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Don’t overthink it: The paradoxical nature of expertise for the detection of errors in conceptual business process models
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 practit...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9731113/ https://www.ncbi.nlm.nih.gov/pubmed/36507322 http://dx.doi.org/10.3389/fnins.2022.982764 |
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author | Boutin, Karl-David Davis, Christopher Hevner, Alan Léger, Pierre-Majorique Labonte-LeMoyne, Elise |
author_facet | Boutin, Karl-David Davis, Christopher Hevner, Alan Léger, Pierre-Majorique Labonte-LeMoyne, Elise |
author_sort | Boutin, Karl-David |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9731113 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97311132022-12-09 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 Front Neurosci Neuroscience 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. Frontiers Media S.A. 2022-11-24 /pmc/articles/PMC9731113/ /pubmed/36507322 http://dx.doi.org/10.3389/fnins.2022.982764 Text en Copyright © 2022 Boutin, Davis, Hevner, Léger and Labonte-LeMoyne. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Boutin, Karl-David Davis, Christopher Hevner, Alan Léger, Pierre-Majorique Labonte-LeMoyne, Elise Don’t overthink it: The paradoxical nature of expertise for the detection of errors in conceptual business process models |
title | Don’t overthink it: The paradoxical nature of expertise for the detection of errors in conceptual business process models |
title_full | Don’t overthink it: The paradoxical nature of expertise for the detection of errors in conceptual business process models |
title_fullStr | Don’t overthink it: The paradoxical nature of expertise for the detection of errors in conceptual business process models |
title_full_unstemmed | Don’t overthink it: The paradoxical nature of expertise for the detection of errors in conceptual business process models |
title_short | Don’t overthink it: The paradoxical nature of expertise for the detection of errors in conceptual business process models |
title_sort | don’t overthink it: the paradoxical nature of expertise for the detection of errors in conceptual business process models |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9731113/ https://www.ncbi.nlm.nih.gov/pubmed/36507322 http://dx.doi.org/10.3389/fnins.2022.982764 |
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