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Assortativity and Hierarchy in Localized R&D Collaboration Networks

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The Geography of Networks and R&D Collaborations

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

One of the challenges of innovative clusters relies on their ability to overlap technological domains in order to maintain their growth path along the cycle of technological markets. The paper studies two particular structural properties of collaboration networks that provide new insights for understanding this overlapping process. On the one hand, the degree distribution of knowledge networks captures the level of hierarchy within networks. It gives a first measure of the ability of networked organisations to coordinate their actions. On the other hand, the degree correlation captures the level of assortativity of networks. It gives a measure of the ability of knowledge to flow between highly and poorly connected organisations. We propose to combine these simple statistical measures of network structuring in order to study the parameters window that allow localized knowledge networks combining technological lock-in with regional lock-out.

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Notes

  1. 1.

    Another traditional representation consists in mapping degree distribution using frequencies of degree values.

  2. 2.

    Then we only focus on the structuring of the network. Entries are considered as exogenous, or occurring in previous periods.

  3. 3.

    If two nodes have the same degree, we arbitrarily rank them as long as it has no incidence on the slope on the power law.

  4. 4.

    In such a way that the density remains the same for the three networks 2t/n(n − 1) = 0.1212, where t is the number of actual links and n the number of nodes).

References

  • Ahuja G, Polodiro F, Mitchell W (2009) Structural homophily or social asymmetry? The formation of alliances by poorly embedded firms. Strateg Manag J 30:941–958

    Article  Google Scholar 

  • Ahuja G, Soda G, Zaheer A (2012) The genesis and dynamics of organisational networks. Organ Sci 23(2):434–448

    Article  Google Scholar 

  • Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47–97

    Article  Google Scholar 

  • Almeida P, Kogut B (1997) The exploration of technological diversity and geographic localization in innovation: start-up firms in the semiconductor industry. Small Bus Econ 9(1):21–31

    Article  Google Scholar 

  • Antonelli C (2006) The business governance of localized knowledge: an information economics approach for the economics of knowledge. Industry Innov 13(3):227–261

    Article  Google Scholar 

  • Appold S (2005) The location patterns of U.S. Industrial research: mimetic isomorphism, and the emergence of geographic charisma. Reg Stud 31(1):17–39

    Article  Google Scholar 

  • Arthur WB (1989) Competing technologies, increasing returns and lock-in by historical events. Econ J 99:116–131

    Article  Google Scholar 

  • Asheim BT, Boschma R, Cooke P (2011) Constructing regional advantage: platform policies based on related variety and differentiated knowledge bases. Reg Stud 45(7):893–904

    Article  Google Scholar 

  • Audretsch D, Falck O, Feldman M, Heblich S (2008) The life cycle of regions, CEPR Discussion paper DP6757

    Google Scholar 

  • Autant-Bernard C, Billand P, Frachisse D, Massard N (2007) Social distance versus spatial distance in R&D cooperation: empirical evidence from European collaboration choices in micro and nanotechnologies. Pap Reg Sci 86(3):495–520

    Article  Google Scholar 

  • Balland PA, Suire R, Vicente J (2013) Structural and geographical patterns of knowledge networks in emerging technological standards: evidence from the European GNSS industry. Econ Innov New Tech 22(1):47–72

    Article  Google Scholar 

  • Baum JAC, McEvily B, Rowley TJ (2012) Better with Age? Tie longevity and the performance implications of bridging and closure. Organ Sci 23(2):529–546

    Article  Google Scholar 

  • Borgatti SP, Everett MG (1999) Models of core/periphery structures. Soc Networks 21:375–395

    Article  Google Scholar 

  • Boschma RA (2005) Proximity and innovation: a critical assessment. Reg Stud 39(1):61–74

    Article  Google Scholar 

  • Boschma R, Fornahl D (2012) Cluster evolution and a roadmap for future research. Reg Stud 45(1):1295–1298

    Google Scholar 

  • Breschi S, Lissoni F (2001) Knowledge spillovers and local innovation systems: a critical survey. Ind Corp Chang 10(4):975–1005

    Article  Google Scholar 

  • Burt RS (2005) Brokerage and closure. Oxford University Press, New York

    Google Scholar 

  • Cattani G, Ferriani S (2008) A core/periphery perspective on individual creative performance: social networks and cinematic achievements in the Hollywood film industry. Organ Sci 19(6):824–844

    Article  Google Scholar 

  • Cho R, Hassink R (2009) Limits to locking-out through restructuring: the textile industry in Daegu, South Korea. Reg Stud 43(9):1183–1198

    Article  Google Scholar 

  • Cohen WM, Klepper S (1992) The tradeoff between firm size and diversity in the pursuit of technological progress. Small Bus Econ 4(1):1–14

    Google Scholar 

  • Coleman JS (1988) Social capital in the creation of human capital. Am J Sociol 94:95–120

    Article  Google Scholar 

  • Cooke P (2005) Rational drug design, the knowledge value chain and bioscience megacentres. Camb J Econ 29(3):325–341

    Article  Google Scholar 

  • Crespo J (2011) How emergence conditions of technological clusters affect their viability? Theoretical perspectives on cluster lifecycles. Eur Plan Stud 19(12):2025–2046

    Article  Google Scholar 

  • Eisingerich AB, Bell SJ, Tracey P (2010) How can clusters sustain performance? The role of network strength, network openness, and environmental uncertainty. Res Policy 39:239–253

    Article  Google Scholar 

  • Farrell J, Saloner G (1985) Standardization, compatibility, and innovation. RAND J Econ 16(1):70–83

    Article  Google Scholar 

  • Frenken K (2006) Technological innovation and complexity theory. Econ Innov New Tech 15(2):137–155

    Article  Google Scholar 

  • Grabher G, Stark P (1997) Organizing diversity: evolutionary theory, network analysis, and post-socialism. Reg Stud 31(5):533–544

    Article  Google Scholar 

  • Iammarino S, McCann P (2006) The structure and evolution of industrial clusters: transactions, technology and knowledge spillovers. Res Policy 35(7):1018–1036

    Article  Google Scholar 

  • Klepper S (2010) The origin and growth of industry clusters: the making of silicon valley and Detroit. J Urban Econ 67:15–32

    Article  Google Scholar 

  • Klepper S, Simons KK (1997) Technological extinctions of industrial firms: an inquiry into their nature and causes. Ind Corp Chang 6(2):379–460

    Article  Google Scholar 

  • König MD, Tessone CJ, Zenou Y (2010) From assortative to dissortative networks: the role of capacity constraints. Adv Complex Syst 13(4):483–499

    Article  Google Scholar 

  • Markusen A (1996) Sticky places in slippery space: a typology of industrial districts. Econ Geogr 72:293–313

    Article  Google Scholar 

  • Menzel MP, Fornahl D (2010) Cluster life cycles-dimensions and rationales of cluster evolution. Ind Corp Chang 19(1):205–238

    Article  Google Scholar 

  • Moore G (1991) Crossing the chasm: marketing and selling high-tech products to mainstream customers. HarperBusiness, New York

    Google Scholar 

  • Newman MEJ (2003) Mixing patterns in networks. Phys Rev E 67:026126

    Article  Google Scholar 

  • Owen-Smith J, Powell WW (2004) Knowledge networks as channels and conduits: the effects of spillovers in the Boston biotechnology community. Organ Sci 15(1):5–21

    Article  Google Scholar 

  • Rivera M, Soderstrom S, Uzzi B (2010) Dynamics of dyads in social networks: assortative, relational, and proximity mechanisms. Annu Rev Sociol 36:91–115

    Article  Google Scholar 

  • Romanelli E, Khessina O (2005) Regional industrial identity: cluster configurations and economic development. Organ Sci 16(4):344–358

    Article  Google Scholar 

  • Saxenian AL (1990) Regional networks and the resurgence of silicon valley. Calif Manage Rev 33(10):89–112

    Google Scholar 

  • Scherngell T, Barber M (2011) Distinct spatial characteristics of industrial and public research collaborations: evidence from the 5th EU framework programme. Ann Reg Sci 46:247–266

    Article  Google Scholar 

  • Schilling MA, Phelps CC (2007) Interfirm collaboration networks: the impact of large-scale network structure on firm innovation. Manag Sci 53(7):1113–1126

    Article  Google Scholar 

  • Simmie J, Martin R (2010) Regional economic resilience: towards an evolutionary approach. Camb J Reg Econ Soc 3(1):29–51

    Google Scholar 

  • Suire R, Vicente J (2009) Why do some places succeed when others decline? A social interaction model of cluster viability. J Econ Geogr 9(3):381–404

    Article  Google Scholar 

  • Suire R, Vicente J (2013) Clusters for life or life cycles of clusters: In Search for the critical factors of clusters resilience, entrepreneurship and regional development (forthcoming)

    Google Scholar 

  • Todtling F, Trippl M (2004) Like phoenix from the ashes? The renewal of clusters in old industrial areas. Urban Stud 41:1175–1195

    Article  Google Scholar 

  • Vicente J, Balland PA, Brossard O (2011) Getting into networks and clusters: evidence from the Midi-Pyrenean GNSS collaboration network. Reg Stud 45(8):1059–1078

    Article  Google Scholar 

  • Watts DJ (2004) The “new” science of networks. Annu Rev Sociol 30:243–270

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Political Science Institute, University of Toulouse, Toulouse, France

    Joan Crespo

  2. University of Rennes 1, CREM-CNRS, Rennes, France

    Raphaël Suire

  3. Toulouse Business School, and Political Science Institute, University of Toulouse, Toulouse, France

    Jérôme Vicente

Authors
  1. Joan Crespo
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  2. Raphaël Suire
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  3. Jérôme Vicente
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Corresponding author

Correspondence to Joan Crespo .

Editor information

Editors and Affiliations

  1. Innovation Systems Department, AIT Austrian Institute of Technology, Wien, Austria

    Thomas Scherngell

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Crespo, J., Suire, R., Vicente, J. (2013). Assortativity and Hierarchy in Localized R&D Collaboration Networks. In: Scherngell, T. (eds) The Geography of Networks and R&D Collaborations. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-02699-2_7

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