A Closed‐Loop Human Resource Management Framework for Energy Efficiency: Integrating Predictive Optimization With Behavioral Implementation

ABSTRACT This study addresses the persistent challenge of integrating energy efficiency into human resource management (HRM). While HRM has traditionally focused on the strategic and behavioral dimensions of organizational performance, the systematic incorporation of energy efficiency as a quantifiable management objective remains underdeveloped. A data‐driven framework that integrates predictive optimization with HRM practices is proposed. The framework establishes a closed‐loop structure linking data acquisition, predictive optimization, strategy translation, and execution feedback, thereby bridging technical optimization and human behavioral implementation. Within this structure, HRM functions as an adaptive execution assurance mechanism, translating strategic energy efficiency goals into quantifiable and timely behavioral directives. Execution outcomes are fed back into the optimization loop for continuous improvement. Empirical validation across 46 organizations showed significant improvements in energy efficiency of the proposed method. The average energy consumption decreased by 13.0 kWh/day (14.8%). The difference‐in‐differences analysis supports the association between framework implementation and observed reductions. This study provides an empirically validated paradigm for advancing energy‐efficient HRM and offers a scalable model of data‐driven organizational management.