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For cross-disciplinary teams to be effective, what knowledge should be shared and what knowledge should remain unique to individual team members? We adopted a mixed-method approach using a sample of grant-funded teams composed of principal and co-principal investigators of diverse disciplines. Interviewees and survey respondents especially favored knowledge similarity over uniqueness for team vision and teamwork, but less preference for convergence emerged for research outcomes and research content (theory, operational details of methodology, analysis). Moreover, more team knowledge convergence was associated with higher perceived collaboration satisfaction and trended in the direction of more grants, publications, and conference presentations.
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Learning in the machine: To share or not to share?
- Award ID(s):
- 1839429
- PAR ID:
- 10191725
- Date Published:
- Journal Name:
- Neural Networks
- Volume:
- 126
- Issue:
- C
- ISSN:
- 0893-6080
- Page Range / eLocation ID:
- 235 to 249
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.neunet.2020.03.016
