Reframing Total Quality Management for Industry 5.0: The TQM5.0 framework and empirical validation

Purpose This study develops and empirically tests a TQM5.0 framework that integrates classical total quality management (TQM) with Industry 5.0 value priorities through the enabling mechanism of quality intelligence. It examines how TQM practices and Industry 5.0 capabilities influence quality intelligence and how quality intelligence drives quality, sustainability, and resilience performance outcomes. Design/methodology/approach Data were collected via a structured questionnaire from 56 purposively selected domain experts with decision-relevant roles in manufacturing quality and operations, and the model was analyzed using Partial Least Squares Structural Equation Modeling in SmartPLS with bootstrapping. The study assessed measurement reliability/validity, collinearity, structural paths, explanatory power (R2), effect sizes (f2), and mediation via specific indirect effects. Findings TQM practices significantly predict quality intelligence, while Industry 5.0 capabilities do not show a significant effect on quality intelligence in this sample. Quality intelligence significantly improves quality, sustainability, and resilience performance (with the strongest effect on resilience), whereas the proposed mediation effects through quality intelligence are not statistically supported. Practical implications Managers should prioritize strengthening TQM governance routines and measurement discipline to build robust quality intelligence capabilities that enable proactive and adaptive performance. Implementation should sequence foundational TQM routines, quality data and analytics infrastructure, and the scaling of Industry 5.0 initiatives through intelligence-enabled governance to improve sustainability and resilience outcomes. Originality/value The study advances the emerging TQM5.0 concept by positioning quality intelligence as a central mechanism linking quality governance to Industry 5.0-aligned performance outcomes and by validating a parsimonious measurement and structural model. It contributes empirical evidence from expert-informed manufacturing contexts and provides actionable guidance on capability sequencing for intelligence-enabled quality transformation.