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What’s That Supposed to Mean? Capturing Micro-Behaviors in TeamsFuture long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning.

To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement.

Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into micro-aggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning.

Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of micro-behaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors.

Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.
Document ID
20230006082
Acquisition Source
Johnson Space Center
Document Type
Presentation
Authors
Sydney Begerowski ORCID
(KBR (United States) Houston, Texas, United States)
Alaa Khader
(JES Tech (United States) Houston, Texas, United States)
Projna Paromita
(Texas A&M University College Station, Texas, United States)
Theodora Chaspari
(Texas A&M University College Station, Texas, United States)
Suzanne T Bell
(Johnson Space Center Houston, Texas, United States)
Date Acquired
April 19, 2023
Publication Date
April 21, 2023
Subject Category
Behavioral Sciences
Meeting Information
Meeting: 38th Annual Society for Industrial and Organizational Psychology (SIOP) Conference
Location: Boston, MA
Country: US
Start Date: April 19, 2023
End Date: April 22, 2023
Sponsors: Society for Industrial and Organizational Psychology
Funding Number(s)
WBS: 344494.01.04.10
CONTRACT_GRANT: 80NSSC22K0775
CONTRACT_GRANT: NNX16AQ48G
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Technical Review
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