JOURNAL ARTICLE
The Power of Precision: How Algorithmic Monitoring and Performance Management Enhances Employee Workplace Well‐Being.
Published In: New Technology, Work & Employment, 2025, v. 40, n. 3. P. 390 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Deng, Hui; Lu, Ying; Fan, Di; Liu, Wei; Xia, Yuhuan 3 of 3
Abstract
Can algorithmic control positively impact employee well‐being in the workplace? This study examines the potential benefits of algorithmic control, particularly through monitoring work activities and assessing performance, in enhancing employees' workplace well‐being within conventional employment settings. Grounded in labour process theory, our analysis of a multi‐wave data set reveals that both algorithmic monitoring and performance management can foster employees' perceptions of organizational fairness, which subsequently supports workplace well‐being. Additionally, the study finds that algorithmic transparency further strengthens these positive effects, emphasizing the value of clear and accessible communication around algorithmic processes. These insights offer a practical framework for leveraging algorithmic tools to harness the power of precision, enhancing perceptions of fairness and promoting employee well‐being. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:New Technology, Work & Employment. 2025/11, Vol. 40, Issue 3, p390
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:0268-1072
- DOI:10.1111/ntwe.12328
- Accession Number:189104491
- Copyright Statement:Copyright of New Technology, Work & Employment is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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