Utility-driven operator-information co-regulation process: a human-centred motivational perspective

Human performance in procedural work varies even under standardised conditions, revealing limits in traditional prescription-based error prevention. This study adopts an abductive, single-case design in industrial remanufacturing to explore how cognitive and motivational regulation shape procedural adherence. Through three iterative cycles of systematic combining, five analytical constructs were identified, leading to the dynamic motivational filtering model. The model conceptualises adaptive guidance as state- information co-regulation, where content is dynamically filtered via a predictive utility function and latent-state estimation. Formalised through a Kalman-based architecture, the system infers the operator's evolving state by integrating motivation as an active control variable. Two illustrative use cases provide a functional proof of concept, demonstrating the model's logic in regulating real-time information flow. The framework provides a computational foundation for next-generation human-machine interfaces that sustain attention, autonomy, and safety in complex Industry 5.0 environments.