How Do Algorithmic Management Practices Affect Workforce Well-Being? A Parallel Moderated Mediation Model
Husam Zayid, Ahmad Alzubi, Ayşen Berberoğlu, Amir Khadem, Xin-An Zhang, Lin Lu, Ming Kong · 2024
Read How Do Algorithmic Management Practices Affect Workforce Well-Being? A Parallel Moderated Mediation Model by Husam Zayid, Ahmad Alzubi, Ayşen Berberoğlu, Amir Khadem, Xin-An Zhang, Lin Lu, Ming Kong (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11672927.txt.
Description
Husam Zayid and colleagues examine how algorithmic management affects worker well-being. Their survey study from twenty twenty-four draws on manufacturing firms in Istanbul. Software now assigns tasks, monitors staff, and scores performance in many workplaces. Such systems promise fairness and efficiency, but workers may feel threatened.
Hundreds of employees answered the survey across many small firms. Statistical modelling tested paths between management style and well-being. Algorithmic practices raised both burnout and perceived threat. Both of those harms then lowered well-being. The direct link from practices to well-being was not significant alone. A good fit between person and job softened some harmful links. Managers learn that fit matters when software runs the workplace.
Catalog record
| CREATOR | Husam Zayid, Ahmad Alzubi, Ayşen Berberoğlu, Amir Khadem, Xin-An Zhang, Lin Lu, Ming Kong |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 77 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |