies increm (0.36) Consultancies radical 1.30** Universities 0.88*** (0.32) Research institutes 0.16 (0.30) (0.58) 1.26** (0.53) 0.99** (0.47) Collaboration with customers has a positive Collaboration effect in with manufacturing competitors has and a other negative services effect in trade and other services (0.32) (0.44) (0.37) (0.30) (0.44) (0.30) 0.89** (0.37) (0.42) Transport & comms. 0.83* (0.48) 1.52** (0.64) (0.59) (0.82) -0.94* (0.51) (0.46) (0.64) (0.49) 0.54 (0.63) Other services 0.26 (0.20) 0.57** (0.23) 0.94*** (0.26) They also tend STI to partners are particularly have a positive important effect in construction in manufacturing -0.91* (0.51) -0.52*** 0.41 (0.20) (0.55) 2.28*** (0.70) (0.20) (0.57) 0.47 (0.54) (0.74) (0.26) * p < 0.05, ** p < 0.01, *** p < 0.001

18 Geographical dimension of DUI and STI Regional DUI non-supply-chain 28.4 (1.1) Non-regional 19.0 (1.0) DUI supply-chain 67.0 (1.2) 61.5 (1.2) STI 48.0 (1.2) 29.1 (1.1) It is much more common to collaborate with STI partners within the region Firms collaborate almost as frequently with nonregional suppliers and customers as with regional ones

19 80% 70% Percent using partner type, by city region and partner location Firms in Oslo collaborate the least with regional partners of all types Firms in Bergen collaborate the least with The difference between Stavanger and Bergen is both DUI and STI partners outside the region mainly that Stavanger firms collaborate more with non-regional consultancies. For university and research institutes, the numbers for Stavanger and Bergen are almost identical 60% 50% 40% 30% 20% 10% 0% DUI non-supplychain regional More firms in Stavanger collaborate with regional consultancies (43 %) than in any other region, while Stavanger has the lowest level of collaboration with its regional university (13 %) of DUI non-supplychain non-regional regional non-regional DUI supply-chain DUI supply-chain STI regional all regions except Oslo (11 %). In Trondheim, 30 % of firms collaborate with the regional university. Oslo Bergen Stavanger Trondheim Kristiansand STI non-regional

20 In the DUI mode, only In the STI mode, regional The effect of collaborating non-regional What type cooperation of knowledge travels better? cooperation also has a with competitors is has a significant effect on positive effect on product negative both within and innovation innovation outside the region Product New to market Process New to For regional industry collaboration, STItype For has non-regional a stronger effect DUI non-supp *** collaboration, DUI-type regional (0.13) (0.15) (0.13) has a stronger (0.17) effect than DUI non-supp non-regional -0.30* (0.15) (0.16) (0.15) STI-type (0.18) DUI supply-ch regional DUI supply-ch non-regional Scientific regional Scientific nonregional 0.12 (0.12) 0.73*** (0.12) 0.23* (0.12) 0.37** (0.14) 0.17 (0.13) 0.72*** (0.14) 0.40** (0.13) 0.33* (0.14) 0.13 (0.12) 0.50*** (0.12) 0.20 (0.12) 0.33* (0.13) Logistic regression models, N = Controls: Sector, region, education, age, board memberships, ownership, size (0.15) 0.42** (0.16) 0.14 (0.15) 0.35* (0.16) * p < 0.05, ** p < 0.01, *** p < 0.001

21 Fitted probabilities of innovation Product New to Collaborating market Process with a supplier New or to customer outside the This region also holds industry more for than radical doubles the likelihood product of product innovation No partners innovation 0.34 for an average firm 0.09 DUI non-supp regional DUI non-supp non-regional DUI supply-ch regional DUI supply-ch non-regional Scientific regional Scientific nonregional

22 Collaboration with nonregional STI partners has a particularly strong effect on product innovation in Product New to market Bergen Process New to industry How does this work in Bergen? DUI non-supp regional DUI non-supp non-regional DUI supply-ch regional DUI supply-ch non-regional Scientific regional Scientific nonregional (0.27) -0.74** (0.33) 0.24 (0.27) 0.86*** (0.26) *** (0.33) (0.32) (0.36) 0.57* (0.31) 0.99*** (0.31) 0.42 (0.27) 0.96*** (0.33) 0.02 (0.26) (0.31) 0.32 (0.26) 0.48** (0.24) 0.04 (0.30) Logistic regression models, N = 401. Controls: Sector, education, age, board memberships, ownership, size 0.38 (0.35) (0.39) (0.33) 0.63* (0.35) -0.80** (0.32) 0.40 (0.37) * p < 0.10, ** p < 0.05, *** p < 0.01 This is mostly the negative effect of consultancies seen in the previous analysis

23 Collaboration with regional competitors is particularly harmful in Stavanger What about Stavanger? DUI non-supp regional DUI non-supp non-regional DUI supply-ch regional DUI supply-ch non-regional Scientific regional Scientific nonregional Product New to market matter less Process New to industry -0.89*** (0.26) (0.32) 0.21 (0.26) 0.90*** 0.30 (0.24) (0.27) -0.81*** (0.29) (0.32) 0.15 (0.27) 0.90*** (0.27) 0.60** (0.27) (0.26) 0.10 (0.32) 0.45* (0.26) 0.60** 0.07 (0.24) 0.20 (0.27) In Stavanger, regional STI partners tend to have a positive effect, while nonregional STI partners (0.34) (0.38) 0.00 (0.33) 0.61* (0.33) 0.33 (0.30) 0.55* (0.31) * p < 0.10, ** p < 0.05, *** p < 0.01 Logistic regression models, N = 400. Controls: Sector, education, age, board memberships, ownership, size

24 Conclusion External cooperation is an important source of firm innovation Both STI and DUI partnerships matter Firms in Stavanger and Bergen tend to use fewer STI partners than firms in other regions especially universities and research institutes However, local interaction has no significant effect on innovation especially within DUI mode Cooperation with competitors can significantly harm firms innovative ability Close links to regional competitors has a particularly strong negative effect in Stavanger Formal pipeline-type interactions key source of innovation both in the STI and in the DUI mode Fewer firms in Bergen tend to use non-regional partners in both the STI and DUI mode Bergen firms who do use such partners, tend to be much more innovative than their peers Excessive cognitive proximity within small and homogeneous regions may be detrimental to innovation Heterogeneity among agents is important

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