Detecting Red Flag of Workplace Crime Using Mobile Data on Abnormal Usage Activities

dc.contributor.authorWang, Yuting
dc.contributor.authorXue, Ling
dc.contributor.authorLiu, Hefu
dc.contributor.authorCai, Zhao
dc.date.accessioned2021-12-24T18:19:07Z
dc.date.available2021-12-24T18:19:07Z
dc.date.issued2022-01-04
dc.description.abstractThe purpose of this paper is to investigate how to detect the workplace crime of organizational sales representatives (e.g., sales who work with external customers) through abnormal activities that can be traced by mobile devices and applications. The guardianship capability of organizations is considered as the moderator influencing the monitoring of abnormal usage activities calculated by deep learning. In this study, we conduct event history analysis on the occurrence of workplace crime utilizing a longitudinal panel data set, which comprises 197179 weekly observations in 3 years (2017-2019). Our finding provides evidence that the abnormal activity pattern is an effective signal for identifying workplace crimes. Furthermore, we illustrate how to design monitoring modes based on guardianship capability in order to maximize the effectiveness of mobile monitoring in reducing workplace crimes.
dc.format.extent9 pages
dc.identifier.doihttps://doi.org/10.24251/HICSS.2022.786
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/80126
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDigital Transformations of Business Operations
dc.subjectdeep learning
dc.subjectevent history analysis
dc.subjectmobile applications
dc.subjectroutine activity theory
dc.subjectworkplace crime
dc.titleDetecting Red Flag of Workplace Crime Using Mobile Data on Abnormal Usage Activities
dc.type.dcmitext

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