Naturalistic observations of multiteam interaction networks: Implications for cognition in crisis management teams

Abstract

Interaction has been recognised as an essential lens to understand how cognition is formed in a complex adaptive team such as a multidisciplinary crisis management team (CMT). However, little is known about how interactions within and across CMTs give rise to the multi-team system’s overall cognitive functioning, which is essential to avoid breakdowns in coordination. To address this gap, we characterise and compare the component CMTs’ role-as-intended (RAI) and role-as-observed (RAO) in adapting to the complexity of managing informational needs. To characterise RAI, we conducted semi-structured interviews with subject matter experts and then made a qualitative synthesis using a thematic analysis method. To characterise RAO, we observed multiteam interaction networks in real-time at a simulated training environment and then analysed the component CMTs’ relative importance using node centrality measures. The resulting inconsistencies between RAI and RAO imply the need to investigate cognition in multiple CMTs through the lens of interaction.

Practitioner summary: When a disaster occurs, multidisciplinary CMTs are expected to serve their roles as described in written or verbal guidelines. However, according to our naturalistic observations of multiteam interaction networks, such descriptions may be (necessary but) insufficient for designing, training, and evaluating CMTs in the complexity of managing informational needs together.

Keywords:

• Crisis management
• team cognition
• multiteam
• network analysis
• adaptive coordination

Acknowledgments

The authors appreciate the Emergency Operations Training Center at the Texas A&M Engineering Extension Service (EOTC at TEEX, e.g. Dr. Jason B. Moats, Jory L. Grassinger, Mike Gibler, and Ronnie Taylor) for their assistance in facilitating data collection. The authors thank the members and alumni of the Applied Cognitive Ergonomics laboratory (ACE-lab, directed by Dr. Farzan Sasangohar) and the Research on the Interaction between Humans and Machines laboratory (RIHM-lab, directed by Dr. S. Camille Peres) at Texas A&M University for their help in data collection and processing (e.g. Alec Smith, Daniel Medrano, Elaine Schneider, Justin Wood, Katherine Renter, Karim Zahed, Nicolas George, Timothy J. Neville, Trevor Hennington, and Vu Hoang Le) as well as manuscript editing and proofreading (Jacob M. Kolman).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Infrastructure Management and Extreme Events program of the National Science Foundation under the EArly-concept Grant for Exploration Research [NSF EAGER #1724676]. This work was also supported in part by a Texas A&M Dissertation Fellowship to the first author and an internal award from Mary Kay O'Connor Process Safety Center to the fourth author.