Sadegh Niroomand

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.

E-business projects may face several risks that are potential to affect the success of such projects. As an important issue for a project manager in this field, the potential risks should be prioritized according to their potential impact on the success of the project. In this study, the potential risks of the e-business sector are to be evaluated and ranked based on the success criteria of such projects. For this aim a complete set of risks compared to the literature is considered and their impact on the project success criteria are determined linguistically by the experts of the field as a case study. To respect the uncertain nature of the evaluations, the linguistic evaluations are converted to belief-degree-based uncertain values. Then for the first time, a data envelopment analysis (DEA) model is used to evaluate and rank such risks. For this aim, the classical Tchebycheff norm DEA model is extended to a belief-degree-based uncertain environment for the first time. An extensive computational study including sensitivity analysis and comparative study is performed by the uncertain Tchebycheff norm model to evaluate and rank the risks of the case study. Based on the obtained results “introducing new technology” and “cultural risk” are the most and least important risks respectively.

This study addresses the critical challenge faced by organizations in selecting an optimal subgroup of decision-making units (DMUs). Such a selection procedure can significantly influence efficiency, profitability, and strategic development. Recognizing the limitations of existing methods in handling inexact data and incorporating managerial preferences, this study proposes a novel framework that integrates data envelopment analysis (DEA) with binary linear programming models. The model applies belief-degree–based representations of uncertainty to capture imprecise inputs and outputs. For this model, two solution approaches—namely, chance-constrained programming and expected value approaches—were developed. These approaches are suitable for real world applications using standard optimization software. The effectiveness of the proposed method was validated through a case study in Iran’s petrochemical industry, where it successfully identified the optimal technology for a new refinery unit while balancing efficiency and profitability under uncertainty. This work is the first study in the literature to combine DEA and binary linear programming under belief-degree–based uncertainty for DMU selection, offering a systematic, practical, and computationally efficient solution, with recommendations for future research to explore alternative uncertainty modeling techniques and broader industrial applications.