Don’t overthink it: The paradoxical nature of expertise for the detection of errors in conceptual business process models - Boutin, Karl-David; Davis, Christopher; Hevner, Alan; Léger, Pierre-Majorique; Labonte-LeMoyne, Elise (Frontiers in Neuroscience, 2022)
Business process models are widely used artifacts in design activities to facilitate communication about business domains and processes. Despite being an extensively researched topic, some aspects of conceptual business modeling are yet to be fully explored and understood by academicians and practitioners alike. We study the attentional characteristics specific to experts and novices in a semantic and syntactic error detection task across 75 Business Process Model and Notation (BPMN) models. We find several intriguing results. Experts correctly identify more error-free models than novices, but also tend to find more false positive defects. Syntactic errors are diagnosed faster than semantic errors by both groups. Both groups spend more time on error-free models. Our findings regarding the ambiguous differences between experts and novices highlight the paradoxical nature of expertise and the need to further study how best to train business analysts to design and evaluate conceptual models.