Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/307507 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Business and Psychology [ISSN:] 1573-353X [Volume:] 38 [Issue:] 5 [Publisher:] Springer US [Place:] New York, NY [Year:] 2022 [Pages:] 1121-1137
Publisher: 
Springer US, New York, NY
Abstract: 
In interprofessional emergency response teams, firefighters, police, and paramedics must communicate efficiently (i.e., request the correct expert) to avoid life-threatening consequences. However, this communication is sometimes inefficient, for example, when a wrong expert is requested due to the lack of meta-knowledge. Team research has shown that meta-knowledge of "who knows what" improves team communication, so that members correctly request each other according to their expertise. Advances in technology, such as software agents holding meta-knowledge, can be used to improve team communication. In this paper, we analyze the effects of meta-knowledge on expert seeking, mistakes in requesting experts, and (adaptive) team performance by comparing manual and automated agent-based team communication. Using a control-center simulation, 360 students in 120 three-person teams had the interdependent task of handling emergencies in three phases. We manipulated meta-knowledge in advance, with 61 teams learning and 59 teams not learning other team members' expertise. Furthermore, in phases 1 and 3, team members had to communicate manually. In phase 2, communication was automated by a software agent taking over expert requesting. In line with our hypotheses, results showed that software agents can compensate the lack of meta-knowledge, so that there were no performance differences between teams with and without meta-knowledge with automated team communication. Our findings provide implications for research and practice that established team constructs should also be considered in human-automation teams.
Subjects: 
Transactive memory system
Meta-knowledge
Team communication
Software agent
Team performance
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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