Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/50416

Understanding Shared Familiarity and Team Performance through Network Analytics

File SizeFormat 
paper0529.pdf535.57 kBAdobe PDFView/Open

Item Summary

Title: Understanding Shared Familiarity and Team Performance through Network Analytics
Authors: Espinosa, J. Alberto
Clark, Mark
Carter, Dorothy R.
Keywords: Knowledge Economics
Team knowledge, team familiarity, task familiarity, shared familiarity, team performance
Issue Date: 03 Jan 2018
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.
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/50416
ISBN: 978-0-9981331-1-9
DOI: 10.24251/HICSS.2018.527
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Knowledge Economics


Please contact sspace@hawaii.edu if you need this content in an alternative format.

Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.