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Psychological safety and turn-taking have both been listed as key factors needed for collaboration in teams to emerge. Specifically, prior work has shown that increased communication in teams can lead to high psychological safety. Prior work on turn-taking as a measure of communication has mostly focused on its inclusivity in a team rather than its frequency. While the gender composition of the team can impact both participation as well as team psychological safety, there is a lack of research at the individual level. As such, this study provides the first attempt at connecting turn-taking, gender, and psychological safety through the analysis of members of fifteen engineering design student teams during the concept generation stage of their project. Specifically, we gathered video data to study how the number of turns and self- turns in a team impact psychological safety at both the individual and the team levels. We also examined how gender impacts participation and individual perceptions of psychological safety. The results found that turns and self-turns have a significant positive impact on an individual’s perception of the team’s psychological safety. However, no such relationship was found at the team level, indicating that there may be additional underlying factors in team level psychological safety. While we found that gender did not impact individual turn-taking, it did affect an individual’s perception of their psychological safety. These results provide quantitative evidence of the role of team communication on psychological safety.
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Conversational turn-taking as a stochastic process on networks.
Understanding why certain individuals work well (or poorly) together as a team is a key research focus in the psychological and behavioral sciences and a fundamental problem for team-based organizations. Nevertheless, we have a limited ability to predict the social and work-related dynamics that will emerge from a given combination of team members. In this work, we model vocal turn-taking behavior within conversations as a parametric stochastic process on a network composed of the team members. More precisely, we model the dynamic of exchanging the `speaker token' among team members as a random walk in a graph that is driven by both individual level features and the conversation history. We fit our model to conversational turn-taking data extracted from audio recordings of multinational student teams during undergraduate engineering design internships. Through this real-world data we validate the explanatory power of our model and we unveil statistically significant differences in speaking behaviors between team members of different nationalities.
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- Award ID(s):
- 1910117
- PAR ID:
- 10437232
- Date Published:
- Journal Name:
- Proceedings of the 56th Asilomar Conference on Signals, Systems, and Computers
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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