The Shape of Attribute Alignment: A Novel Method to See Who on the Team Is Responsible for Its Performance
Abstract
A major problem in current team research is that little consideration is given to patterns team member attributes before aggregating them into group-level properties. However, we know that team members have multiple attributes, and these attributes interact to dictate their thoughts, feelings, and behaviors, which in turn interact to produce collective action. By conceptualizing teams as matrices composed of rows representing team members and columns representing their attributes, the attribute alignment framework can account for the impact of the alignment of two or more within-team member attributes on team outcomes by calculating the distance between attribute vectors. However, the current approach to alignment does not distinguish between vector magnitudes. This means that the attributes of members low on all attributes are just as aligned as those high on all attributes. Here, we build on our previous attribute alignment approach by defining a method to account for the shape of attribute alignment. Then, we test it on seven datasets, including one novel field dataset from the USMA at West Point. To do this, we apply basis functions to differentiate attribute patterns. By mapping different shapes based on whether individual members contain lower than average, average, or higher than average levels of attributes, we can tell which members account for team performance and how much they account for. We also build software allowing researchers and Army practitioners to account for how specific patterns of attributes, within team members and across the team, influence specific team processes and outcomes.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 23, 2023
- Source ID
- W911NF2310299
Entities
People
- Kyle J Emich
Organizations
- Army Contracting Command
- United States Army
- University of Delaware