Optimizing Knowledge Flow in Hybrid Work Models: The Impact of Alternating Schedules and Tacit Knowledge

In response to the widespread adoption of hybrid work models, organizations must strategically address the challenges of knowledge transfer and organizational learning in distributed environments. We extend March’s computational model of organizational learning by initially incorporating three variables: the ratio of tacit-to-explicit knowledge, the proportion of remote workers, and structured shift arrangements. The extended model incorporates distinct subgroups for remote and on-site workers, organizational memory mechanisms for tacit knowledge exchange, and alternating work location schedules designed to foster interaction. Simulation results reveal that under non-contact scheduling, the interaction effect between learning from code/memory (p1) and the proportion of tacit knowledge (q) is insignificant, while the coefficient of interaction effect p2×q between learning by code/memory (p2) and q is twice that under partial-contact or full-contact scheduling. Moreover, under full-contact scheduling, the interaction effect between p1 and the proportion of work-from-home employees (wfh) is insignificant (p > 0.1), whereas the interaction effect between p2 and wfh is significant (p< 0.05). Aligning with March’s findings that a low p1 and high p2 contribute to higher organizational knowledge, our simulation results indicate that non-contact scheduling preserves knowledge diversity, and full-contact scheduling promotes small-world network effects, thereby enhancing organizational knowledge equilibrium. These findings position hybrid work scheduling as a data-driven managerial decision, and the proposed model offers analytical insights for optimizing knowledge processes within business analytics contexts.