AI-Assisted Sustainability Intelligence and Decision Support in Manufacturing Organizations Using Expert-Validated Indicators Under the Triple Bottom Line Framework

ESG-AI-MCDM: An AHP-TOPSIS Framework for Smart Tourism SMEs

This study addresses the underexplored financial side of smart tourism by validating an integrated ESG-AI-MCDM decision-support framework. The model links AI-driven predictive analytics with a hybrid multi-criteria loop combining AHP, TOPSIS, and DEA to evaluate strategic alternatives under seasonality and sustainability constraints. Quantitative validation via structural equation modelling (SEM) with 416 active participants proved that AI analytics significantly enhance managerial efficiency ( β = 0.49) and operational resilience ( β = 0.36). Empirically, the AI platform cut operational costs by 26% and boosted visitor satisfaction by 18%. When ESG criteria held a dominant 35% weight, hybrid optimisation identified sustainable energy investment as the optimal strategy, achieving a top TOPSIS score of 0.83 and a superior DEA efficiency score of 0.91. The framework eliminates subjective managerial bias, providing a scalable, resilient financial governance mechanism for tourism SMEs.

A fuzzy MCDM-based approach to monitoring the vaccine supply chain sustainability using objective data and subjective opinions

This study aims to propose an integrated framework for monitoring vaccine supply chain (VSC) sustainability across multiple regions by combining subjective expert judgement and objective performance data. It also examines how regional rankings change when the relative emphasis on these two evidence sources varies. A VSC sustainability evaluation index was developed using sustainability criteria and associated performance indicators across economic, social and environmental dimensions. Subjective weights were derived using spherical fuzzy DEMATEL to capture causal interrelationships among criteria under uncertain expert judgement. Objective weights were obtained using CRITIC to identify criteria with high regional variation and non-redundant information. The two weights were combined through a sensitivity factor, and VIKOR was applied to generate compromise rankings of regions. The framework was applied to 18 blocks in an Indian district. SF-DEMATEL identified cost optimisation, sustainable financing and training as the most structurally influential criteria. CRITIC identified sustainable financing, workforce requirement and energy and emission as the most informative criteria for differentiating regional performance. Under balanced weighting, Namkum and Sonahatu emerged as the strongest-performing blocks, while Chanho and Itki appeared among the weakest. Sensitivity analysis showed that some blocks remained stable across weighting conditions, whereas others were more sensitive to expert-derived causal judgement. The framework helps public health managers identify whether a region requires expert-led systemic diagnosis, PI-level corrective action or broader multi-criteria strengthening. This study develops a VSC sustainability assessment framework integrating causal expert weights, objective regional performance information and sensitivity-based ranking diagnosis.

Prioritizing sustainability indicators for project success in volatile environments: a three-phase MCDM approach in Iran

The purpose of this study is to identify and prioritize the sustainability indicators (SIs) perceived as most influential for project success within Iran’s volatile socioeconomic environment. It aims to bridge the gap between global sustainability standards and local operational realities in resource-constrained contexts. A sequential mixed-methods design used a hybrid multicriteria decision-making framework. First, the fuzzy delphi method (FDM) screened indicators using an 11-expert panel. Second, the method based on the removal effects of criteria (MEREC) objectively weighted the success criteria based on data variance. Finally, the combined compromise solution (CoCoSo) method ranked the SIs. The analysis reveals a context-specific prioritization where financial analysis, project safety and resilience emerged as the top-ranked indicators, while renewable materials ranked lowest. This suggests that in sanctioned, high-inflation settings, practitioners prioritize economic viability and operational risk mitigation; broader environmental aspirations are strategically deferred until foundational project continuation is secured. The findings reflect expert judgments predominantly from heavy industrial sectors in Iran. Therefore, results are context-bound and may not fully capture the nuances of agile sectors (e.g. software) or translate to stable macroeconomic environments. For practitioners in volatile markets, the study provides a resource allocation roadmap. It recommends integrating financial and safety metrics into core control systems (e.g. earned value management) and dynamic risk registers, moving beyond “one-size-fits-all” sustainability checklists. This paper integrates the FDM-MEREC-CoCoSo framework into sustainable project management to minimize subjective bias. It contributes empirical evidence extending contingency theory by demonstrating that macroeconomic volatility fundamentally reorders sustainability priorities, reframing sustainability in volatile environments as a pragmatic risk-management strategy.

Evaluation of AI Tools in Terms of Sustainable Workforce Productivity and Their Impact on Organizational Outcomes Using an Intuitionistic Fuzzy Approach

A multi-criteria decision-making framework for evaluating six AI tools against eight sustainability-oriented criteria indicates that improving individual productivity and enhancing employee engagement and motivation are the most influential evaluation criteria.

AI and Fuzzy Multi-Criteria Decision-Making for Organizational Performance and Strategic Management

This paper investigates the role of contemporary MCDM approaches in improving organizational performance assessment and strategic decision-making by examining methodological developments, practical implementation frameworks, evaluation indicators, and performance outcomes and proposes an integrated conceptual framework that facilitates objective strategic prioritization, efficient resource allocation, and sustainable organizational competitiveness.

A fuzzy multi-criteria decision framework for sustainability-oriented prioritization of nature-based solutions in circular economy investments