Evidence map›Paper›PMID 36507322›Full record

ArticleFrontiers in neuroscience2022

Don't overthink it: The paradoxical nature of expertise for the detection of errors in conceptual business process models.

Karl-David Boutin, Christopher Davis, Alan Hevner, Pierre-Majorique Léger, Elise Labonte-LeMoyne

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.0field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 2 countries.

Karl-David BoutinDepartment of Information Technologies, HEC Montréal, Montreal, QC, Canada.
Christopher DavisSchool of Information Systems and Management, University of South Florida, Tampa, FL, United States.
Alan HevnerSchool of Information Systems and Management, University of South Florida, Tampa, FL, United States.
Pierre-Majorique LégerDepartment of Information Technologies, HEC Montréal, Montreal, QC, Canada.
Elise Labonte-LeMoyneDepartment of Information Technologies, HEC Montréal, Montreal, QC, Canada.
HEC Montréal · CAUniversity of South Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

attentional characteristicsBPMNbusiness analystsbusiness process managementconceptual modelingexpertiseeye trackingprocess modeling

Identifiers

PMID36507322
PMCPMC9731113
OpenAlexW4309861989

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.