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Collaboration and growth in a large research cooperative: A network analytic approach

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  • Ducharme, Lori J.
  • Fujimoto, Kayo
  • Kuo, Jacky
  • Stewart, Jonathan
  • Taylor, Bruce
  • Schneider, John

Abstract

Research networks encourage team science and facilitate collaboration within and across research teams. While many analyses have examined the output of these collaborative networks (e.g., authorship networks, publications, grant applications), less attention has been paid to the formative phases of these initiatives. This article presents analyses of a whole-network survey of investigators participating in a new research initiative, and examines the development of collaborative ties over the network’s first year. In particular, we examine the influence of research center affiliation, seniority, and prior network experience on the number and structure of collaborative ties, including participants’ bridging and broker roles. Such analyses can inform the overall management of the project in purposefully promoting new collaboration opportunities, and may ultimately predict the number of collaborative products generated by the network members.

Suggested Citation

  • Ducharme, Lori J. & Fujimoto, Kayo & Kuo, Jacky & Stewart, Jonathan & Taylor, Bruce & Schneider, John, 2024. "Collaboration and growth in a large research cooperative: A network analytic approach," Evaluation and Program Planning, Elsevier, vol. 102(C).
    • Handle: RePEc:eee:epplan:v:102:y:2024:i:c:s0149718923001520
    DOI: 10.1016/j.evalprogplan.2023.102375
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    References listed on IDEAS

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    1. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    2. Jodi E. Basner & Katrina I. Theisz & Unni S. Jensen & C. David Jones & Ilya Ponomarev & Pawel Sulima & Karen Jo & Mariam Eljanne & Michael G. Espey & Jonathan Franca-Koh & Sean E. Hanlon & Nastaran Z., 2013. "Measuring the evolution and output of cross-disciplinary collaborations within the NCI Physical Sciences--Oncology Centers Network," Research Evaluation, Oxford University Press, vol. 22(5), pages 285-297, October.
    3. Woodland, Rebecca H. & Douglas, Jacole & Matuszczak, Densie, 2021. "Assessing organizational capacity for diffusion: A school-based social network analysis case study," Evaluation and Program Planning, Elsevier, vol. 89(C).
    4. Amar Dhand & Douglas A Luke & Bobbi J Carothers & Bradley A Evanoff, 2016. "Academic Cross-Pollination: The Role of Disciplinary Affiliation in Research Collaboration," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-13, January.
    5. Matthew Claudel & Emanuele Massaro & Paolo Santi & Fiona Murray & Carlo Ratti, 2017. "An exploration of collaborative scientific production at MIT through spatial organization and institutional affiliation," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-22, June.
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    Cited by:

    1. Weijun Hu & Mingxing Li & Xiaomeng Chi & Xinxing Wang & Asad Ullah Khan, 2024. "Intangible cultural heritage research in China from the perspective of intellectual property rights based on bibliometrics and knowledge mapping," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.

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