Towards a taxonomy of team workflow structures

Sean M. Fitzhugh ()
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Sean M. Fitzhugh: DEVCOM Army Research Laboratory

Journal of Computational Social Science, 2024, vol. 7, issue 3, No 21, 2895 pages

Abstract: Abstract Team workflow represents interactions between individuals and specific actions or tasks. Individuals’ interactions have important effects on fellow teammates’ actions by expanding or constraining actions available to them. For example, teammates may avoid performing the same action to avoid duplication of effort or they may perform their actions sequentially if one task’s completion is a prerequisite for another task. Complex dependencies embedded in these interactions suggest the need to understand team workflows from a relational perspective. As workflow structures are shaped by elements of organizational design, cognitive factors, and features of the task environment, no single workflow structure is optimal for all teams, and team workflows may manifest in countless distinct configurations. Through a systematic, network-based representation of team workflows, this paper uses a sample of 139,500 teams on GitHub to identify common patterns of team workflows. Each team is represented as a two-mode network where individuals form ties to up to fifteen distinct actions capturing productivity, discussion, and team management. Several node-level and graph-level centrality indices highlight patterns of differentiation across team workflows, and a k-means clustering algorithm detects three distinct clusters of team workflow structures: small teams of highly active generalists, small teams with a moderately active mix of focused and generalist members, and large, segmented teams of focused individuals collectively engaging in a few extremely popular actions. These results demonstrate how a structural representation of team workflows provides unique insight into team behavior and highlights distinctions that may otherwise be lost when examining team activity in aggregate.

Keywords: Team workflow; Task-oriented groups; Social networks; Big data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s42001-024-00327-x

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