Enhancing Team Mental Model Measurement with Performance Appraisal Practices

2000 ◽  
Vol 3 (4) ◽  
pp. 307-322 ◽  
Author(s):  
Sheila Simsarian Webber ◽  
Gilad Chen ◽  
Stephanie C. Payne ◽  
Sean M. Marsh ◽  
Stephen J. Zaccaro
2012 ◽  
Vol 97 (4) ◽  
pp. 825-841 ◽  
Author(s):  
David M. Fisher ◽  
Suzanne T. Bell ◽  
Erich C. Dierdorff ◽  
James A. Belohlav

1992 ◽  
Vol 36 (16) ◽  
pp. 1195-1199 ◽  
Author(s):  
Anna L. Rowe ◽  
Nancy J. Cooke ◽  
Kelly J. Neville ◽  
Chris W. Schacherer

Although use of the mental model construct has proliferated in recent research, the construct lacks a clear definition and an agreed upon method of measurement. Furthermore, the reliability and validity of the different measurement techniques in use have not been established, thereby making generalizations across studies of mental models difficult. The purpose of the current project was to assess several methods of measuring mental models in terms of their reliability/stability over time. Subjects” mental models of the automobile engine system were elicited on two occasions separated by one week, using seven different knowledge elicitation techniques. Subjects” level of experience was also measured to allow comparisons between experts and novices. The results indicate that each of the measurement techniques tended to be reliable for both experts and novices. However, reliability tended to be greater for experts than novices. Additionally, experts tended to agree with each other more than did the novices. Some evidence also indicated that the results from the similarity ratings and subsequent Pathfinder analysis converged with those from the structured interviews.


Author(s):  
Hayward P. Andres

The purpose of this study was to explore team cognition as a multidimensional activity comprised of team learning, team reflexivity, and team mental model during project teamwork. A laboratory experiment was conducted to examine the effects of two different modes of collaboration – face-to-face and technology-mediated collaboration on team cognition and its subsequent impact on task outcomes. Team cognition was represented as a second-order construct comprised of three first-order dimensions. A direct-observation rating scale used to derive measures of the first-order dimensions was shown to have strong psychometric properties. The partial least squares method was used to test a structural equation model where the second-order construct was presented as a mediator between collaboration mode and task outcomes (productivity and interaction quality). As hypothesized, team cognition significantly influenced productivity and interaction quality outcomes. Further, collaboration mode significantly improved team cognition through its specific effects on the team learning, team reflexivity, and team mental model development. The main contribution of the study lies in its finding that team cognition can be viewed as a hierarchical construct that accounts for distinct yet cognition-related behaviors. This finding offers an extension to current related research models and identifies behavioral indicators that can be monitored by project managers in developing prescriptive measures aimed at promoting project success.


2022 ◽  
Vol 6 (GROUP) ◽  
pp. 1-29
Author(s):  
Beau G. Schelble ◽  
Christopher Flathmann ◽  
Nathan J. McNeese ◽  
Guo Freeman ◽  
Rohit Mallick

An emerging research agenda in Computer-Supported Cooperative Work focuses on human-agent teaming and AI agent's roles and effects in modern teamwork. In particular, one understudied key question centers around the construct of team cognition within human-agent teams. This study explores the unique nature of team dynamics in human-agent teams compared to human-human teams and the impact of team composition on perceived team cognition, team performance, and trust. In doing so, a mixed-method approach, including three team composition conditions (all human, human-human-agent, human-agent-agent), completed the team simulation NeoCITIES and completed shared mental model, trust, and perception measures. Results found that human-agent teams are similar to human-only teams in the iterative development of team cognition and the importance of communication to accelerating its development; however, human-agent teams are different in that action-related communication and explicitly shared goals are beneficial to developing team cognition. Additionally, human-agent teams trusted agent teammates less when working with only agents and no other humans, perceived less team cognition with agent teammates than human ones, and had significantly inconsistent levels of team mental model similarity when compared to human-only teams. This study contributes to Computer-Supported Cooperative Work in three significant ways: 1) advancing the existing research on human-agent teaming by shedding light on the relationship between humans and agents operating in collaborative environments, 2) characterizing team cognition development in human-agent teams; and 3) advancing real-world design recommendations that promote human-centered teaming agents and better integrate the two.


2021 ◽  
pp. 105960112110232
Author(s):  
Sjir Uitdewilligen ◽  
Mary J. Waller ◽  
Robert A. Roe ◽  
Peter Bollen

Drawing on the concept of requisite complexity, we propose that mental model complexity is crucial for teams to thrive in dynamic complex environments. Using a longitudinal research design, we examined the influence of team mental model complexity on team information search and performance trajectories in a sample of 64 teams competing in a business strategy simulation over time. We found that team information search positively influences performance growth over time. More specifically, and consistent with requisite complexity, we found that mental model complexity positively influences both performance growth and information search over time, above and beyond the effects of mental model similarity and accuracy.


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