Managerial Consequences of the Algorithmic Determination of Incentive Decisions on Procedural Justice Perceptions in Production Line

This study investigates the managerial consequences of algorithm-based decision-making in incentive allocation on production line workers’ perceptions of procedural fairness. Grounded in organizational justice theory and the socio-technical systems framework, the research explores how algorithm transparency, process clarity, and perceived impartiality influence fairness judgments. Using a mixed-method approach, data were collected from 186 factory workers through surveys, complemented by in-depth interviews, and analyzed using structural equation modeling. The findings reveal that low algorithm transparency correlates negatively with perceptions of procedural fairness, while the absence of human involvement in decision-making reduces trust in managerial systems. These results emphasize the need for human-centered algorithm design and fair management practices to uphold employee trust and fairness in Industry 4.0 environments.