Features and forecasts: Defining and predicting team coordination breakdowns

Kyana van Eijndhoven, Travis Wiltshire, Elwira A. Halgas, J.M.P. Gevers

Research output: Working paperScientific


Team coordination dynamics describe the self-organized behavior of team members’ interactivity to achieve a larger, continuously changing common goal. The common goal changes based on dynamically shifting demands and environments (Demir et al., 2018; Gorman & Amazeen, 2010; Kelso, 1994). A team coordination breakdown occurs when this self-organized behavior fails, resulting in a temporarily diminished ability to function effectively as a team affecting team performance (Bearman et al., 2010). Previous studies have shown that by examining team coordination dynamics, we can identify breakdowns in team coordination, which disrupt effective team functioning (Amazeen, 2018; Eijndhoven et al., 2022; Likens et al., 2014). By identifying team coordination breakdowns (TCBs), ineffective team functioning can be located in time. If TCBs can be identified computationally feedback on suboptimal team coordination can be delivered in real-time (Wiltshire et al., 2022). Especially during crisis situations, in which effective team functioning is challenged by rapidly changing demands and environments, such feedback can be of crucial importance. Eijndhoven et al. (2022) were able to computationally identify up to 96% of TCBs, albeit that the precision of the approaches was found to be lower (i.e., below 30%; . To improve this previous work, further research is needed to examine the underlying structure of team coordination dynamics data as it relates to TCBs. To this end, we aim to study what features (e.g., sample entropy, mean change, autocorrelation) are relevant for more precisely distinguishing between coordination dynamics that reflect TCBs and those that do not. In addition, we aim to examine the extent to which we can use these features to forecast TCBs, and to interpret the patterns in team coordination that precede or occur during TCBs. Besides the computational identification of TCBs in our previous work, we also conducted manual identification. We coded TCBs as time-localized instances of inefficient team functioning that affected team performance, based on audiovisual recordings of the experiment. For the current study, we further examine these instances, by looking beyond the team performance during these specific instances, to the effect of TCBs on the global team performance. More specifically, we aim to investigate the relationship between the total duration of TCBs and the amount of time it takes teams to achieve the main goal (with longer amounts of time indicating poorer performance). Such an investigation will help to better understand the effects of TCBs on team performance. Ultimately, our current study will contribute to deepening our understanding of how team coordination dynamics underly TCBs, and pave the way towards enabling the provision of feedback to support effective team functioning, and subsequently team performance.
Original languageEnglish
Publication statusUnpublished - 2022


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