Advertisement

Structural holes, innovation and the distribution of ideas

  • Robin CowanEmail author
  • Nicolas Jonard
Open Access
Original Article

Abstract

We model knowledge diffusion in a population of agents situated on a network, interacting only over direct ties. Some agents are by nature traders, others are by nature “givers”: traders demand a quid pro quo for information transfer; givers do not. We are interested in efficiency of diffusion and explore the interplay between the structure of the population (proportion of traders), the network structure (clustering, path length and degree distribution), and the scarcity of knowledge. We find that at the global level, trading (as opposed to giving) reduces efficiency. At the individual level, highly connected agents do well when knowledge is scarce, agents in clustered neighbourhoods do well when it is abundant. The latter finding is connected to the debate on structural holes and social capital.

Keywords

Social Capital Degree Distribution Knowledge Diffusion Structural Hole Herfindahl Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Ahuja G (2000) Collaboration networks, structural holes and innovation: a longitudinal study. Adm Sci Q 45:425–455CrossRefGoogle Scholar
  2. Allen R (1983) Collective invention. J Econ Behav Org 4:1–24CrossRefGoogle Scholar
  3. Barabási A, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512CrossRefGoogle Scholar
  4. Baum JAC, Shipilov AV, Rowley TJ (2003) Where do small worlds come from? Ind Corp Change 12:697–725CrossRefGoogle Scholar
  5. Burt RS (1992) Structural holes: the social structure of competition. Harvard University Press, CambridgeGoogle Scholar
  6. Cowan R, Jonard N (2003) The dynamics of collective invention. J Econ Behav Org 52:513–532CrossRefGoogle Scholar
  7. Cowan R, Jonard N (2004) Network structure and the diffusion of knowledge. J Econ Dyn Control 28:1557–1575CrossRefGoogle Scholar
  8. Dyer JH, Nobeoka K (2000) Creating and managing a high performance knowledge sharing network: the Toyota case. Strateg Manage J 21:345–367CrossRefGoogle Scholar
  9. Granstrand O, Sjolander S (1990) Managing innovation in multi-technology corporations. Res Policy 19:35–60CrossRefGoogle Scholar
  10. Gulati R, Gargiulo M (1999) Where do inter-organizational networks come from? Am J Sociol 104:1439–1493CrossRefGoogle Scholar
  11. Lamoureaux NR (1999) Accounting for capitalism in early american history: farmers, merchants, manufacturers, and their economic worlds. Technical report, UCLA, 1999. Available at http://www.sscnet.ucla.edu/history/activities/usccpapers/lamoreaux.htmlGoogle Scholar
  12. McGaw JA (1987) Most wonderful machine: mechanization and social change in Berkshire paper making. 1801-1885. Princeton University Press, Princeton (1987)Google Scholar
  13. Podolny JM (1993) A status-based model of market competition. Am J Sociol 98:829–872CrossRefGoogle Scholar
  14. Powell WW (1990) Neither markets nor hierarchies: network forms of organization. In: Shaw BM, Cummings LL (eds) Research in organizational behavior, vol 12, pp 395–336Google Scholar
  15. Powell WW, Koput KW, Smith-Doerr L (1996) Inter-organizational collaboration and the locus of innovation: networks of learning in biotechnology. Adm Sci Q 41:116–145CrossRefGoogle Scholar
  16. Powell WW, White DR, Koput KW, Owen-Smith J (2005) Network dynamics and field evolution: the growth of inter-organizational collaboration in the life sciences. Am J Sociol 110:1132–1205CrossRefGoogle Scholar
  17. Rowley T, Behrens D, Krachhardt D (2000) Redundant governance structures: an analysis of structural and relational embeddedness in the steel and semiconductor industries. Strateg Manage J 21:369–386CrossRefGoogle Scholar
  18. von Hippel E (1987) Cooperation between rivals: informal know-how trading. Res Policy 16:291–302CrossRefGoogle Scholar
  19. Walker G, Kogut B, Shan W (1997) Social capital, structural holes and the formation of an industry network. Org Sci 8:108–125CrossRefGoogle Scholar
  20. Watts D, Strogatz S (1998) Collective dynamics of small-world networks. Nature 393:440–442CrossRefGoogle Scholar
  21. Werker C, Brenner T (2004) Empirical calibration of simulation models. Max Planck Institute, Jena; Papers on Economics and Evolution number 2004-10Google Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.BETAUniversité Louis PasteurStrasbourgFrance
  2. 2.UNU-MERITMaastricht UniversityMaastrichtThe Netherlands
  3. 3.Université du LuxembourgLuxembourgLuxembourg

Personalised recommendations