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In this pilot study, we used the Interaction Dynamics Notation (IDN), originally designed for use with engineering design teams, to explore the dynamic interactions of five NSF I-Corps™ teams engaged in a simple design activity. Our aim was to relate these interaction data to selected cognitive characteristics of the team members, as well as team design outcomes and individual perceptions related to the experience. The individual cognitive characteristics we assessed focused on cognitive style, as measured by the Kirton Adaption-Innovation inventory (KAI), while team outcomes included the novelty, usefulness, and feasibility of each team’s design solutions, as well as their success within and beyond the NSF I-Corps™ program. Our findings show that the Interaction Dynamics Notation (IDN) can be readily extended to the study of entrepreneurial teams, with important insights gained from the combined study of interaction dynamics, individual cognitive characteristics as measured by KAI, and team outcomes. The results of this study demonstrate the feasibility and value of this approach for investigating the dynamic interactions of NSF I-Corps™ teams, as well as product-focused design teams in general.
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System-level investigation of cognitive adaptation in incident management.
My dissertation research to date has focused on understanding how incident management teams (IMTs), hastily formed multidisciplinary multiteam systems, cognitively function together as adaptive, joint cognitive systems-of-systems embedded in complex sociotechnical systems. Catastrophic disasters such as Hurricane Harvey highlight the importance of collective efforts for adaptive incident management. Team cognition has emerged as a coordinating mechanism in safety-critical disciplines; however, little is known about cognition in IMTs. Through a scoping review of existing definitions, I proposed an expanded definition that deliberately takes into account IMT’s unique contextual characteristics, based on three premises: cognition in IMTs (1) manifests as interactions among humans, teams, and technologies at multiple levels of multiteam systems, (2) aims to achieve the system-level cognitive goals of perceiving (P), diagnosing, (D), and adapting (A) to information, and (3) serves as an open communication platform for adaptive coordination.Then, I operationalized our proposed definition in a simulated environment as an initial attempt to model IMTs’ system-level cognition. Based on several observations of IMTs’ naturalistic interactive behaviors under different types of disaster scenarios, I proposed a model that can capture how IMTs as joint cognitive systems (or systems-of-systems) perceive (P), diagnose, (D), and adapt (A) to information, i.e., perceive, diagnose, adapt (P, D, A) model. With an emphasis on system-level cognitive goals that applies to multiple units of analysis (e.g., individuals, dyads, teams, and multiteam systems), I could gain an understanding of system-level cognitive adaptation in incident management. Using the P, D, A model as a base platform, I expect to discuss resilience as cognitive adaptation processes along with its implications on human information processing and joint cognitive systems theories.I became a Ph.D. candidate after successfully proposing my dissertation research in last June. After completing data collection and processing, I am currently working on data analysis and manuscript preparation. As a part of NSF-funded project (NSF EArly-concept Grant for Exploratory Research, #1724676), I believe my dissertation work has a potential to practically impact scenario-based training practices of incident management, and thereby lead to a more rapid and better coordinated decision-making in saving lives and infrastructures.
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- Award ID(s):
- 1724676
- PAR ID:
- 10135711
- Date Published:
- Journal Name:
- Resilience Engineering Association (REA) Symposium
- Volume:
- 2019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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