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 actually tell the difference between a Best Fit solution and a Dark Horse solution?

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. Regarding the feasibility rubric, Best Fit and Dark Horse solutions did not show significant differences in their scores. Ultimately, then, some of our initial assumptions were confirmed, while others were not. These findings have implications for both design educators and practitioners as they frame design prompts and tasks for their teams of interest.


Best Fits and Dark Horses: Can Design Teams Tell the Difference?

Division / Sponsors: 
Design Engineering Division and Computers & Information In Engineering Division


Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2020/volume/83907

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