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The Networked Nature of R&D in a Spatial Context Read chapter Read first chapter

2013 | OriginalPaper | Chapter

4. Determinants of Cross-Regional R&D Collaboration Networks: An Application of Exponential Random Graph Models

Authors : Tom Broekel, Matté Hartog

Published in: The Geography of Networks and R&D Collaborations

Publisher: Springer International Publishing

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Abstract

This study investigates the usefulness of exponential random graph models (ERGM) to analyze the determinants of cross-regional R&D collaboration networks. Using spatial interaction models, most research on R&D collaboration between regions is constrained to focus on determinants at the node level (e.g. R&D activity of a region) and dyad level (e.g. geographical distance between regions). ERGMs represent a new set of network analysis techniques that has been developed in recent years in mathematical sociology. In contrast to spatial interaction models, ERGMs additionally allow considering determinants at the structural network level while still only requiring cross-sectional network data.
The usefulness of ERGMs is illustrated by an empirical study on the structure of the cross-regional R&D collaboration network of the German chemical industry. The empirical results confirm the importance of determinants at all three levels. It is shown that in addition to determinants at the node and dyad level, the structural network level determinant “triadic closure” helps in explaining the structure of the network. That is, regions that are indirectly linked to each other are more likely to be directly linked as well.

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previous chapter Recent Developments of Complex Network Analysis in Spatial Planning
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Appendix
Available only for authorised users
Footnotes
1
The relational data derived from the 5th, 6th, and 7th EU-Framework Programmes are (currently) a good example in this respect. While they represent longitudinal data, it covers only a limited time-period (1998–2013). Of course, this may change when data on future programs will become available.
 
2
This figure is based on the number of executing organizations (“Ausführende Stelle”) as given in the data. Many of these organizations are part of larger organizations. This has however little relevance for the results as all data are aggregated to the regional level.
 
3
The latest version of the “Patentatlas” was published in 2006 and includes the patent data up to 2005. We use the aggregated numbers for 2001–2005 to minimize annual fluctuation.
 
4
The employment numbers are relatively stable over time. Using data for a single year is therefore considered appropriate.
 
Literature
Autant-Bernard C, Billand P, Frachisse D, Massard N (2007) Social distance versus spatial distance in R&D co-operation: empirical evidence from European collaboration choices in micro and nanotechnologies. Pap Reg Sci 86:495–519CrossRef
Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 15:509–512
Boschma R (2005) Proximity and innovation: a critical assessment. Reg Stud 39(1):61–74CrossRef
Boschma RA, Ter Wal ALJ (2007) Knowledge networks and innovative performance in an industrial district: the case of a footwear district in the south of Italy. Ind Innov 14:177–199CrossRef
Breschi S, Lissoni F (2009) Mobility of skilled workers and co-invention networks: an anatomy of localized knowledge flows. J Econ Geogr 9:439–468CrossRef
Broekel T (2007) A concordance between industries and technologies – matching the technological fields of the Patentatlas to the German industry classification. Jenaer economic research papers, 2007-013
Broekel T, Boschma R (2012) Knowledge networks in the Dutch aviation industry: the proximity paradox. J Econ Geogr 12:409–433CrossRef
Broekel T, Graf H (2012) Public research intensity and the structure of German R&D networks: a comparison of 10 technologies. Econ Innov New Technol 21(4):345–372CrossRef
Broekel T, Hartog M (2013) Explaining the structure of inter-organizational networks using exponential random graph models: does proximity matter? Industry Innov 20(3):277–295CrossRef
Cohen WM, Levinthal DA (1990) Absorptive capacity: a new perspective on learning and innovation. Adm Sci Q 35:128–152CrossRef
Coleman JS (1988) Social capital in the creation of human capital. Am J Sociol 94(Suppl):S95–S120CrossRef
Fischer MM, Scherngell T, Jansenberger E (2006) The geography of knowledge spillovers between high-technology firms in Europe. Evidence from a spatial interaction modeling perspective. Geogr Anal 38:288–309CrossRef
Fritsch M, Slavtchev V (2007) Universities and innovation in space. Ind Innov 14:201–218CrossRef
Giuliani E, Bell M (2005) The micro-determinants of meso-learning and innovation: evidence from a Chilean wine cluster. Res Policy 34:47–68CrossRef
Glückler J (2010) The evolution of a strategic alliance network: exploring the case of stock photography. In: Boschma R, Martin R (eds) Handbook of evolutionary economic geography. Edward Elgar, Cheltenham, pp 298–315
Greif S, Schmiedl D (2002) Patentatlas 2002 Dynamik und Strukturen der Erfindungstätigkeit. Deutsches Patent- und Markenamt, München
Greif S, Schmiedl D, Niedermeyer G (2006) Patentatlas 2006. Regionaldaten der Erfindungstätigkeit. Deutsches Patent- und Markenamt, München
Gulati R (1999) Network location and learning: the influence of network resources and firm capabilities on alliance formation. Strateg Manag J 20:397–420CrossRef
Handcock MS (2003) Statistical models for social networks: degeneracy and inference. In: Breiger RL, Carley KM, Pattison P (eds) Dynamic social network modeling and analysis: workshop summary and papers. National Academies Press, Washington, DC, pp 229–240
Hoekman J, Frenken K, Van Oort F (2009) The geography of collaborative knowledge production in Europe. Ann Reg Sci 43:721–738CrossRef
Hoekman J, Frenken K, Tijssen RJW (2010) Research collaboration at a distance: changing spatial patterns of scientific collaboration within Europe. Res Policy 39:662–673CrossRef
Hunter DR (2007) Curved exponential family models for social networks. Soc Netw 29(2):216–230CrossRef
Hunter DR, Goodreau SM, Handcock MS (2008) Goodness of fit for social network models. J Am Stat Assoc 103:248–258CrossRef
Jaffe A (1989) Real effects of academic research. Am Econ Rev 79(5):957–970
LeSage J, Fischer MM, Scherngell T (2007) Knowledge spillovers across Europe. Evidence from a Poisson spatial interaction model with spatial effects. Pap Reg Sci 86:393–421CrossRef
Maggioni MA, Nosvelli M, Uberti TE (2007) Space versus networks in the geography of innovation: a European analysis. Pap Reg Sci 86:471–493CrossRef
Maskell P, Malmberg A (1999) Localized learning and industrial competitiveness. Camb J Econ 23:167–186CrossRef
McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27:415–444CrossRef
Metcalfe S (1995) The economic foundations of technology policy: equilibrium and evolutionary perspectives. In: Stoneman P (ed) Handbook of the economics of innovation and technological change. Basil Blackwell, Oxford, pp 409–512
Nelson R, Winter S (1982) An evolutionary theory of economic change. Belknap, Cambridge, MA
Nooteboom B (2000) Learning and innovation in organizations and economies. Oxford University Press, Oxford
Ponds R, Van Oort F, And Frenken K (2007) The geographical and institutional proximity of research collaboration. Pap Reg Sci 86:423–443CrossRef
Powell WW, White DR, Koput KW, Owen-Smith J (2005) Network dynamics and field evolution: the growth of interorganizational collaboration in the life sciences. Am J Sociol 110:1132–1206CrossRef
Robins R, Snijders T, Wang P, Handcock M, Pattison P (2006) Recent developments in exponential random graph (p*) models for social networks. Soc Netw 29:192–215CrossRef
Robins G, Pattison P, Kalish Y, Lusher D (2007) An introduction to exponential random graph (p*) models for social networks. Soc Netw 29:173–191CrossRef
Saul ZM, Filkov V (2007) Exploring biological network structure using exponential random graph models. Bioinformatics 23:2604–2611CrossRef
Scherngell T, Barber MJ (2009) Spatial interaction modeling of cross-region R&D collaboration: empirical evidence from the 5th EU framework programme. Pap Reg Sci 88:531–546CrossRef
Scherngell T, Barber MJ (2011) Distinct spatial characteristics of industrial and public research collaborations: evidence from the fifth EU framework programme. Ann Reg Sci 46:247–266CrossRef
Snijders TAB, Pattison PE, Robins G, Handcock MS (2006) New specifications for exponential random graph models. Sociol Methodol 36:99–153CrossRef
Snijders TAB, van de Bunt GG, Steglich CEG (2010) Introduction to stochastic actor-based models for network dynamics. Soc Netw 32:44–60CrossRef
Sobrero M, Roberts EB (2001) The trade-off between efficiency and learning in interorganizational relationships for product development. Manag Sci 47:493–511CrossRef
Ter Wal ALJ (2011) The dynamics of inventor networks in German biotechnology: geographical proximity versus triadic closure. Pap Evol Econ Geogr 11:02
Ter Wal ALJ, Boschma RA (2009) Applying social network analysis in economic geography: framing some key analytic issues. Ann Reg Sci 43:739–756CrossRef
Uddin S, Hamra J, Hossain L (2012) Exploring communication networks to understand organizational crisis using exponential random graph models. Comput Math Organ Theor. doi:10.​1007/​s10588-011-9104-8
Van Duin MAJ, Gille KJ, Handcock MS (2009) A framework for the comparison of maximum pseudo-likelihood and maximum likelihood estimation of exponential family random graph models. Soc Netw 31:52–62CrossRef
Varas MLL (2007) Essays in social space: applications to Chilean communities on inter-sector social linkages, social capital, and social justice. Dissertation at University of Illinois, Urbana-Champaign
Metadata
Title
Determinants of Cross-Regional R&D Collaboration Networks: An Application of Exponential Random Graph Models
Authors
Tom Broekel
Matté Hartog
Copyright Year
2013
Book
The Geography of Networks and R&D Collaborations

Print ISBN: 978-3-319-02698-5

Electronic ISBN: 978-3-319-02699-2

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-319-02699-2_4