Communication in Transactive Memory Systems: A Review and Multidimensional Network Perspective

Yan, Bei; Hollingshead, Andrea B.; Alexander, Kristen S.; Cruz, Ignacio; Shaikh, Sonia Jawaid

doi:10.1177/1046496420967764

Cited by 29 publications

(29 citation statements)

References 99 publications

(202 reference statements)

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“…Previous research shows that successful teams use transactive memory systems for storing and retrieving their knowledge (Yan et al, 2021), and externalizing knowledge structures can be a first step toward establishing such systems.…”

Section: Discussionmentioning

confidence: 99%

Discipline differences in mental models: How mechanical engineers and automation engineers evaluate machine processes

Schmidt

Müller

2023

Hum Ftrs & Erg Mfg Svc

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Interdisciplinary collaboration frequently comes into play when existing problems cannot be solved by one discipline alone. However, the interlocking of contributions from different disciplines is by no means trivial. This exploratory study examines one foundation of successful teamwork, namely shared mental models. To this end, we compared the contents of mental models between members of different but interdependent disciplines who collaboratively solve knowledge‐intensive, creative tasks. Five automation and five mechanical engineers were recruited from a company that produces packaging machines. In semi‐structured interviews, participants reported their approach to evaluating the process behavior of a packaging machine, and their mental models were represented in concept maps. Quantitative analyses revealed that the maps of automation engineers were smaller than those of mechanical engineers. In qualitative analyses, the focus on different levels of abstraction and on contents from the two disciplines was examined. Automation engineers represented a large proportion of rather abstract machine functions, whereas mechanical engineers additionally represented the physical implementation of these functions. The disciplinary focus also differed in the sense that automation engineers mainly attended to automated machine processes, while mechanical engineers attended to both mechanical and automated processes. Overall, automation engineers' focus was narrower than that of mechanical engineers. We explain these results by considering typical tasks and reasoning processes in both disciplines, and discuss how shared mental models can aid the integration of different disciplinary perspectives, for instance, during Systems Engineering.

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Section: Discussionmentioning

confidence: 99%

Discipline differences in mental models: How mechanical engineers and automation engineers evaluate machine processes

Schmidt

Müller

2023

Hum Ftrs & Erg Mfg Svc

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“…Those software agents can be implemented to take over team communication (cf. Fiore & Wiltshire, 2016;Yan et al, 2021;real-life example: Unite Alarm Agent, AscomUK, 2015). At the highest level of automated team communication, software agents take over team communication by requesting experts and answering requests from other team members.…”

Section: Effects Of Automated Team Communication On Team Processes An...mentioning

confidence: 99%

“…Previous research has shown that teams with a transactive memory system perform better than teams without a transactive memory system (e.g., Austin, 2003;Hinsz et al, 1997;Lewis, 2003Lewis, , 2004. As a transactive memory system leads to high interdependence among team members, they must communicate with each other by requesting other experts in their team (Yan et al, 2021). For successful team communication (i.e., less information seeking about the experts and few mistakes in requesting experts), the importance of a meta-knowledge as a directory of "who knows what" that stores knowledge about the expertise of other team members has been emphasized Faraj & Sproull, 2000;Wegner, 1987Wegner, , 1995.…”

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confidence: 99%

How Software Agents Can Help to Coordinate Emergency Response Teams: Adaptive Team Performance Comparing Manual and Automated Team Communication

et al. 2022

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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.

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“…Measuring the emotions of team members and, at the same time, the information elaboration of their teams seems promising because team members in positive rather than negative moods are more willing and open to share the information and expertise that only they possess (e.g., Dietz et al, 2017; Pfaff, 2012; Pillay et al, 2020). These informational exchanges then also facilitate the development of TMS (Yan et al, 2021).…”

Section: Future Research Agendamentioning

confidence: 99%

A Multilevel Review of Curvilinear Effects on the Creative Work of Teams

Razinskas

2022

Small Group Research

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Their positive potential often diminishes or even turns negative when antecedents of creativity are taken too far. Despite empirical evidence supporting such curvilinear effects on important outcomes of creative work at the individual and team levels, their theorizing remains rather incomplete, with more attention being paid to explaining the curves’ upward rather than downward slopes. By developing a multilevel antecedent-benefit-cost (ABC) framework that synthesizes 120 quantitative-empirical studies on curvilinear effects, this review guides creativity and innovation literature toward conceptual clarity and methodological precision across levels. This is important because the cost-related mechanisms of certain antecedents are still not well understood.

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Communication in Transactive Memory Systems: A Review and Multidimensional Network Perspective

Cited by 29 publications

References 99 publications

Discipline differences in mental models: How mechanical engineers and automation engineers evaluate machine processes

Discipline differences in mental models: How mechanical engineers and automation engineers evaluate machine processes

How Software Agents Can Help to Coordinate Emergency Response Teams: Adaptive Team Performance Comparing Manual and Automated Team Communication

A Multilevel Review of Curvilinear Effects on the Creative Work of Teams

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