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The flow of creative ideas throughout the engineering design process is essential for innovation. However, few studies have examined how individual traits affect problem-solving behaviors in an engineering design setting. Understanding these behaviors will enable us to guide individuals during the idea generation and concept screening phases of the engineering design process and help support the flow of creative ideas through this process. As a first step towards understanding these behaviors, we conducted an exploratory study with 19 undergraduate engineering students to examine the impact of individual traits, using the Preferences for Creativity Scale (PCS) and Kirton’s Adaption-Innovation inventory (KAI), on the creativity of the ideas generated and selected for an engineering design task. The ideas were rated for their creativity, quality, and originality using Amabile’s consensual assessment technique. Our results show that the PCS was able to predict students’ propensity for creative concept screening, accounting for 74% of the variation in the model. Specifically, team centrality and influence and risk tolerance significantly contributed to the model. However, PCS was unable to predict idea generation abilities. On the other hand, cognitive style, as measured by KAI, predicted the generation of creative and original ideas, as well as one’s propensity for quality concept screening, although the effect sizes were small. Our results provide insights into individual factors impacting undergraduate engineering students’ idea generation and selection.

Engineering organizations around the word are increasingly becoming team-based due to a teams’ ability to generate solutions to complex problems. This is thought to be attributed to the “wisdom of the collectives” where teams outperform the sum of their individual members. Despite heavy emphasis of teamwork in engineering design, our understanding of how to cultivate teamwork skills is poorly understood. This is due in part to the fact that research on engineering design teams is often based on ‘snap-shot’ ethnographic methods that do not account for the dynamic changes that happen over the course of a project. Research that does account for these interactions are hindered by the human processing required to code and analyze the immense amounts of video data acquired through team studies. Recent technological advances in the way of sociometric badges provide a potential avenue to explore intricate communication patterns and help researchers identify when and how team interventions should be developed. However, these technologies have not been validated for use in the dynamic context of engineering design teams. Thus, the current study was developed to examine sociometric badges for their accuracy, precision, and ability to determine speech dominance in engineering design teams. Our results show that sociometric badges can accurately capture total speaking time. However, the results also show that environmental conditions can impact their accuracy. In addition, we found that sociometric badges did not capture speech precisely and often overestimated when a team member was speaking. These results support the use of sociometric badges for capturing high level team interactions (e.g. participation and total speaking time) in engineering design research.