Thesis: S.M in Management Research, Massachusetts Institute of Technology, Sloan School of Management, September, 2020 / Hagay Volvovsky is the sole author of thesis. Ray Reagans and Ron Burt are professors and not authors. Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 17-21). / Learning is an often given explanation for why social networks improve performance. For example, a closed network allows individuals to identify and share best practices and to coordinate joint problem solving and each is conducive for learning. Despite the widespread belief that networks affect learning, there is little direct evidence linking social networks to learning. And opposing network features are often emphasized. While some scholars have emphasized the importance of closed networks, others have highlighted networks that span structural holes, a network form that encourages divergent thinking and creative problem solving. Without direct evidence, we do not know if social networks affect learning, and if they do which network forms are most conducive for learning. We analyzed learning rates across 45 teams that varied in terms of how team members were allowed to communicate with each other. All teams exhibited evidence for learning but teams in open networks learned faster than teams in closed networks. The best teams, however, combined elements of open and closed network structures. We discuss the implications of our results for research on networks, knowledge transfer, and learning. / by Ray Reagans, Hagay Volvovsky, Ron Burt. / S.M in Management Research / S.MinManagementResearch Massachusetts Institute of Technology, Sloan School of Management
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/130222 |
| Date | January 2020 |
| Creators | Volvovsky, Hagay(Hagay Constantin), Reagans, Ray E., Burt, Ronald S. |
| Contributors | Roberto Fernandez., Sloan School of Management., Sloan School of Management |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 27 pages, application/pdf |
| Rights | MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided., http://dspace.mit.edu/handle/1721.1/7582 |
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