Skip to main content
SpringerLink
Log in

Multilateral R&D Collaboration: An ERGM Application on Biotechnology

  • Chapter
  • First Online:
The Geography of Networks and R&D Collaborations

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

Abstract

This chapter presents an empirical study on formation of multilateral R&D collaboration networks among organizations. The objective of the study is to investigate how geography and heterogeneity in institutional types affect the way organizations come together around consortiums to perform R&D. It makes use of data on project proposals submitted to the 7th Framework Program (FP) in the field of biotechnology to construct a two-mode network. It employs extensions of exponential random graph models (ERGM) (Frank and Strauss, J Am Stat Assoc 81(395):832–842, 1986; Wasserman and Pattison, Psychometrika 61(3):401–425, 1996, for affiliation networks (Wang et al., Soc Netw 31:12–25, 2009). The results show that higher education institutions and research institutions tend to show higher connectivity and hence bridge learning across consortiums. Furthermore, organizations located in the core European countries tend to participate in the same consortium and these consortiums tend to be small in size. Finally, homophily in institutional types and network effects do not affect the formation process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    For statistical and mathematical foundations of ERGM, readers are referred to the joint probability of a Markov field or the extensions of statistical mechanics of Gibbs to the study of networks by Park and Newman (2004), and to the Hammersely Clifford Theorem (Besag 1974) proving the Gibbs-Markov equivalence.

  2. 2.

    Regulation (EC) No 1906/2006; Article 5/(1) states that “at least three legal entities must participate, each of which must be established in a Member State or associated country, and no two of which may be established in the same Member State or associated country”.

  3. 3.

    All estimations are carried out using “BPNet”, which is an extension of the PNet programme (Wang et al. 2006) and bases on MCMCMLE technique.

  4. 4.

    Convergence is measured by t-ratios calculated to check whether the estimate of the parameter vector is capable of producing a graph distribution centered at the observed network (Wang et al. 2009). Snijders (2002) suggests that if the absolute value of t-values for all local configurations (|t Q |) are less than or equal to 0.1 convergence is excellent; if 0.1 < |t Q | ≤ 0.2, it is good, else if 0.2 < |t Q | ≤ 0.3 convergence is fair.

References

  • Anselin L, Varga A, Acs Z (2000) Geographical spillovers and university research: a spatial econometric perspective. Growth Change 31:501–515

    Article  Google Scholar 

  • Autant-Bernard C et al (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):495–519

    Article  Google Scholar 

  • Bala V, Goyal S (2000) A non-cooperative model of network formation. Econometrica 68:1181–1229

    Article  Google Scholar 

  • Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Article  Google Scholar 

  • Besag J (1974) Spatial interaction and the statistical analysis of lattice systems. J R Stat Soc Ser B (Methodol) 36(2):192–236

    Google Scholar 

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

    Article  Google Scholar 

  • Boschma RA, Frenken K (2009) The spatial evolution of innovation networks: a proximity perspective. In: Boschma RA, Martin R (eds) Handbook of evolutionary economic geography. Edward Elgar, Cheltenham

    Google Scholar 

  • Cassiman B, Veugelers R (2002) R&D cooperation and spillovers: some empirical evidence from Belgium. Am Econ Rev 92(4):1169–1184

    Article  Google Scholar 

  • Cohen WM, Levinthal DA (1990) Absorptive capacity: a new perspective on learning and innovation. Adm Q 35(1):128–152, Special Issue: Technology, Organizations, and Innovation

    Article  Google Scholar 

  • Cooke P, Uranga MG, Etxebarria G (1997) Regional innovation systems: institutional and organisational dimensions. Res Policy 26:475–491

    Article  Google Scholar 

  • Cowan R, Jonard N, Zimmermann JB (2007) Bilateral collaboration and the emergence of innovation networks. Manage Sci 53(7):1051–1067

    Article  Google Scholar 

  • Fafchamps M, Goyal S, van der Leij MJ (2010) Matching and network effects. J Eur Econ Assoc 8(1):203–231

    Article  Google Scholar 

  • Feldman MP (1993) An examination of the geography of innovation. Ind Corp Change 2(3):451–470

    Google Scholar 

  • Feldman MP, Florida R (1994) The geographic sources of innovation: technological infrastructure and product innovation in the United States. Ann Assoc Am Geogr 84(2):210–229

    Article  Google Scholar 

  • Frank O, Strauss D (1986) Markov graphs. J Am Stat Assoc 81(395):832–842

    Article  Google Scholar 

  • Frenken K, Van Oort F, Verburg T (2007) Related variety, unrelated variety and regional economic growth. Reg Stud 41(5):685–697

    Article  Google Scholar 

  • Hagedoorn J (2002) Inter-firm R&D partnerships: an overview of major trends and patterns since 1960. Res Policy 31:477–492

    Article  Google Scholar 

  • Handcock MS (2003) Assessing degeneracy in statistical models of social networks. Working paper 39, Centre for Statistics and Social Sciences, University of Washington

    Google Scholar 

  • Hanneke S, Fu W, Xing EP (2010) Discrete temporal models of social networks. Electron J Stat 4:585–605

    Article  Google Scholar 

  • Hekkert MP et al (2007) Functions of inovation systems: a new approach for analyzing technological change. Technol Forecast Soc Change 74:413–432

    Article  Google Scholar 

  • Hoekman J, Frenken K, van Oort F (2009) The geography of collaborative Knowledge production in Europe. Ann Reg Sci 43(3):721–738

    Article  Google Scholar 

  • Jackson OM, Rogers BW (2007) Meeting strangers and friends of friends: how random are social networks? Am Econ Rev 97(3):890–915

    Article  Google Scholar 

  • Jackson MO, Wolinsky A (1996) A strategic model of social and economic networks. J Econ Theory 71:44–74

    Article  Google Scholar 

  • Krivitsky PN, Handcock MS (2010) A separable model for dynamic networks. arXiv:1011.1937v1

    Google Scholar 

  • Lane PJ, Lubatkin M (1998) Relative absorptive capacity and inter-organizational learning. Strateg Manage J 19:461–477

    Article  Google Scholar 

  • Lhuillery S, Pfister E (2011) Do firms know the scope of their R&D network? An empirical investigation of the determinants of network awareness on French survey data. Ind Innov 18(1):105–130

    Article  Google Scholar 

  • Regulation (EC) No 1906/2006 of the European Parliament and of the Council of 18 December 2006 laying down the rules for the participation of undertakings, research centres and universities in actions under the Seventh Framework Programme and for the dissemination of research results (2007–2013)

    Google Scholar 

  • Nooteboom B et al (2007) Optimal cognitive distance and absorptive capacity. Res Policy 36(7):1016–1034

    Article  Google Scholar 

  • Paier M, Scherngell T (2008) Determinants of collaboration in European R&D networks: empirical evidence from a binary choice model perspective. NEMO working paper 10

    Google Scholar 

  • Park J, Newman MEJ (2004) The statistical mechanics of networks. Phys Rev E 70(6), 066117

    Google Scholar 

  • Pattison P, Robins G (2002) Neighborhood-based models for social networks. Sociol Methodol 32:301–337

    Article  Google Scholar 

  • Powell WW, Koput KW, Smith-Doerr L (1996) Interorganizational collaboration and the locus of innovation: networks of learning in biotechnology. Adm Sci Q 41(1):116–145

    Article  Google Scholar 

  • Robins G et al (2007) An introduction to exponential random graph (p*) models for social networks. Soc Netw 29:173–191

    Article  Google Scholar 

  • Scherngell T, Barber MJ (2009) Spatial interaction modeling of cross-region R&D collaborations: empirical evidence from the 5th EU Framework Program. Pap Reg Sci 88(3):531–546

    Article  Google Scholar 

  • Scherngell T, Lata R (2013) Towards an integrated European Research Area? Findings from Eigenvector spatially filtered spatial interaction models using European Framework Programme data. Pap Reg Sci 92:555–577

    Google Scholar 

  • Snijders TAB (2002) Markov chain Monte Carlo estimation of exponential random graph models. J Soc Struct 3, Article 2

    Google Scholar 

  • Straus D, Ikeda M (1990) Pseudolikelihood estimation for social networks. J Am Stat Assoc 85(409):204–212

    Article  Google Scholar 

  • Uzzi B (1996) The sources and consequences of embeddedness for the economic performance of organizations: the network effect. Am Sociol Rev 61(4):674–698

    Article  Google Scholar 

  • Van der Leij M, Fafchamps M, Goyal S (2006) Matching and network effects. Discussion paper 611, University of Essex

    Google Scholar 

  • Wang P et al (2006) PNet: a program for the simulation and estimation of exponential random graph models. School of Behaviour Science, University of Melbourne. http://www.sna.unimelb.edu.au/pnet/pnet.html

  • Wang P et al (2009) Exponential random graph (p*) models for affiliation networks. Soc Netw 31:12–25

    Article  Google Scholar 

  • Wasserman S, Pattison P (1996) Logit models and logistic regression for social networks: I. An introduction to Markov graphs and p*. Psychometrika 61(3):401–425

    Article  Google Scholar 

  • Wuchty S, Jones BF, Uzzi B (2007) The increasing dominance of teams in production of knowledge. Science 316:1036–1039

    Article  Google Scholar 

  • Zaheer A, McEvily B, Perrone V (1998) Does trust matter? Exploring the effects of interorganizational and interpersonal trust on performance. Organ Sci 9(2):141–159

    Article  Google Scholar 

  • Zand DE (1972) Trust and managerial problem solving. Adm Sci Q 17(2):229–239

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université de Lyon, Lyon, F-69007, France

    Çilem Selin Hazir

  2. Université Jean Monnet, Saint-Etienne, F-42000, France

    Çilem Selin Hazir

  3. CNRS, GATE Lyon Saint-Etienne, Ecully, F-69130, France

    Çilem Selin Hazir

Authors
  1. Çilem Selin Hazir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Çilem Selin Hazir .

Editor information

Editors and Affiliations

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

    Thomas Scherngell

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Hazir, Ç.S. (2013). Multilateral R&D Collaboration: An ERGM Application on Biotechnology. 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_12

Download citation

Publish with us

Policies and ethics

Search

Navigation