Don’t let perfect be the enemy of good: how perfectionism influences human-centred designing engagement and communal design production in civil engineering

Perfectionism is a personality trait associated with a desire for flawlessness, high-standard expectations and criticism of the self and others. As engineering design seeks to address more wicked problems that move beyond technical considerations, it is possible that engineers with perfectionism may struggle to engage flexibly with complexity and more creativity-focused solutions. The present study seeks to understand perfectionism prevalence in an undergraduate cohort of civil engineers and the impact of this trait on complex design decisions and engagements that include social as well as technical considerations. 184 civil engineering students were involved in this study. We found that 74.5% of the engineers classify as perfectionists, with 68.5% of these perfectionists being maladaptive. Further, we examined how perfectionism associated with Communal Designs, a design approach that aims to meet physical community needs as well as more metaphysical, empathy-informed criteria. We found that although perfectionists were more likely to have higher scores of prosocialness and empathy, non-perfectionists were more likely to produce Communal Designs. This suggested an apparent intention-behaviour mismatch. Engineering students may have intended to but then failed to produce Communal Designs; this could also be explained via our finding that perfectionists tend to have higher social desirability scores. The results indicate that complex decision-making in engineering design cannot be separated from the mindsets and personalities of engineers. Strategies to mitigate the negative impact of perfectionism are discussed, including both supported exposure to open-ended, contextualised design, and the use of critical reflection. A regression model predictive of Communal Design production was also developed and discussed using engineering undergraduates’ personality characteristics’ scores as predictors.