Algorithmic Operational Control in Logistics: The Mediating Role of Human Operational Agency in Perceived Innovation Quality

Background: Algorithmic Operational Control increasingly shapes logistics operations through AI-enabled decision systems, yet its relationships with Human Operational Agency and innovation remain insufficiently understood. This study examines relationships among Algorithmic Operational Control, Leadership Mediation Capacity, Human Operational Agency, and perceived Logistics Innovation Quality. Methods: Survey data were collected from 3613 logistics employees across six Western Balkan countries. A split-sample design combined exploratory and confirmatory factor analyses with structural equation modeling, supplemented by organizational clustering diagnostics, multi-group CFA, and a reduced-overlap sensitivity analysis. Results: Algorithmic Operational Control was negatively associated with Human Operational Agency and Logistics Innovation Quality, whereas Leadership Mediation Capacity was positively associated with Human Operational Agency. Human Operational Agency partially mediated the association between Algorithmic Operational Control and Logistics Innovation Quality and fully mediated the association between Leadership Mediation Capacity and Logistics Innovation Quality. Organizational clustering was negligible, the measurement structure was stable across countries, and sensitivity analysis preserved all principal relationships and mediation effects. Conclusions: The findings indicate that preserving human judgment and adaptive decision-making is associated with more exploratory and long-term-oriented innovation and position leadership as an interpretive mechanism reconciling algorithmic recommendations with human judgment.