@inproceedings{c6ac9fe6b2814ea8aa51332870476f71,
title = "Detecting Red Flag of Workplace Crime Using Mobile Data on Abnormal Usage Activities",
abstract = "The 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.",
author = "Yuting Wang and Hefu Liu and Ling Xue and Zhao Cai",
note = "Funding Information: This work was supported by the National Key R&D Program of China (2020AAA0103804) and the National Natural Science Foundation of China (NSFC: 71971202). Publisher Copyright: {\textcopyright} 2022 IEEE Computer Society. All rights reserved.; 55th Annual Hawaii International Conference on System Sciences, HICSS 2022 ; Conference date: 03-01-2022 Through 07-01-2022",
year = "2022",
language = "English",
series = "Proceedings of the Annual Hawaii International Conference on System Sciences",
publisher = "IEEE Computer Society",
pages = "6492--6500",
editor = "Bui, {Tung X.}",
booktitle = "Proceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022",
address = "United States",
}
