How Do Algorithmic Management Practices Affect Workforce Well-Being? A Parallel Moderated Mediation Model.
Husam Zayid1, Ahmad Alzubi1, Ayşen Berberoğlu1
1Department of Business Administration, Institute of Graduate Research and Studies, University of Mediterranean Karpasia, 33010 Mersin, Turkey.
Algorithmic management practices increase job burnout and perceived threat, negatively impacting workforce well-being. However, person-job fit can mitigate these adverse effects, highlighting the need for human-centric approaches.
Area of Science:
- Organizational Behavior
- Human Resource Management
- Digital Transformation
Background:
- Modern workplaces increasingly adopt Algorithmic Management Practices (AMPs) for efficiency and objectivity.
- Concerns exist regarding the impact of AMPs on Workforce Well-being (WFW).
- Theoretical frameworks like Self-Determination Theory (SDT) and Conservation of Resources Theory (COR) inform the study.
Purpose of the Study:
- To examine the relationship between AMPs and WFW.
- To investigate the mediating roles of Job Burnout (JBO) and Perceived Threat (PT).
- To explore the moderating effect of Person-Job Fit (PJF).
Main Methods:
- Cross-sectional survey design with 666 respondents from Istanbul manufacturing SMEs.
- Data analysis using Smart PLS 4 and structural equation modeling.
- Testing a model with AMPs, JBO, PT, PJF, and WFW.
Main Results:
- AMPs significantly increased JBO and PT, which negatively affected WFW.
- The direct impact of AMPs on WFW was not significant.
- PJF moderated the relationship between AMPs and mediators (JBO, PT), influencing WFW.
Conclusions:
- Organizations must balance algorithmic efficiency with human-centric practices.
- Prioritizing PJF and transparency in AMPs can mitigate negative employee well-being outcomes.
- Strategic implementation of AMPs is crucial for sustainable organizational success in the digital age.
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