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Scientific teams: Self-assembly, fluidness, and interdependence

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  • Wang, Jian
  • Hicks, Diana

Abstract

Science is increasingly produced in collaborative teams, but collaborative teams in science are self-assembled and fluid. Such characteristics call for a network approach to account for external activities responsible for team product but taking place beyond closed team boundaries in the open network. Given such characteristics of collaborative teams in science, we empirically test the interdependence between collaborative teams in the same network. Specifically, using fixed effects Poisson models and panel data of 1310 American scientists’ life-time publication histories, we demonstrate knowledge spillovers from new collaborators to other teams not involving these new collaborators. Our findings have important implications for studying the organization of science.

Suggested Citation

  • Wang, Jian & Hicks, Diana, 2015. "Scientific teams: Self-assembly, fluidness, and interdependence," Journal of Informetrics, Elsevier, vol. 9(1), pages 197-207.
    • Handle: RePEc:eee:infome:v:9:y:2015:i:1:p:197-207
    DOI: 10.1016/j.joi.2014.12.006
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    Cited by:

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    3. Meijun Liu & Dongbo Shi & Jiang Li, 2017. "Double-edged sword of interdisciplinary knowledge flow from hard sciences to humanities and social sciences: Evidence from China," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-16, September.
    4. Bastian Rake & Pablo D’Este & Maureen McKelvey, 2021. "Exploring network dynamics in science: the formation of ties to knowledge translators in clinical research," Journal of Evolutionary Economics, Springer, vol. 31(5), pages 1433-1464, November.
    5. Enric Senabre Hidalgo & Mayo Fuster Morell, 2019. "Co-designed strategic planning and agile project management in academia: case study of an action research group," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-13, December.
    6. Jian Wang & Bart Thijs & Wolfgang Glänzel, 2015. "Interdisciplinarity and Impact: Distinct Effects of Variety, Balance, and Disparity," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
    7. Tang, Xuli & Li, Xin & Ding, Ying & Song, Min & Bu, Yi, 2020. "The pace of artificial intelligence innovations: Speed, talent, and trial-and-error," Journal of Informetrics, Elsevier, vol. 14(4).
    8. Guo, Liying & Wang, Yang & Li, Meiling, 2024. "Exploration, exploitation and funding success: Evidence from junior scientists supported by the Chinese Young Scientists Fund," Journal of Informetrics, Elsevier, vol. 18(2).
    9. Hamid Bouabid & Hind Achachi, 2022. "Size of science team at university and internal co-publications: science policy implications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 6993-7013, December.
    10. Aliakbar Akbaritabar & Andrés F. Castro Torres & Vincent Larivière, 2023. "A global perspective on the social structure of science," MPIDR Working Papers WP-2023-029, Max Planck Institute for Demographic Research, Rostock, Germany.
    11. D’Ippolito, Beatrice & Rüling, Charles-Clemens, 2019. "Research collaboration in Large Scale Research Infrastructures: Collaboration types and policy implications," Research Policy, Elsevier, vol. 48(5), pages 1282-1296.
    12. Enrique Rosales-Asensio & Carlos Sierra & Clara Pérez-Molina & Jesús Romero-Mayoral & Antonio Colmenar-Santos, 2021. "Teaching Using Collaborative Research Projects: Experiences with Adult Learners in Distance Education," Sustainability, MDPI, vol. 13(18), pages 1-12, September.
    13. Ma, Guoshuai & Yuhua, Qian & Zhang, Yayu & Yan, Hongren & Cheng, Honghong & Hu, Zhiguo, 2022. "The recognition of kernel research team," Journal of Informetrics, Elsevier, vol. 16(4).

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