The impact of instrumental attribution in AI-enabled monitoring on counterproductive work behavior

AI-enabled monitoring tools are theoretically expected to suppress unethical employee behavior. However, in practice, employees may perceive such monitoring as being driven by leaders' instrumental motives, primarily focused on personal performance evaluation and self-interest. This perception...

Full description

Saved in:
Bibliographic Details
Main Author: ZHANG, Qiang
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
Online Access:https://ink.library.smu.edu.sg/etd_coll/655
https://ink.library.smu.edu.sg/context/etd_coll/article/1653/viewcontent/博_学位论__GPBF_AY2024_PhD_ZhangQiang.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.etd_coll-1653
record_format dspace
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic AI-enabled Monitoring
Instrumental Attribution
Unethical Employee Behavior
Employee Turnover
Job Insecurity
Moral Disengagement
Ethical Management Control
Accounting
Artificial Intelligence and Robotics
Finance and Financial Management
spellingShingle AI-enabled Monitoring
Instrumental Attribution
Unethical Employee Behavior
Employee Turnover
Job Insecurity
Moral Disengagement
Ethical Management Control
Accounting
Artificial Intelligence and Robotics
Finance and Financial Management
ZHANG, Qiang
The impact of instrumental attribution in AI-enabled monitoring on counterproductive work behavior
description AI-enabled monitoring tools are theoretically expected to suppress unethical employee behavior. However, in practice, employees may perceive such monitoring as being driven by leaders' instrumental motives, primarily focused on personal performance evaluation and self-interest. This perception can foster feelings of job insecurity and moral disengagement, ultimately leading to counterproductive work behavior (CWB), which includes unethical employee behavior and turnover. These outcomes may undermine the intended effectiveness of AI-enabled monitoring tools. This study aims to explore the impact of Instrumental Attribution in AIenabled Monitoring (IAAIM) on CWB, specifically focusing on unethical employee behavior and turnover, through both theoretical and empirical lenses. Drawing on attribution theory, the study first reviews existing research on the psychological and behavioral consequences of AI-enabled monitoring, the drivers of unethical employee behavior and turnover, and the roles of job insecurity, moral disengagement, and ethical management control. A theoretical framework is then systematically constructed to explain how IAAIM influence CWB. Subsequently, an empirical analysis is conducted using survey data from 72 work teams comprising 558 sales representatives within the marketing department of a large real estate company in China. This analysis provides empirical evidence on the influence of IAAIM on unethical employee behavior and turnover, examining the mediating roles of job insecurity and moral disengagement, as well as the moderating effect of ethical management control. The findings reveal the following: First, IAAIM positively affects job insecurity, though the impact of job insecurity on unethical employee behavior and turnover is not significant. Second, IAAIM positively influences moral disengagement. However, the mediating effect of IAAIM on employee turnover through moral disengagement is not significant. Third, ethical management control mitigates the relationship between IAAIM and job insecurity, and negatively moderates the indirect effect of IAAIM on unethical employee behavior through job insecurity. This suggests that stronger ethical management control weakens this indirect effect. However, this moderating effect is not significant concerning the indirect effect on employee turnover. Fourth, ethical management control also reduces the relationship between IAAIM and moral disengagement, and negatively moderates the indirect effect of IAAIM on unethical employee behavior through moral disengagement, indicating that more effective ethical management control diminishes this indirect effect. Again, this moderating effect is not significant concerning the indirect effect on employee turnover. Theoretically, this study elucidates the internal mechanisms through which IAAIM influences CWB by increasing job insecurity and moral disengagement, and clarifies the moderating role of ethical management control. This research contributes to the literature on the consequences of AI-enabled monitoring and the driving factors of unethical employee behavior and turnover. Practically, it offers valuable insights for enterprises to effectively utilize AI in controlling and preventing unethical employee behavior and turnover, thereby continuously improving human resource management.
format text
author ZHANG, Qiang
author_facet ZHANG, Qiang
author_sort ZHANG, Qiang
title The impact of instrumental attribution in AI-enabled monitoring on counterproductive work behavior
title_short The impact of instrumental attribution in AI-enabled monitoring on counterproductive work behavior
title_full The impact of instrumental attribution in AI-enabled monitoring on counterproductive work behavior
title_fullStr The impact of instrumental attribution in AI-enabled monitoring on counterproductive work behavior
title_full_unstemmed The impact of instrumental attribution in AI-enabled monitoring on counterproductive work behavior
title_sort impact of instrumental attribution in ai-enabled monitoring on counterproductive work behavior
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/etd_coll/655
https://ink.library.smu.edu.sg/context/etd_coll/article/1653/viewcontent/博_学位论__GPBF_AY2024_PhD_ZhangQiang.pdf
_version_ 1827070756392009728
spelling sg-smu-ink.etd_coll-16532025-02-13T06:05:07Z The impact of instrumental attribution in AI-enabled monitoring on counterproductive work behavior ZHANG, Qiang AI-enabled monitoring tools are theoretically expected to suppress unethical employee behavior. However, in practice, employees may perceive such monitoring as being driven by leaders' instrumental motives, primarily focused on personal performance evaluation and self-interest. This perception can foster feelings of job insecurity and moral disengagement, ultimately leading to counterproductive work behavior (CWB), which includes unethical employee behavior and turnover. These outcomes may undermine the intended effectiveness of AI-enabled monitoring tools. This study aims to explore the impact of Instrumental Attribution in AIenabled Monitoring (IAAIM) on CWB, specifically focusing on unethical employee behavior and turnover, through both theoretical and empirical lenses. Drawing on attribution theory, the study first reviews existing research on the psychological and behavioral consequences of AI-enabled monitoring, the drivers of unethical employee behavior and turnover, and the roles of job insecurity, moral disengagement, and ethical management control. A theoretical framework is then systematically constructed to explain how IAAIM influence CWB. Subsequently, an empirical analysis is conducted using survey data from 72 work teams comprising 558 sales representatives within the marketing department of a large real estate company in China. This analysis provides empirical evidence on the influence of IAAIM on unethical employee behavior and turnover, examining the mediating roles of job insecurity and moral disengagement, as well as the moderating effect of ethical management control. The findings reveal the following: First, IAAIM positively affects job insecurity, though the impact of job insecurity on unethical employee behavior and turnover is not significant. Second, IAAIM positively influences moral disengagement. However, the mediating effect of IAAIM on employee turnover through moral disengagement is not significant. Third, ethical management control mitigates the relationship between IAAIM and job insecurity, and negatively moderates the indirect effect of IAAIM on unethical employee behavior through job insecurity. This suggests that stronger ethical management control weakens this indirect effect. However, this moderating effect is not significant concerning the indirect effect on employee turnover. Fourth, ethical management control also reduces the relationship between IAAIM and moral disengagement, and negatively moderates the indirect effect of IAAIM on unethical employee behavior through moral disengagement, indicating that more effective ethical management control diminishes this indirect effect. Again, this moderating effect is not significant concerning the indirect effect on employee turnover. Theoretically, this study elucidates the internal mechanisms through which IAAIM influences CWB by increasing job insecurity and moral disengagement, and clarifies the moderating role of ethical management control. This research contributes to the literature on the consequences of AI-enabled monitoring and the driving factors of unethical employee behavior and turnover. Practically, it offers valuable insights for enterprises to effectively utilize AI in controlling and preventing unethical employee behavior and turnover, thereby continuously improving human resource management. 2024-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/etd_coll/655 https://ink.library.smu.edu.sg/context/etd_coll/article/1653/viewcontent/博_学位论__GPBF_AY2024_PhD_ZhangQiang.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Dissertations and Theses Collection (Open Access) eng Institutional Knowledge at Singapore Management University AI-enabled Monitoring Instrumental Attribution Unethical Employee Behavior Employee Turnover Job Insecurity Moral Disengagement Ethical Management Control Accounting Artificial Intelligence and Robotics Finance and Financial Management