Team Processes and Team Effectiveness
The research put forward by Michigan State University has 3 specific aims and associated deliverables that represent an integrated approach for measuring, monitoring, and regulating teamwork processes and long-term team functioning in isolated, confined, and extreme (ICE) environments:
(1) Benchmark long duration team functioning in ICE analog environments. This research will use experience sampling methods (ESM; daily assessments) to assess team functioning in ICE environments. The product of this research aim is to quantify the expected range of variation in key teamwork processes (e.g., cohesion, collaboration, conflict), identify internal and external shocks that influence variation, and assess dynamic effects on team performance. Benchmark data in ICE analog environments are essential for developing standards to distinguish expected variation in teamwork from anomalies indicative of a threat to team functioning. Such standards are essential for triggering countermeasures /interventions.
(2) Extend engineering development of an unobtrusive monitoring technology (i.e., a wearable wireless sensor package). The product of this research aim is to advance development of a prototype monitoring technology to capture dynamic multimodal (i.e., physical, physiological, and behavioral) data capturing team member and teamwork interactions. Initial validation has demonstrated the reliability and accuracy of the monitoring technology sufficient to establish proof of concept.
(3) Develop teamwork interaction metrics and regulation support systems. The monitoring technology has the capability to provide high frequency data streams on a range of team interaction and individual-level indicators. The product of this research aim is to develop 3 additional supporting components required for these dynamic data streams to be utilized as a countermeasure for teams to regulate teamwork interactions and psycho-social health. These components include the development of (a) specific metrics to assess interactions and reactions; (b) protocols to fuse the data streams and classify the effectiveness of team functioning; and (c) a system architecture designed to integrate the metrics and classification, and to direct feedback or other countermeasures when the system detects an anomaly in team functioning. Flexible options for distributing and displaying team status assessments and countermeasures need to be provided (e.g., individual team member, dyads, team leader, ground control).
Contact: Steve Kozlowski, PhD, Michigan State University, East Lansing, MI. firstname.lastname@example.org