Quantifying knowledge exchange in R&D networks: a data-driven model

  • Giacomo Vaccario
  • Mario V. Tomasello
  • Claudio J. Tessone
  • Frank Schweitzer
Regular Article

Abstract

We propose a model that reflects two important processes in R&D activities of firms, the formation of R&D alliances and the exchange of knowledge as a result of these collaborations. In a data-driven approach, we analyze two large-scale data sets, extracting unique information about 7500 R&D alliances and 5200 patent portfolios of firms. These data are used to calibrate the model parameters for network formation and knowledge exchange. We obtain probabilities for incumbent and newcomer firms to link to other incumbents or newcomers able to reproduce the topology of the empirical R&D network. The position of firms in a knowledge space is obtained from their patents using two different classification schemes, IPC in eight dimensions and ISI-OST-INPI in 35 dimensions. Our dynamics of knowledge exchange assumes that collaborating firms approach each other in knowledge space at a rate μ for an alliance duration τ. Both parameters are obtained in two different ways, by comparing knowledge distances from simulations and empirics and by analyzing the collaboration efficiency \(\mathcal {\hat {C}}_{n}\). This is a new measure that takes in account the effort of firms to maintain concurrent alliances, and is evaluated via extensive computer simulations. We find that R&D alliances have a duration of around two years and that the subsequent knowledge exchange occurs at a very low rate. Hence, a firm’s position in the knowledge space is rather a determinant than a consequence of its R&D alliances. From our data-driven approach we also find model configurations that can be both realistic and optimized with respect to the collaboration efficiency \(\mathcal {\hat {C}}_{n}\). Effective policies, as suggested by our model, would incentivize shorter R&D alliances and higher knowledge exchange rates.

Keywords

Inter-firm network R&D alliances Patents Knowledge exchange Agent-based model 

JEL Classification

C63 D85 O31 

Notes

Compliance with Ethical Standards

GV acknowledges support from the Swiss State Secretariat for Education, Research and Innovation (SERI), Grant No. C14.0036 as well as from EU COST Action TD1210 KNOWeSCAPE. M. V. T. acknowledges financial support from the Seed Project SP-RC 01-15 “Performance and resilience of collaboration networks”, granted by the ETH Zurich Risk Center. CJT acknowledges financial support from the University Research Priority Program on Social Network, University of Zurich.

Conflict of interests

The authors declare that they have no conflict of interest.

References

  1. Ahuja G (2000) Collaboration networks, structural holes, and innovation: a longitudinal study. Adm Sci Q 45(3):425–455CrossRefGoogle Scholar
  2. Axelrod R (1997) The dissemination of culture. J Confl Resolut 41(2):203–226CrossRefGoogle Scholar
  3. Baum J, Cowan R, Jonard N (2010) Network-independent partner selection and the evolution of innovation networks. Manag Sci 56(11):2094–2110CrossRefGoogle Scholar
  4. Baum JA, Calabrese T, Silverman BS (2000) Don’t go it alone: alliance network composition and startups’ performance in Canadian biotechnology. Strat Manag J 21(3):267–294CrossRefGoogle Scholar
  5. Burt R (1992) Structural holes: the social structure of competition cambridge. Harvard University Press, MassachussetsGoogle Scholar
  6. Cowan R, Jonard N, Ozman M (2004) Knowledge dynamics in a network industry. Technol Forecast Soc Chang 71(5):469–484CrossRefGoogle Scholar
  7. Cowan R, Jonard N, Zimmermann J (2007) Bilateral collaboration and the emergence of innovation networks. Manag Sci 53(7):1051–1067CrossRefGoogle Scholar
  8. Das T, Teng B (2000) A resource-based theory of strategic alliances. J Manag 26(1):31Google Scholar
  9. Deffuant G, Neau D, Amblard F, Weisbuch G (2000) Mixing beliefs among interacting agents. Adv Complex Syst 3(4):87–98CrossRefGoogle Scholar
  10. DeGroot MH (1974) Reaching a consensus. J Am Stat Assoc 69(345):118–121CrossRefGoogle Scholar
  11. Fagiolo G, Dosi G (2002) Exploitation, exploration and innovation in a model of endogenous growth with locally interacting agents. LEM Papers SeriesGoogle Scholar
  12. Fagiolo G, Dosi G (2003) Exploitation, exploration and innovation in a model of endogenous growth with locally interacting agents. Struct Chang Econ Dyn 14 (3):237–273CrossRefGoogle Scholar
  13. Fischer MM, Fröhlich J (2001) Knowledge, complexity and innovation systems. Springer Science & Business MediaGoogle Scholar
  14. Fruchterman T, Reingold E (1991) Graph drawing by force-directed placement. Software- Practice and Experience 21(11):1129–1164CrossRefGoogle Scholar
  15. Garas A, Tomasello MV, Schweitzer F (2017) Newcomers vs. incumbents: how firms select their partners for R&D collaborations. arXiv:1403.3298
  16. Gilbert N (2004) Agent-based social simulation: dealing with complexity. Tech. Rep., Center for Research on Social Simulation University of Surrey, Guildford, UKGoogle Scholar
  17. Gomes-Casseres B, Hagedoorn J, Jaffe A (2006) Do alliances promote knowledge flows? J Financ Econ 80(1):5–33CrossRefGoogle Scholar
  18. Grant R, Baden-Fuller C (2004) A knowledge accessing theory of strategic alliances. J Manag Stud 41(1):61–84CrossRefGoogle Scholar
  19. Groeber P, Schweitzer F, Press K (2009) How groups can foster consensus: the case of local cultures. Journal of Artificial Societies and Social Simulation 12(2):4Google Scholar
  20. Gulati R, Gargiulo M (1999) Where do interorganizational networks come from? Am J Sociol 104(5):1398–1438CrossRefGoogle Scholar
  21. Gulati R, Sytch M, Tatarynowicz A (2012) The rise and fall of small worlds: exploring the dynamics of social structure. Organ Sci 23(2):449–471CrossRefGoogle Scholar
  22. Hagedoorn J (2002) Inter-firm R&D partnerships: an overview of major trends and patterns since 1960. Res Policy 31(4):477–492CrossRefGoogle Scholar
  23. Hagedoorn J, Link AN, Vonortas NS (2000) Research partnerships. Res Policy 29(4-5):567–586CrossRefGoogle Scholar
  24. Hanaki N, Nakajima R, Ogura Y (2010) The dynamics of R&D network in the IT industry. Res Policy 39(3):386–399CrossRefGoogle Scholar
  25. Hegselmann R, Krause U (2002) Opinion dynamics and bounded confidence: models, analysis and simulation. Journal of Artificial Societies and Social Simulation 5(3). http://jasss.soc.surrey.ac.uk/5/3/2.html
  26. Inkpen AC, Ross J (2001) Why do some strategic alliances persist beyond their useful life? Calif Manag Rev 44(1):132–148CrossRefGoogle Scholar
  27. König MD, Battiston S, Napoletano M, Schweitzer F (2012) The efficiency and stability of R&D networks. Games and Economic Behavior 75(2):694–713CrossRefGoogle Scholar
  28. Liebeskind JP (1996) Knowledge, strategy, and the theory of the firm. Strateg Manag J 17:93–109CrossRefGoogle Scholar
  29. Mowery D, Oxley J, Silverman B (1998) Technological overlap and interfirm cooperation: implications for the resource-based view of the firm. Res Policy 27 (5):507–523CrossRefGoogle Scholar
  30. 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):5–21CrossRefGoogle Scholar
  31. Phelps CC (2010) A longitudinal study of the influence of alliance network structure and composition on firm exploratory innovation. Acad Manag J 53(4):890–913CrossRefGoogle Scholar
  32. Podolny JM (1993) A status-based model of market competition. Am J Sociol 98(4):829–872CrossRefGoogle Scholar
  33. Powell W, Koput K, Smith-Doerr L (1996) Interorganizational collaboration and the locus of innovation: networks of learning in biotechnology. Adm Sci Q 41 (1):116–145CrossRefGoogle Scholar
  34. Powell W, White D, Koput K, Owen-Smith J (2005) Network dynamics and field evolution: the growth of interorganizational collaboration in the life sciences. Am J Sociol 110(4):1132–1205CrossRefGoogle Scholar
  35. Pyka A, Fagiolo G (2007) Agent-based modelling: a methodology for neo-schumpeterian economics. Edward Elgar Publishing, chap 29Google Scholar
  36. Raub W, Weesie J (1990) Reputation and efficiency in social interactions: an example of network effects. Am J Sociol 96(3):626CrossRefGoogle Scholar
  37. Rosenkopf L, Almeida P (2003) Overcoming local search through alliances and mobility. Manag Sci 49(6):751–766CrossRefGoogle Scholar
  38. Rosenkopf L, Nerkar A (2001) Beyond local search: boundary-spanning, exploration, and impact in the optical disk industry. Strat Manag J 22(4):287–306CrossRefGoogle Scholar
  39. Rosenkopf L, Padula G (2008) Investigating the microstructure of network evolution: alliance formation in the mobile communications industry. Organ Sci 19 (5):669CrossRefGoogle Scholar
  40. Rosenkopf L, Schilling M (2007) Comparing alliance network structure across industries: observations and explanations. Strateg Entrep J 1(3-4):191–209CrossRefGoogle Scholar
  41. Sampson RC (2007) R&D Alliances and firm performance: the impact of technological diversity and alliance organization on innovation. Acad Manag J 50 (2):364–386CrossRefGoogle Scholar
  42. Schmoch U (2008) Concept of a technology classification for country comparisons. Final report to the world intellectual property organisation (WIPO)Google Scholar
  43. Schweitzer F, Behera L (2009) Nonlinear voter models: the transition from invasion to coexistence. Eur Phys J B Condensed Matter and Complex Systems 67 (3):301–318CrossRefGoogle Scholar
  44. Thomson-Reuters (2013) SDC Platinum dataset. http://thomsonreuters.com/sdc-platinum/
  45. Tomasello MV, Burkholz R, Schweitzer F (2017a) Modeling the formation of R&D alliances: an agent-based model with empirical validation. Economics E-Journal, Discussion Papers No. 2017–107, Kiel Institute for the World EconomyGoogle Scholar
  46. Tomasello MV, Napoletano M, Garas A, Schweitzer F (2017b) The rise and fall of R&D networks. Ind Corp Chang 26(4):617–646Google Scholar
  47. Tomasello MV, Perra N, Tessone CJ, Karsai M, Schweitzer F (2014) The role of endogenous and exogenous mechanisms in the formation of R&D networks. Sci Rep 4:5679CrossRefGoogle Scholar
  48. Tomasello MV, Tessone CJ, Schweitzer F (2016) A model of dynamic rewiring and knowledge exchange in R&D networks. Adv Complex Syst 19(1–2):1650004CrossRefGoogle Scholar
  49. Walker G, Kogut B, Shan W (1997) Social capital, structural holes and the formation of an industry network. Organ Sci 8(2):109–125CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Giacomo Vaccario
    • 1
  • Mario V. Tomasello
    • 1
    • 2
  • Claudio J. Tessone
    • 1
    • 3
  • Frank Schweitzer
    • 1
  1. 1.Chair of Systems Design, Department of Management, Technology and EconomicsETH ZürichZürichSwitzerland
  2. 2.Ernst & YoungZürichSwitzerland
  3. 3.URPP Social Networks, Department of Business AdministrationUniversität ZürichZürichSwitzerland

Personalised recommendations