Hierarchical Continuous Monitoring and Resource Reallocation Under Resistance to Change: A Decision-Making Framework Balancing Skill Constraints and Managerial Capacity

Organizational change is a complex process often accompanied by intense human reactions and increased uncertainty. Resistance to change (RtC) can cause critical performance declines during the organizational change period, which can delay implementation. The evolution of information systems and digital infrastructures provides immediate access to operational data and analytical tools, making it possible to continuously monitor performance and timely adjust decisions during change. Although recent approaches attempt to minimize these impacts through continuous monitoring and resource reallocation, they typically view human resource allocation as a single-level problem. In hierarchical structures where work and decision-making are distributed across levels, RtC can increase backlogs, place an excessive amount of work on managers, and result in operational issues or the failure of the change. From an algorithmic perspective, the proposed method formulates a hierarchical dynamic optimization problem with two coupled assignment layers, in which the operational output of Level 1 dynamically determines the workload processed at Level 2. Both assignment problems are solved at each time step using the Hungarian algorithm, while RtC is modelled as a time-dependent stochastic process aligned with a reference change curve, allowing employee and managerial performance to be updated dynamically over the planning horizon. In contrast to static Classical Change Management Model (CCMM), large-scale experimental results demonstrate that the new approach increases total processed workload by approximately 20%, while at the peak of resistance, the improvement reaches 56.8%. At the same time, it substantially reduces backlog accumulation, maintaining very low backlog levels (18 versus 16,424 units) within the tested setting. Finally, by applying a 50% reallocation threshold, the organization maintains 98.5% of maximum performance while avoiding 45% of the reallocations. Overall, the proposed method provides a dynamic optimization framework that combines hierarchical organizational modeling with stochastic performance updates across organizational levels.