Advertisement

Social Network Analysis of Team Dynamics and Intra-Organizational Development in an Aerospace Firm

  • Kristie Ogilvie
  • Dimitris Assimakopoulos
Part of the IFIP — The International Federation for Information Processing book series (IFIPAICT, volume 243)

Abstract

This research examines results from a dual case study in defining a model for high productivity and performance of cross-functional development teams in an aerospace engineering community. More specifically it explores cohesiveness and team dynamics over an approximate 4-year period in a project team that recently designed and built a highly innovative propulsion system. The ‘successful’ team delivered this propulsion system ahead of schedule, below cost, and was considered a highly productive team within the researched Aerospace firm. Ucinet is used to map k-cores, month by month, for the entire life cycle of the project. This methodology is then compared to a ‘less successful’ team to determine those variables responsible for high productivity and overall success of a highly technical research and development team. The results encompass the critical times in networked teams that inclusion in membership of the team is most critical for success.

Keywords

Team Member Social Network Analysis Interaction Level Collaborative Network Core Member 
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. 1.
    Barthelemy, J.-F., Waszak, M. R., Jones, K. M, Silcox, R. J., Silva, W. A and Nowaczyk, R.H.,“Charting Multidisciplinary Team External Dynamics using a Systems Thinking Approach,” AIAA Paper 98-4939, AIAA/USAF/NASA/ISSMOGoogle Scholar
  2. 2.
    Borgatti, S. & Cross, R. (2003). A Social Network View of Organizational Learning: Relational and Structural Dimensions of‘Know Who’. Management Science, 49 pp. 432–445.CrossRefMATHGoogle Scholar
  3. 3.
    Brown Seely, John and Duguid, Paul. “ Organizing Knowledge.” California Management Review 40, no. 3 (1998): 90–111.CrossRefGoogle Scholar
  4. 4.
    Brown, J. S., & Duguid, P. (2000). The social life of information. Boston, MA: Harvard Business School Press.Google Scholar
  5. 5.
    Burt, Ronald S. “ Structural Holes Versus Network Closure As Social Capital.” Social Capital: Theory and Research (2001): 31–56.Google Scholar
  6. 6.
    Cross, R., Parker, A., Prusak, L. & Borgatti, S.P. “ Knowing What We Know: Supporting Knowledge Creation and Sharing in Social Networks.” Organizational Dynamics 30, no. 2 (2001): 100–20.CrossRefGoogle Scholar
  7. 7.
    Dodgson, Mark. “ Learning, trust, and technological collaboration” . Human Relations, New York, Jan 1993, Volume 46, Issue 1, Page 77, 19 pages.CrossRefGoogle Scholar
  8. 8.
    Hannemann, Robert A. Introduction to Social Networking Methods, 2001.Google Scholar
  9. 9.
    Powell, W. W., K. W. Koput, L. Smith-Doerr, and J. Owen-Smith. 1999. “ Network Position and Firm Performance.” pp. 129–59 in Research in the Sociology of Organizations, edited by S. Andrews and D. Knoke, vol. 16, JAI Press.Google Scholar
  10. 10.
    Rothwell, Roy. Towards the Fifth generation Innovation Process;, Science Policy Research Unit, University of Sussexm UK, International Marketing Review Vol 11m No 1, 1994 pp. 7–31 MCB University Press 0265–1335.Google Scholar
  11. 11.
    Wassermann, S. and Faust, K (1994). Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  12. 12.
    Wellman, B. Networks in the global village: life in contemporary communities. -1999 -Boulder, Colo: Westview Press.Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2007

Authors and Affiliations

  • Kristie Ogilvie
    • 1
  • Dimitris Assimakopoulos
    • 1
  1. 1.Grenoble Ecole de ManagementEuropoleGrenobleFrance

Personalised recommendations