Justice-Voice Model for Neuro-Hybrid Leadership: Integrating Organizational Justice and Voice Behavior into Strategic Human Resource Management for Hybrid Work

Abstract

We propose the Justice-Voice Model for Neuro-Hybrid Leadership (JV-NHL), a novel framework that integrates organizational justice theory and voice behavior into the adaptive feedback loops of strategic human resource management for hybrid work environments. The conventional neuro-leadership architecture, which passively collects employee neural data to inform leadership recommendations, is replaced by a bidirectional, justice-constrained system. This system conditions both data ingestion and intervention output on perceived procedural fairness and employee voice opportunity. The JV-NHL comprises three core modules: a Justice-Voice Perception Encoder that quantifies real-time perceptions of justice and voice from multimodal data streams, a Procedural Fairness Gate that filters neuro-data based on threshold conditions derived from validated psychometric instruments, and an Adaptive Leadership Protocol Calibrator that employs reinforcement learning to generate personalized leadership interventions. The gate ensures that only data streams where employees perceive high procedural justice and have exercised voice are used to inform adaptive recommendations, thereby protecting employee agency. The calibrator then optimizes a reward function that balances performance outcomes with the maintenance of justice and voice scores. This closed-loop architecture substitutes into conventional performance management and learning and development systems, creating a feedback mechanism where every adaptive recommendation is audited for ethical robustness. Our contribution is threefold: we operationalize organizational justice and voice behavior as computational constraints within neuro-leadership, we introduce a gating mechanism that prevents low-trust data from influencing leadership decisions, and we provide a reinforcement learning framework that explicitly rewards the preservation of fairness perceptions. The significance of this work lies in its potential to reduce employee resistance to neuro-monitoring while empirically grounding leadership automation in validated organizational behavior theory.