Interaction dynamics are considered to be key characteristics of complex adaptive systems (CAS). Taking a CAS approach, this study examines how three team interaction patterns affect team effectiveness. Specifically, we analyze recurring, heterogeneous, and participative patterns of team interaction in routine and nonroutine team-task contexts. Fine-grained coding of video-based footage plus nonlinear dynamical systems (NDS) statistics are used to identify the interaction patterns in a sample of 96 real-life teams, comprising 1,395 team members. We establish that recurring patterns of team interaction reduce perceived team information sharing and, in turn, team effectiveness and that these harmful effects are more pronounced in teams doing nonroutine work than in those engaged in routine work. Participative team interaction was found to be positively related to a high level of perceived team information sharing and effectiveness. Heterogeneous team interaction was not associated with perceived team information sharing and effectiveness. Post hoc analyses, in which the behavioral content of the interaction patterns of the 15 most effective and least effective teams is compared, revealed primarily task-directed patterns in the most effective teams. We offer practical recommendations for team development and call for more CAS research on the communicative behaviors within teams of knowledge workers.
The presented empirical study demonstrates that the predictive validity of Bass' “transformational‐transactional” model of leadership can be enhanced by incorporating certain aspects of the older Ohio State “initiating structure‐consideration” model of leadership. A precise, fine‐grained video‐based method shows that “initiating structure” behaviors (e.g., directing, informing, structuring) explained the variance in leader and team effectiveness better than “transactional behavior.” Thus, a refined version of Bass' augmentation thesis is supported: initiating structure behaviors (and not transactional behaviors, as originally posed) plus transformational leader behaviors are associated with high leader effectiveness. Another moderation effect of transformational leadership is established: between management‐by‐exception active and team effectiveness. The resulting expanded version of the transformational–transactional model calls for further video‐based research of effective (team) leadership behaviors.
In healthcare, action teams are carrying out complex medical procedures in intense and unpredictable situations to save lives. Previous research has shown that efficient communication, high-quality coordination, and coping with stress are particularly essential for high performance. However, precisely and objectively capturing these team interactions during stressful moments remains a challenge. In this study, we used a multimodal design to capture the structure and content of team interactions of medical teams at moments of high arousal during a simulated crisis situation. Sociometric badges were used to measure the structure of team interactions, including speaking time, overlapping speech and conversational imbalance. Video coding was used to reveal the content of the team interactions. Furthermore, the Empatica E4 was used to unobtrusively measure the team leader's skin conductance to identify moments of high arousal. In total, 21 four-person teams of technical medicine students in the Netherlands were monitored in a simulation environment while they diagnosed and managed a patient with cardiac arrest. Outcomes of this exploratory study revealed that more effective teams showed greater conversational imbalance than less effective teams, but during moments of high arousal the opposite was found. Also, a number of differences were found for the content of team interaction. Combining sensor technology with traditional measures can enhance our understanding of the complex interaction processes underlying effective team performance, but technological advances together with more knowledge about the simultaneous application of these methods are needed to tap into the full potential of wearable sensor technology in team research.
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