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

Yuting Wang; Hefu Liu; Ling Xue; Zhao Cai

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

Yuting Wang
, Hefu Liu
, Ling Xue
, Zhao Cai

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022
Editors	Tung X. Bui
Publisher	IEEE Computer Society
Pages	6492-6500
Number of pages	9
ISBN (Electronic)	9780998133157
Publication status	Published - 2022
Event	55th Annual Hawaii International Conference on System Sciences, HICSS 2022 - Virtual, Online, United States Duration: 3 Jan 2022 → 7 Jan 2022

Publication series

Name	Proceedings of the Annual Hawaii International Conference on System Sciences
Volume	2022-January
ISSN (Print)	1530-1605

Conference

Conference	55th Annual Hawaii International Conference on System Sciences, HICSS 2022
Country/Territory	United States
City	Virtual, Online
Period	3/01/22 → 7/01/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

ASJC Scopus subject areas

General Engineering

Cite this

Wang, Y., Liu, H., Xue, L., & Cai, Z. (2022). Detecting Red Flag of Workplace Crime Using Mobile Data on Abnormal Usage Activities. In T. X. Bui (Ed.), Proceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022 (pp. 6492-6500). (Proceedings of the Annual Hawaii International Conference on System Sciences; Vol. 2022-January). IEEE Computer Society.

Wang, Yuting ; Liu, Hefu ; Xue, Ling et al. / Detecting Red Flag of Workplace Crime Using Mobile Data on Abnormal Usage Activities. Proceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022. editor / Tung X. Bui. IEEE Computer Society, 2022. pp. 6492-6500 (Proceedings of the Annual Hawaii International Conference on System Sciences).

@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",

}

Wang, Y, Liu, H, Xue, L & Cai, Z 2022, Detecting Red Flag of Workplace Crime Using Mobile Data on Abnormal Usage Activities. in TX Bui (ed.), Proceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022. Proceedings of the Annual Hawaii International Conference on System Sciences, vol. 2022-January, IEEE Computer Society, pp. 6492-6500, 55th Annual Hawaii International Conference on System Sciences, HICSS 2022, Virtual, Online, United States, 3/01/22.

Detecting Red Flag of Workplace Crime Using Mobile Data on Abnormal Usage Activities. / Wang, Yuting; Liu, Hefu; Xue, Ling et al.
Proceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022. ed. / Tung X. Bui. IEEE Computer Society, 2022. p. 6492-6500 (Proceedings of the Annual Hawaii International Conference on System Sciences; Vol. 2022-January).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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T1 - Detecting Red Flag of Workplace Crime Using Mobile Data on Abnormal Usage Activities

AU - Wang, Yuting

AU - Liu, Hefu

AU - Xue, Ling

AU - Cai, Zhao

N1 - 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: © 2022 IEEE Computer Society. All rights reserved.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:85152229615

T3 - Proceedings of the Annual Hawaii International Conference on System Sciences

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BT - Proceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022

A2 - Bui, Tung X.

PB - IEEE Computer Society

T2 - 55th Annual Hawaii International Conference on System Sciences, HICSS 2022

Y2 - 3 January 2022 through 7 January 2022

ER -

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

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

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