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1. Design whodunit: The relationship between individual characteristics and interaction behaviors in design concept generation

   Sonalkar, N. (August 2017, Proceedings of the ... ASME Design Engineering Technical Conferences)

   This paper investigates the relationship between interaction behaviors and the cognitive characteristics of participating individuals in engineering design teams engaged in concept generation. Individual characteristics were measured using the Kirton Adaption-Innovation inventory (KAI), which assesses an individual’s cognitive preference for structure in seeking and responding to change. Team interactions were measured using the Interaction Dynamics Notation (IDN), which allows interaction behaviors to be quantitatively analyzed. A correlation analysis revealed statistically significant correlations between individual characteristics and specific interaction behaviors and ideation utterances. An interaction sequence analysis of the team data also revealed specific interaction sequences associated with greater probabilities of idea occurrence within the team. These findings serve as a first step towards building a cognitive-behavioral model of engineering design team performance. 

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2. Design whodunit: The relationship between individual characteristics and interaction behaviors in design concept generation.

   Sonalkar, N. (August 2017, Proc. of the ASME 2017 International Design Engineering Technical Conferences (IDETC))

   This paper investigates the relationship between interaction behaviors and the cognitive characteristics of participating individuals in engineering design teams engaged in concept generation. Individual characteristics were measured using the Kirton Adaption-Innovation inventory (KAI), which assesses an individual’s cognitive preference for structure in seeking and responding to change. Team interactions were measured using the Interaction Dynamics Notation (IDN), which allows interaction behaviors to be quantitatively analyzed. A correlation analysis revealed statistically significant correlations between individual characteristics and specific interaction behaviors and ideation utterances. An interaction sequence analysis of the team data also revealed specific interaction sequences associated with greater probabilities of idea occurrence within the team. These findings serve as a first step towards building a cognitive-behavioral model of engineering design team performance. 

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3. Exploring the dynamic interactions and cognitive characteristics of NSF Innovation Corps (I-Corps™) teams

   Jablokow, K. (June 2018, Proc. of the ASEE 2018 Annual Conference & Exposition)

   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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4. A Game-Theoretic Research Platform for Team-based Design Decisions under Competition

   https://doi.org/10.1017/pds.2025.10024  

   Li, Xiang; Chen, Siyu; Bayrak, Alparslan Emrah; Sha, Zhenghui (August 2025, Proceedings of the Design Society)

   Design decision-making under competition is a critical challenge in real-world engineering design. These challenges are compounded by bounded rationality, where cognitive limitations and imperfect information influence decision-making strategies. To address these issues, we develop a game-theoretic research platform to investigate team-based design under competition. This platform abstracts and simulates real-world competitive design scenarios through controlled experiments. It features a user-friendly interface to collect behavioral data, which supports the analysis of team and individual strategies. Additionally, we validated the platform through a pilot study, demonstrating its ability to capture realistic design features and generate meaningful insights into competitive design behaviors. 

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5. Impact of Student/Team Characteristics on Design Project Outcomes in Senior Design Courses

   Godbole, Hrushikesh; DeBartolo, Elizabeth; Takai, Shun; Esterman, Marcos (June 2023, ASEE Annual Conference proceedings)

   Ability to effectively work in teams is one of the desired outcomes of engineering and engineering technology programs. Unfortunately, working in teams is still challenging for many students. Rather than contributing to team projects, some students resort to social loafing. Social loafing tends to destroy both teamwork performance and individual learning, especially in solving ill-structured problems, such as design. Furthermore, a bad experience on a past team is a significant concern as it could generate negative feelings toward future team projects. Formation of collaborative teams is a critical first step in team-project-based design courses as team composition directly affects not only teamwork processes and outcomes, but also teamwork skills and experience. This NSF-IUSE sponsored project aims to enhance students’ teamwork experiences and teamwork learning through 1) understanding how to form better student design teams and 2) identifying exercises that will effectively improve team member collaboration. We do this by comparing student characteristics and design task characteristics with the quality of the design team outcome, and examining the resulting correlations. Student characteristics cover six categories: 1) background information, 2) work structure preferences, 3) personality, 4) ability, 5) motivation, and 6) attitude. Task characteristics and design team outcomes are characterized using the Creative Product Semantic Scale. In this article, we present correlations between student/team characteristics and design project outcome, and correlations between task characteristics and design project outcome for 2020-2021 senior design teams at two institutions. For both institutions, we will present correlations between individual student characteristics and team outcome. For one institution, we will also present correlation between team-level characteristics and team outcomes. 

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