Brandt, A., Hahn, C., Krätke, S., Kiese, M.: Metropolitan Regions in the Knowledge Economy -...

Tijdschrift voor Economische en Sociale Geografie – 2009, Vol. 100, No. 2, pp. 236–249.© 2009 by the Royal Dutch Geographical Society KNAGPublished by Blackwell Publishing Ltd., 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

Blackwell Publishing Ltd

METROPOLITAN REGIONS IN THE KNOWLEDGE ECONOMY: NETWORK ANALYSIS AS A STRATEGIC INFORMATION TOOL

ARNO BRANDT*, CLAUDIA HAHN*, STEFAN KRÄTKE** & MATTHIAS KIESE***

*

NORD/LB Regional Economics, Hannover, Germany. E-mail: [email protected]

**

Department of Economic and Social Geography, European University Viadrina, Frankfurt an der Oder, Germany. E-mails: [email protected]; [email protected]

***

(corresponding author), Department of Geography, Ludwig-Maximilians-Universität, München, Luisenstr. 37, D-80333 Munich, Germany. E-mail: [email protected]

Received: May 2008; revised October 2008

ABSTRACTSince the early 1990s, regional networks have received a lot of academic and political attentionas vehicles for knowledge-based economic development. However, this powerful rhetoric has beenaccompanied by surprisingly little concrete analysis. Economic geography is only recently wakingup to the potential of network analysis for interorganisational linkages within and between regions.We discuss network analysis as a strategic information tool for regional knowledge managementand apply it to the metropolitan region of Hannover-Braunschweig-Göttingen-Wolfsburg in thenorthern German state of Lower Saxony. Producing network diagrams and parameters of networksize, density, centrality, cohesion and connectivity from a large sample of actors and linkages, oursurvey shows striking differences between different fields of competence that highlight thepotential of network analysis as a powerful tool and a necessary basis for decision-making to propelmetropolitan regions into the knowledge economy. We outline both case-specific and genericimplications for the practice of regional knowledge management. However, a few methodologicalshortcomings still call for further research to be conducted.

Key words: Knowledge, networks, network analysis, network management, metropolitan regions,

Lower Saxony

NETWORKS AND METROPOLITAN REGIONS IN THE KNOWLEDGE ECONOMY

Advanced national and regional economiestend to graduate into knowledge economiesdirectly based on the production, distributionand application of knowledge (cf. OECD 1996;Neef 1998; Cooke 2002; see Sokol 2004 for acritique). According to Smith (2002, pp. 7–12),the transition to a knowledge-based economy(KBE) comes in four dimensions: First, know-ledge assumes increasing importance as an

input

vis-à-vis

the conventional factors of pro-duction, both quantitatively and qualitatively.Second, knowledge also features more promi-nently as a product as evident from the above-average growth of high-tech manufacturingindustries and knowledge-intensive businessservices. Third, we observe an increasing codi-fication of knowledge: tacit knowledge (know-how, know-who) is transformed into explicitknowledge (know-what, know-why) at an ever-faster pace, leading to an unprecedented floodof information (cf. Lundvall & Johnson 1994).However, Klapp (1982) pointed out the lack of

METROPOLITAN REGIONS IN THE KNOWLEDGE ECONOMY

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© 2009 by the Royal Dutch Geographical Society KNAG

meaning in information: while the quantity ofavailable information is steadily rising, the shareof meaningful knowledge embodied therein isconstantly falling. Furthermore, there arenatural limits to the codification of tacit know-ledge, and implicit knowledge hence remainsimportant not only as the only source of codi-fied knowledge, but also as a necessary precon-dition for its context-specific application asemphasised by Nonaka and Takeuchi (1995).Fourth, the advance of information and com-munication technologies leads to a dramaticdecline in the costs of acquiring and diffusingknowledge. As another transformation typicalof a KBE we may add the rise of hybrid formsof knowledge that are neither private nor publicin character (cf. Arrow 1994). This hybrid formof knowledge is freely available only to membersof knowledge-based networks, but not to out-siders, thus, resembling a club good as conceivedby Buchanan (1965). Cooke and Morgan (1993)were among the first to acknowledge the adventof a new ‘network paradigm’ in regional econ-omic development. Despite findings such asthe impact of a firm’s network position on itsperformance (e.g. Uzzi 1996; Powell

et al

. 1999),however, recent reviews by Cantner and Graf(2008) and ter Wal and Boschma (2008) showthat the empirical picture on the innovation-related outcomes of networking is still far fromconclusive, both at the firm and regional level.

As we move from industrial to increasinglyknowledge-based forms of economic activities,the traditional division of labour is being trans-formed into a division of knowledge: knowledgethat is distributed across a large number ofindividual and collective actors needs to becombined in novel ways to create commercialvalue (cf. von Hayek 1937, 1945; Smith 2002).This seminal principle is reflected by interactivemodels of innovation (Kline & Rosenberg 1986)and the ensuing systemic view of innovationencapsulated in concepts of national, regionalor metropolitan innovation systems (cf. Capello2001, Fischer

et al.

2001, Revilla Diez 2002,pp. 64–67; and Simmie 2003 for the latter).Metropolitan regions (MRs) are commonlyseen as nodes within the emerging KBE as theyoffer a high density and large variety of actorsas carriers of knowledge, forming a vibrantrepository. Spatial proximity facilitates face-to-face contacts, accelerates the transfer of tacit

knowledge (‘buzz’, cf. Storper & Venables 2004),and reduces uncertainty through building trust.However, recent literature acknowledges thatspatial proximity by itself is an insufficient pre-condition for knowledge transfer – there is nosimple ‘learning by being there’ (Gertler 2003).Collective learning first and foremost requirescognitive and social proximity, which may becorrelated with or facilitated by spatial proximity(cf. Breschi & Lissoni 2003; Boschma 2005).Assuming that space does matter under certainconditions such as radical technological changeand knowledge tacitness, metropolitan regionsmay be seen as spaces of localised learning(cf. Malmberg & Maskell 2006; Brenner 2007;Lorenzen 2007). While metropolitan regionshave always fed on agglomeration economies,the growing importance of knowledge as botha product and a factor of production have intro-duced localised knowledge spillovers as a newdynamic quality of agglomeration economies(cf. Breschi & Lissoni 2001).

Over the past 15 years, the network paradigmhas become firmly established in academia, policyand practice. To succeed in an increasinglyknowledge-based locational competition, aregion needs internal network structures tofully exploit its internal knowledge base whileat the same time absorbing state-of-the-artknowledge from outside the region. However,there is still a striking mismatch between therhetoric and sound empirical analysis ofregional networks, with a lack of original networkanalysis at the meso-scale of the region. Instead,regional networks are usually taken for grantedas soon as there is some kind of industrial and/or technological agglomeration. The researchpresented in this paper applies network analysisto map knowledge-based linkages betweenresearch institutions and innovating firms in themetropolitan region of Hannover-Braunschweig-Göttingen-Wolfsburg (MRHBGW) and to system-atically compare network properties betweendifferent industries and technologies. Beforepresenting our findings, we will introduce networkanalysis as a tool for knowledge-based regionaldevelopment, followed by a brief sketch of ourcase study region and an outline of our method-ology. Our empirical results allow us to derivea number of important policy recommenda-tions that underscore the value of networkanalysis as a tool for regional knowledge

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ARNO BRANDT, CLAUDIA HAHN, STEFAN KRÄTKE & MATTHIAS KIESE


management. However, some methodologicallimitations open up avenues for further research.

NETWORK ANALYSIS AS A TOOL FOR KNOWLEDGE-BASED REGIONAL DEVELOPMENT

For a detailed investigation of knowledgeexchanges between regional actors, network ana-lysis offers a promising methodical approach(cf. Knoke & Kuklinski 1982; Burt & Minor1983; Jansen 2006). First developed in sociologyfor the analysis of interpersonal relationshipswith a comparatively small numbers of actors(cf. Marsden & Lin 1982), it is increasinglyapplied to interorganisational relations in thesocial and behavioural sciences, economics,marketing and industrial engineering. Ingeneral, social network analysis reflects aperspective that concentrates on relationshipsamong social entities and is an important addi-tion to the focus on attributes of the social unitsanalysed by standard social and behaviouralresearch (cf. Wasserman & Faust 1994; Carrington

et al.

2005). Economic geography is only recentlywaking up to the hitherto largely untappedpotential of network analysis for interorganisa-tional linkages within and between regions (cf.ter Wal & Boschma 2008).

For regional science applications, the value ofnetwork analysis lies in its potential to identifyspecific strengths and weaknesses of regionalknowledge network structures. As far as weknow, however, primary network analysis hasonly been applied to the interlinking of regionaleconomic actors in very few cases. One of thesenotable exceptions is Krätke’s analysis of thePotsdam/Babelsberg film industry (Krätke 2002),while Wrobel (2004) basically applied the samemethodological framework to the logistics indus-try of Bremen. In contrast, network analysisin economic geography has been spearheadedby analyses of co-patenting activities whichhighlighted the role of labour mobility forthe establishment of networks of innovators, asillustrated by the work of Fleming and Frenken(2006) for the US or that of Cantner and Graf(2006) covering Jena, Germany. However, fur-ther to the drawbacks of patents as a measureof innovative activity per se (cf. Grupp &Schmoch 1999), this research only measuresa rather small and formalised subset of the

diverse range of interactions that make up in-novation networks. The shortage of originalnetwork analyses based on primary survey datacontrasts sharply with the high level of attentionthat regional networks currently receive fromtheorists and policy-makers alike. To measurethe density and structure of the linkagesbetween scientific institutions and firms andto systematically compare network propertiesbetween different fields of competence (as wellas

vis-à-vis

other regions) therefore remain aparticular challenge for the analysis of regionalknowledge networks.

To start with, the methodical approach of anetwork analysis shall be briefly introduced.As a first step, relational data on the regionalactors’ co-operative linkages need to be gath-ered to identify who is collaborating with whom.The identification of network relations is basedon a survey of relevant actors on the co-operationwith identifiable business partners and institu-tions on the regional, national and interna-tional level. In principle, there are two possiblevariants of ‘data mining’ for network relations:First, the list query asks for the relations of anactor with all other given actors of a network.This promises a complete capture of the regionalactors’ network relations. Since all relevantnetwork partners must be known in advance, listquery is mainly only applicable to rather smallnetworks. Second, the open query method asksrespondents for a given number of their mostimportant partners, for example, the top threeaccording to their own judgement, while thetotal population of network actors remainsunknown. It thus allows for the identification ofactors which could not be listed in advance,such as the multitude of private sector firmswhich might be identified as partners withina regional network. In most cases of opennetwork query, the network relations within aregion cannot be recorded completely becausesome relevant actors will not participate in thesurvey, while others may not be able or willingto reveal all their partners. Therefore, the ana-lysis only covers the observable part of regionalnetwork relations. Open query is mainly em-ployed for the investigation of large networkswith an extensive number of actors, as in theanalysis described in this paper.

Based on the identification of network rela-tions and actors, the second step of the analysis


239


concentrates on a set of network parametersmeasuring the positioning of individual actorsas well as particular structural componentswithin a regional actor network (see Table 1).The analysis identifies not only focal actors, butalso relatively isolated actors with no or onlyweak ties into the network. Parameters of over-all network properties and the positions of indi-vidual actors may be used to compare andcontrast different networks or different fields ofcompetence within or across regions (cf. Krätke2002).

Based on relational data and a variety ofnetwork parameters, the findings of a networkanalysis may serve as a foundation for the designof specific measures to mobilise and improveinterorganisational links and knowledge flowsamong the regional actors, and hence improvethe circulation of knowledge within the region’sdistributed knowledge base (cf. Smith 2002).Nevertheless, network analysis also has distinctdrawbacks and limitations. Mapping a region’snetwork structures in a reliable manner requiresa high response rate, namely, a near-completecoverage of the relevant actors.

1

Even if this isachieved, however, a respondent’s knowledgeabout the transaction and communication link-ages of her/his organisation still introduces aconsiderable source of error that should alwaysbe remembered when interpreting fractionaldifferences in network parameters (cf. ter Wal& Boschma 2008, p. 10). Likewise, researchersdepend on respondents’ judgements when itcomes to the intensity of co-operation. From apractical point of view, however, the main rea-son for not carrying out network analyses on a

regional scale is not methodological flaws butthe high costs – this is why collective learningis commonly inferred from the spatial concen-tration of actors alone. However, any attemptto economise by not carrying out a proper net-work analysis from the start might in the endbackfire and incur much higher long-term coststhrough an unspecific and inefficient networkmanagement that addresses the wrong actors.

CASE STUDY REGION AND RESEARCH DESIGN

The metropolitan region of Hannover-Braunschweig-Göttingen-Wolfsburg –

With 3.9million inhabitants spread throughout 18,600square kilometres, the metropolitan region ofHannover-Braunschweig-Göttingen-Wolfsburg(MRHBGW) is about a quarter of the size ofthe Netherlands in terms of population, but45 per cent in terms of area (see Figure 1).Comprising 16 counties and three urban dis-tricts, the MRHBGW was officially design-ated as one of 11 European metropolitanregions (EMR) by the German MinisterialConference for Spatial Planning in April 2005.Among the German EMRs, the MRHBGWoccupies a medium rank in terms of size, econ-omic performance and knowledge potentialas assessed in terms of the share of R&D staffin manufacturing, the share of engineers andscientists in total employment, and start-upactivities in high-tech manufacturing normalisedagainst the national average. Hence, the regionhas some significant knowledge potential, butdoes not rival Germany’s leading high-tech

Table 1. Definition of network parameters.

Parameter Definition

Size Number of actorsDensity Number of linkages weighted by an ordinal measure of intensity, divided by

the number of actors designated as active network nodesCohesion Number of isolated actors and network components (the higher the number,

the lower the network cohesion)Centralisation Density of the five most central actors in each field of competenceConnectivity Number of a specific subset of co-operation partners (e.g. firms, international

actors), divided by total number of partnersInterregionalconnectivity (national)

Share of actors with above-average linkages to other German regions

International connectivity Share of actors with above-average international linkages

240



regions in the South (see Table 2). Rather, itbelongs to the large category of ‘grey mass’regions often neglected in regional studies.

With its 16 universities and universities ofapplied sciences comprising some 433 facultiesand institutes in fields widely regarded as econ-omically relevant, the MRHBGW is sometimesreferred to as the ‘research triangle’ within thestate of Lower Saxony. This status is further rein-forced by 52 non-university research institutes,including renowned institutions of the MaxPlanck and the Fraunhofer Society, as well assome large industrial R&D facilities, mostnotably that belonging to VW at the company’sWolfsburg headquarters. However, previousresearch such as the European regional inno-vation survey (ERIS) covering 11 sub-nationalEuropean regions in the late 1990s indicatesthat the weakness of intraregional innovationlinkages might obstruct the translation of thisexcellent research infrastructure into regionaldevelopment outcomes (cf. Backhaus 2000;Sternberg 2000). Drawing on the region’s well-

developed knowledge base, some cities andcounties within the MR teamed up with privatepartners to devise and pursue cluster-based econ-omic development strategies (cf. Kiese 2008).In its search for content, the fledgling MRH-BGW commissioned a study of innovationpotential and knowledge-based networking tostrengthen the region’s competitive position.

Research design –

To evaluate the state ofknowledge-based networking within the MRH-BGW, we applied network analysis to survey datafrom all economically relevant university andnon-university research institutions andinnovation-oriented firms. Covering researchinstitutes only, the first phase of our survey wasconducted between January and June 2006.Across the MRHGB, 496 such units were iden-tified, 403 of which responded to the survey,yielding a respectable response rate of 81 percent. In total, these research institutes disclosed363 regional firms as their co-operation partners,which formed the basis of a regional database

Figure 1. The metropolitan region of Hannover-Braunschweig-Göttingen-Wolfsburg.

MET

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REG

ION

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WL

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E ECO

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Table 2.

Germany’s metropolitan regions, ranked by gross value added.

Metropolitan region Populationmillion

2006

Areakm

2

2006

Densitypop/km

2

2006

GVA

a

bn2005

GVA/labourforce

a

2005

Unemployment2007

Mfg R&Dstaff

Eng’ers &scientists

High-techstart-ups

Share GER

=

100

2005 2002–05

Munich 5.1 21,500 236 171 62,200 7.2%

d

13.7% 5.8% 143Frankfurt/Rhine-Main 5.5 14,800 374 168 60,500 10.2% 6.6% 3.4% 105Stuttgart 5.3

e

15,400

e

343

e

164 61,200 n. a. 8.5% 5.2% 90Hamburg 4.3 19,800 216 124 61,300

d

10.6%

c

6.7% 2.8% 81Berlin-Brandenburg 6.0 30,370 196 118

d

45,600

d

17.1% 6.0% 2.7% 83Rhine-Ruhr 5.2

e

4,435

e

1,183

e

116 51,448 14.2%

c

5.0% 2.7% 87Nuremberg 3.5

c

20,544

c

171 c 103 57,775 6.3% 6.1% 3.4% 104Hannover-Braunschweig-Göttingen-Wolfsburg 3.9

e

18,600

e

210 e 93 50,811 9.1% 5.7% 3.3% 89Saxon Triangle 3.5

b

12,100

b 290 b 64

a

39,000

a

19.8%

c

5.4% 3.0% 84Rhine-Neckar 2.4 5,637 419 62

d

56,700

d

8.0%

d

6.5% 3.4% 137Bremen-Oldenburg 2.4 11,600

b

205

b

55

b

50,000

a

13.7%

c

5.0% 2.4% 94

a

2003,

b

2004,

c

2005,

d

2006,

e

2007.

Source

:

Initiativkreis Europäische Metropolregionen in Deutschland

(

<

www.deutsche-metropolregionen.org

>

, last accessed 9 April, 2008); NORD/LB calculationsbased on University of Dortmund and ZEW.

242



of innovating firms that was further informedby a survey asking economic developmentagencies to identify the 10 most prominentinnovating firms within their respective sub-regions. After consolidation, the databasecomprised 770 firms, which were subsequentlyreduced to 670 following feedback from firmsindicating they would not classify themselves asinnovative. Surveyed between October 2006and May 2007, 363 usable replies yielded aresponse rate of 61 per cent, which is verysatisfactory by corporate survey standards. Sucha high response rate is a necessary preconditionfor inferring from partial sample results toentire regional network structures in each fieldof competence. Joining both phases, our surveyidentified 1,138 actors including research insti-tutes and innovative firms, as well as chambers,associations, foundations, economic develop-ment agencies and network initiatives that wereidentified as co-operation partners during thefirst stage of our survey.

For our survey, we distinguish three forms ofco-operation, which are sorted by intensity indescending order:

1. Long-term strategic co-operation designedto improve the actors’ competitiveness throughintensive exchange of knowledge over severalyears (e.g. R&D partnerships);

2. Short-term co-operation for specific tasks:temporary but nevertheless intensive exchangeof knowledge to solve specific problemsbased on trust and embedded in long-termrelationships; and

3. Co-operation on HR issues (qualification,vocational training) including the generationof tacit knowledge through apprenticeships,internships or training programmes (learn-ing by doing).

Following the first survey stage, actors wereclassified into nine fields of competence, whileour findings reported here will focus on sevenfields with direct economic applicability. Theseare life sciences (medical research and technology,pharmaceuticals, biotechnology), informationand communication technology (ICT) includ-ing media, power generation and environmentaltechnologies, agriculture and forestry, planningand construction, mobility (manufacturing ofautomobiles, railways, air and spacecraft) aswell as manufacturing technologies (materials

science, process technology, mechatronics,

2

microsystems, measurement and optical tech-nologies). Two predominantly academic fieldsof competence have also been surveyed but willnot be reported here, namely, basic research innatural sciences (physics, mathematics, chemis-try, biology and earth sciences) as well as econ-omics, law and social sciences.

EMPIRICAL FINDINGS

Cross-sectional findings –

The survey identifieda total of 2,183 co-operation linkages withinthe MRHGB, the majority of which (58%) wereclassified as long-term strategic co-operation.Around 35 per cent were characterised as short-term co-operation for specific tasks, while onlyseven per cent focused on HR development.Summarising network parameters, Table 3 dis-plays characteristic differences in the networkproperties of the seven applied fields of com-petence. Across the board, networks are fairlyhomogenous in size, the only exception beingthe cross-sectional field of manufacturingtechnologies, which comprises a much largernumber of actors. Similarly, network densitydoes not vary too much across the fields. How-ever, network relations tend to be denser thanthe average in the related fields of mobility andmanufacturing technologies. Relative differencesbetween the fields increase slightly when itcomes to measures of network cohesion andcentralisation. As for network density, cohesionis lowest in life science and ICT, while the inter-linked network structures of mobility and manu-facturing technologies stand out as the mostcentralised, reflecting the dominance of VWand some large suppliers within the regionaleconomy.

Looking at connectivity in general, namely,without reference to spatial scales and across allfields of competence, it is worth noting signifi-cant differences according to type of collabora-tion. With a weighted average of 51.4 per cent,connectivity is much higher from research insti-tutes to firms than the other way around at 30.8per cent. At a mere 12.9 per cent, connectivityis much lower within the business sector alone.With some minor variations, this pattern isstable across most of the seven fields of com-petence. For instance, business-to-businessconnectivity is particularly poor in agriculture


243


and forestry, but much higher in the field ofmobility with its multi-tiered supplier system.

Introducing the spatial dimension, we distin-guish the (intra)regional, the national and theinternational scale. On the whole, linkages toinnovating firms appear to be more stronglylocalised than those with research institutes.Across all scales, the mobility field is morestrongly connected to external partners thanthe average. Connectivity outside the region isalso above average in agriculture and forestry,as well as in the field of life sciences. The latteralso displays above-average connectivity toresearch institutes within the MRHBGW, butmarkedly less to regional firms. This reflects amismatch between a well-developed researchinfrastructure on the one hand and a relativedearth of innovative life sciences firms in theregion. International connectivity may be seenas a proxy for access to state-of-the-art know-ledge worldwide, or the external dimension incluster terms (‘pipelines’, cf. Bathelt

et al.

2004). In this respect, the seven fields of com-petence do vary substantially. As may be expectedfrom a young and science-based field, lifesciences are most strongly connected inter-nationally, closely followed by the smallest field

of agriculture and forestry, which appears lessobvious at first sight but may be explained byits strong research focus. On the other hand,the regional ICT and manufacturing technologynetworks display comparatively few internationallinkages, the former hardly networking beyondthe regional scale.

Based on this overview, we proceed by select-ing two fields of competence for a comparativeevaluation. We contrast the relatively dense,coherent and centralised field of mobility withthat of ICT, which is comparable in size, butmuch less dense, coherent and centralised.

Case study I: Mobility industry –

Includingautomotive, railway, space and aircraft manufac-turers as well as suppliers, service providersand research institutes, the field of mobilityrepresents the region’s traditional industrialstrength. Within our sample, it is a medium-sized network with 192 actors, of which almostone third may be assigned to the automotiveindustry. The network is dominated by onelarge and highly connected component com-prising eight actors who are integrated parti-cularly strongly into regional knowledge flows,one of them being VW as

the

focal actor. Besides

Table 3. Network parameters across the seven fields of competence.

Network parameter Fields of competence

1 2 3 4 5 6 7

Size (no. of actors) 167 209 180 122 164 192 367Density 16.8 14.5 18.4 15.7 16.8 26.0 22.5Cohesion 23 29 20 19 13 12 22Centralisation 289 282 265 268 225 452 555Connectivity- S to B 47.3% 56.2% 49.8% 39.5% 46.9% 56.8% 54.4%- B to S 43.0% 24.2% 29.1% 43.3% 28.3% 20.6% 32.2%- B to B 9.5% 15.9% 15.0% 5.0% 12.5% 19.5% 11.1%Regional connectivity- S to B 17.5% 40.7% 33.6% 24.5% 36.0% 37.7% 44.3%- B to S 38.5% 24.8% 24.2% 30.8% 23.9% 32.2% 24.0%- all to B 36.5% 56.9% 52.2% 40.2% 47.0% 52.1% 59.4%- all to S 59.3% 36.8% 42.2% 53.3% 40.9% 42.2% 35.1%Interregional connectivity (national) 39.8% 25.2% 28.9% 34.3% 32.4% 46.0% 33.7%International connectivity 43.9% 20.3% 27.8% 42.9% 24.3% 33.3% 20.0%

Notes: Fields of competence: 1 = Life Sciences; 2 = ICT; 3 = Power generation and environmental technologies;4 = Agriculture and forestry; 5 = Planning and construction; 6 = Mobility; 7 = Manufacturing technologies.B = Business sector (innovating firms). S = Science sector (research institutes).

244 ARNO BRANDT, CLAUDIA HAHN, STEFAN KRÄTKE & MATTHIAS KIESE


this central complex, there are only a few iso-lated smaller components and unconnectedindividual actors. As a consequence, the field ofmobility shows the highest density of all coveredfields, and comes second in centralisation onlyto manufacturing technologies. These measuresindicate favourable conditions for intraregionalknowledge flows, especially since more thanhalf of the linkages displayed in Figure 2represent long-term strategic co-operation asthe most intense form of linkage. However, anexcessive degree of centralisation and stabilityof ‘strong ties’ may just as well be seen as a lackof flexibility and a risk of lock-ins (cf. Grabher1993). This may be counteracted by a continuousrenewal of the regional stock of firms throughstart-ups, and a sufficient measure of opennessvis-à-vis sources of knowledge external to theregion.

The mobility industry displays the strongestlinkages between research institutes and firms,and business-to-business collaborations also makeup for a comparatively large share of linkages.However, their focus on large firms not just inthe region but nationwide means that linkagesbetween research institutes are not as well deve-loped as in the other fields of competence.Compared to the other fields of competence,the region’s mobility industry is strongly linkedto national partners outside the MRHBGW.This can mainly be attributed to innovativefirms, while the regional research institutionshave fewer interregional or even internationallinkages than in any other field.

All in all, networking is most intense in themobility industry, with a particular emphasis onscience-to-business relations. This field is moststrongly integrated into national networks and

Figure 2. Network diagram of the mobility field of competence in the metropolitan region of Hannover-Braunschweig-Göttingen-Wolfsburg.

METROPOLITAN REGIONS IN THE KNOWLEDGE ECONOMY 245


dominated by the automotive industry, reflect-ing the dominant position of VW and its asso-ciated R&D activities.

Case study II: ICT industry – With 209 actors,the field of ICT (information and communica-tion technology including media) is only a frac-tion larger than the mobility industry’s network,but it displays a completely different networkstructure. The ICT industry has more isolatedactors than any other field in our survey, andthis fragmented structure explains why thisnetwork is also the least dense. Nevertheless,certain research institutes and innovative firmsare fairly well integrated into regional know-ledge flows. While collaboration between regionalresearch institutes appears weak, linkagesbetween science and industry are above averageand more intense than the other way around.As a reasonable explanation, we suggest that

these research co-operations tend to focus onapplied research. Similarly, the intensity ofbusiness-to-business co-operations also exceedsthe average.

Compared to other fields of competence,network linkages are predominantly local in theICT industry. Only 35 per cent of all surveyedactors in this field reported linkages extendingbeyond the MRHBGW. This especially appliesto firms, while research institutes reportedinterregional linkages more frequently. As is thecase overall, long-term strategic partnershipsare still the most common form of linkage inthe ICT field, but its share of short-term specificcollaborations is higher than in any other field(see Figure 3).

In summary, ICT contrasts sharply with themobility field of competence in its low networkdensity and strong intraregional focus. Thefragmented nature of the regional ICT network

Figure 3. Network diagram of the ICT field of competence in the metropolitan region of Hannover-Braunschweig-Göttingen-Wolfsburg.



with twice as many isolated actors than in anyother field may be both the cause and effect ofa rather heterogeneous knowledge base. In anycase, it poses a serious obstacle to intraregionalknowledge circulation and a challenge to anyattempt at regional knowledge management.

IMPLICATIONS FOR POLICY AND RESEARCH

Being the subject of the first network analysisof this size using primary survey data on co-operation between research institutions andinnovating firms across all fields of competencein an entire metropolitan region gives theMRHGB an informational edge for the designand implementation of a network-based regionalknowledge management strategy. As our mostbasic finding, the variety of network propertiesacross the fields of competence directly trans-lates into the advice to account for these differ-ences in the design and implementation of aknowledge-based regional networking strategy.We suggest that such a strategy focus on thefields of mobility, manufacturing technologies,life sciences, ICT as well as power generationand environmental technologies.

The analysis has identified significant nodesof competence in the mobility industry and therelated field of manufacturing technologies. Thesefocal actors are embedded in dense co-operativerelationships and may serve as motors andmultipliers of a regional networking strategy.Conversely, such a strategy might also con-centrate on integrating peripheral actors, andon overcoming institutional barriers to intrare-gional co-operation. This is most urgentlyneeded in the automotive field, where morethan 55 per cent of all actors are concentratedin the Braunschweig sub-region and another30 per cent in the neighbouring Hannover sub-region. Despite this intraregional concentration,more than half of all identified co-operationlinkages are within Braunschweig, but only 17per cent between the two sub-regions. Further-more, the city of Wolfsburg and the sub-regionsof Braunschweig and Hannover pursue inde-pendent, overlapping and competitive strategiesof cluster promotion (cf. Kiese 2008). Takingthe findings from our network analysis seriously,the metropolitan region should use the mobilityfield as a model case for a flexible geometry of

cluster promotion that is aligned to the actualgeography of networks and overcomes the cur-rent underbinding of cluster policy.

In contrast to the region’s strongholds inmobility and manufacturing technology, ournetwork analysis highlights the potential of thestill underdeveloped network structures in lifesciences, ICT as well as power generation and envi-ronmental technologies for further development.However, we would not advocate the pursuit ofa regional network strategy for planning andconstruction owing to its predominantly localoutreach. Conversely, the network pattern ofresearch into agriculture and forestry is too inter-national for an intraregional knowledge man-agement strategy to make any sense. Albeitfor very different reasons, attempts to intensifyintraregional knowledge circulation throughnetwork policies are not bound to produce anysignificant effects in these two fields.

Further to the necessity to differentiate betweenfields of competence, our network analysisidentified a number of further challenges thatregional network management needs to address:

• Network management could make an attemptat bridging gaps to increase the circulationof knowledge. This may be done by a system-atic promotion of co-operation, the targetedattraction of mobile firms, and the likewisepromotion of start-ups. However, such a highdegree of selectivity looks like being a verydemanding endeavour in the practice ofregional economic development.

• The analysis reveals hitherto isolated actorsand network fragments that may be integra-ted for the better use of existing knowledgeand new opportunities for collective learning.However, there may be reasons for isolationand fragmentation that may not be remov-able by any means. More detailed qualitativeresearch is needed to clearly identify theunderlying obstacles. Nevertheless, in thecase of our study region, it seems safe toadvocate a closer integration of partial net-works across the different sub-regions.

• Network analysis clearly identifies the centralor focal actors within regional networks.These actors can assume a strategic role fornetwork management not only as importantcarriers of knowledge, but by using theircentral position to disseminate knowledge



throughout the network. However, strategicactors should not exclusively be sought inthe centre of networks, but also at the inter-faces between established fields of compet-ence, where previously isolated bodies ofknowledge within the bands of relatedvariety (cf. Frenken et al. 2007) may be com-bined for new breakthrough innovations.Another set of actors that warrants specialattention are SMEs that are to a lesser extentintegrated into regional networks for resourceconstraints, but may for the same reason gainmost from improved linkages.

• It goes without saying that a regional net-working strategy must strengthen inter-regional and international linkages whereneeded to absorb interregional knowledgeand to prevent lock-ins.

• A knowledge-based networks strategy for theMRHBGW needs to be institutionalised by arelay centre that co-ordinates the existingsub-regional cluster and economic develop-ment agencies for those fields of competencethat display a clear metropolitan outreach inour network analysis, developing the MRinto a new co-operation space. Such a relaycentre would have to balance subsidiarityand intraregional competition vis-à-vis inter-regional competitiveness.

Perhaps the main practical drawback of net-work analysis is that it is a rather expensive infor-mation tool. Political vision and long-termleadership is thus needed to commit resourcesnot only to network analysis, but especially tothe ensuing strategy development and regionalnetwork management. In our case study region,it still remains to be seen if our rich informa-tional foundation will translate into a properknowledge management strategy. As a final wordof caution, occupation with the co-operative‘network paradigm’ must not blind us to over-look competition as an important alternativesource of knowledge spillovers (Porter external-ities, cf. Glaeser et al. 1992, pp. 1127 ff.).

Our exercise highlights the potential of net-work analysis as a strategic information tool forregional knowledge management. This method-ology offers more detailed insights into regionalnetwork structures than the conventional ana-lysis of regional innovative potential as applied,for instance, by the European Regional Innova-

tion Survey (ERIS) in the late 1990s (cf. Stern-berg 2000). While ERIS only measured ordinalintensity of co-operation by scale and type ofpartner, our network analysis goes one criticalstep further in disclosing co-operative linkagesand measuring network parameters. However,network analysis also has its drawbacks and limit-ations as it hinges on the individual respondent’slimited information and subjective judgement.It can only approximate the existence andintensity of actual knowledge flows; more in-depth case-study research is needed to assessthe magnitude and impact of the phenomenon,but also to explain the reasons underlying thedifferent network properties between thevarious fields of competence.3 Furthermore, awell-founded approach to regional knowledgemanagement would need more insights intoobstacles to co-operation. Network deficienciesuncovered in our network analysis do notnecessarily mean that their removal might leadto improved knowledge circulation; they mayequally be a consequence of an excessivelyheterogeneous knowledge base that might callfor a finer delineation of fields of competence.After all, our empirical exercise can only providea snapshot of network relationships, whilesecondary network analysis of patent data hasalready provided some insight into the evolutionof narrowly defined networks of innovators. AsGlückler (2007, p. 631) points out, networks arenot ends in themselves, but a means of facilitat-ing innovation and growth. This critical trans-mission between means and ends thus remainsyet another unresolved challenge that is likelyto keep scholars busy for some time to come.

Acknowledgements

Funding from the metropolitan region of Hannover-Braunschweig-Göttingen-Wolfsburg is greatfullyacknowledged. The authors would like to thank Hans-Martin Zademach and three anonymous reviewersfor helpful comments on an earlier draft of thispaper. The usual disclaimer applies.

Notes

1. Some non-response is commonly considered toler-able since each link should ideally be mentionedby both of the partners involved (reciprocation,cf. ter Wal & Boschma 2008, p. 9).



2. As a portmanteau of mechanics and electronics,the term mechatronics denotes the fusion of con-vergent technologies in mechanical, electronicand computer engineering.

3. The patent-based network analysis by Flemingand Frenken (2006) may serve as a good examplefor the bridging of quantitative and qualitativemethods in regional network analysis.

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