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Understanding Shared Familiarity and Team Performance through Network Analytics

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dc.contributor.author Espinosa, J. Alberto
dc.contributor.author Clark, Mark
dc.contributor.author Carter, Dorothy R.
dc.date.accessioned 2017-12-28T02:01:31Z
dc.date.available 2017-12-28T02:01:31Z
dc.date.issued 2018-01-03
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/50416
dc.description.abstract In this article, we propose a network approach to understanding team knowledge with archival data, offering conceptual and methodological advantages. Often, the degree to which team members’ possess shared knowledge has been conceptualized and measured as an aggregate property of a team as a whole. Rather than an aggregate property, however, we argue that shared team knowledge is more appropriately conceptualized as a network of knowledge overlaps or linkages between sets of team members. We created shared knowledge networks for a sample of 1,942 software teams based on members’ prior experiences working with one another on different tasks and teams. We included metrics representing topological features of team shared knowledge networks within predictive models of team performance. Our results suggest that network patterning provides additional predictive power for explaining software development team performance over and above the effects of average level of knowledge similarity within a team.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Knowledge Economics
dc.subject Team knowledge, team familiarity, task familiarity, shared familiarity, team performance
dc.title Understanding Shared Familiarity and Team Performance through Network Analytics
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2018.527
Appears in Collections: Knowledge Economics


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