Industry 5.0 and e-waste mitigation: environmental policy stringency as moderator – a mixed-methods PLS-SEM, NCA and Delphi study

Purpose This study investigates the direct and interrelated impacts of Industry 5.0 pillars – Sustainability, Resilience, and Human-centricity – on three integral E-waste mitigation drivers: Collection and Recycling; Segregation and Repurposing; Generation Reduction and Material Recovery. Additionally, the moderating effect of Environmental Policy Stringency is explored. Design/methodology/approach A mixed-methods approach combines Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA) on macro-level indicators from 113 countries with micro-level insights from a two-round Delphi, triangulating statistical evidence with practitioner judgement. Findings The PLS-SEM results reveal that Sustainability exerts the strongest influence on Generation Reduction and Material Recovery, while Resilience significantly enhances Collection and Recycling. Environmental Policy Stringency positively amplifies the relationships. Notably, Human-centricity shows a modest direct effect that becomes substantial under high Environmental Policy Stringency. Complementing that, NCA uncovers key necessity and sufficiency thresholds for each Industry 5.0 pillar. Finally, Delphi provides practical insights across five refined dimensions: Technological integration; Human-centric approaches; Sustainability and circular economy; Regulatory and policy framework; Stakeholder collaboration and knowledge sharing. Practical implications The findings guide decision-makers on prioritized thresholds for resource allocation and policy design to improve E-waste outcomes across different country archetypes. Also, practical Environmental Social Governance actions for E-waste stakeholders are elaborated. Originality/value This study advances literature on sustainable E waste management in Industry 5.0 era by developing an empirically grounded mixed methods framework that integrates macro level data with micro level expert insights.