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Abstract Design teams are often asked to produce solutions of a certain type in response to design challenges. Depending on the circumstances, they may be tasked with generating a solution that clearly follows the given specifications and constraints of a problem (i.e., a Best Fit solution), or they may be encouraged to provide a higher risk solution that challenges those constraints, but offers other potential rewards (i.e., a Dark Horse solution). In the current research, we investigate: what happens when design teams are asked to generate solutions of both types at the same time? How does this request for dual and conflicting modes of thinking impact a team’s design solutions? In addition, as concept generation proceeds, are design teams able to discern which solution fits best in each category? Rarely, in design research, do we prompt design teams for “normal” designs or ask them to think about both types of solutions (boundary preserving and boundary challenging) at the same time. This leaves us with the additional question: can design teams tell the difference between Best Fit solutions and Dark Horse solutions? In this paper, we present the results of an exploratory study with 17 design teams from five different organizations. Each team was asked to generate both a Best Fit solution and a Dark Horse solution in response to the same design prompt. We analyzed these solutions using rubrics based on familiar design metrics (feasibility, usefulness, and novelty) to investigate their characteristics. Our assumption was that teams’ Dark Horse solutions would be more novel, less feasible, but equally useful when compared with their Best Fit solutions. Our analysis revealed statistically significant results showing that teams generally produced Best Fit solutions that were more useful (met client needs) than Dark Horse solutions, and Dark Horse solutions that were more novel than Best Fit solutions. When looking at each team individually, however, we found that Dark Horse concepts were not always more novel than Best Fit concepts for every team, despite the general trend in that direction. Some teams created equally novel Best Fit and Dark Horse solutions, and a few teams generated Best Fit solutions that were more novel than their Dark Horse solutions. In terms of feasibility, Best Fit and Dark Horse solutions did not show significant differences. These findings have implications for both design educators and design practitioners as they frame design prompts and tasks for their teams of interest. 

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. 

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. 

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. 

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.