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This study aims to determine the extent to which the textile industry considers ecological issues in the supplier evaluation process by examining the relationships between environmental and operational criteria.

The supplier evaluation process within the industry was examined using the Fuzzy DEMATEL-based Analytic Network Process (FDANP) hybrid method. Criterion relationships were determined using the Fuzzy DEMATEL method. Two different ANP processes (the classical DANP approach and the new DANP approach) were used to assess the criterion weights, and the analysis results were compared. The satisfaction levels of suppliers with whom businesses collaborated were also examined using IPA analysis.

The results of the hybrid approaches with different steps in the study also differ. While the classic FDANP approach prioritizes cost and quality in the green supplier evaluation process, green products and EMS come to the fore according to the new FDANP approach. In addition, while businesses are satisfied with the operational performance of suppliers, their environmental performance remains below average.

This study was conducted in a sector-specific context, limited to Denizli province and green raw material suppliers.

This study revealed important findings regarding environmental awareness in buyer-supplier relations in the textile industry and offers a practical decision-making tool for decision- makers.

The results of this study may help industrial enterprises gain an environmental perspective when planning their operations to create a more sustainable environment.

This study contributes to the literature from a holistic perspective by examining the role and importance of operational and environmental criteria in the green supplier evaluation process in the textile industry.

The purpose of this study is to explore how to select a suitable supply chain collaboration paradigm (SCCP) based on the intelligent manufacturing model (IMM) of enterprises.

Given the fit between internal collaboration and external collaboration, we propose a model to select a suitable SCCP based on two-sided matching between SCCPs and IMMs. In this decision problem, we invited five university scholars and seven related consultants to evaluate SCCPs and IMMs based on the regret theory, which is used to obtain the perceived utility and matching results. The evaluation values are comfortably expressed through probabilistic linguistic term sets (PLTSs). Also, we set the lowest acceptance threshold to improve the accuracy of matching results.

The findings indicate that the characteristics of IMMs can significantly influence the selection of SCCPs, and an SCCP is not suitable for all IMMs. Interestingly, the study findings suggest that the selection of SCCP is diverse and multi-optional under the constraints of IMMs.

Existing studies have explored supply chain collaboration (SCC) in Industry 4.0 to improve supply chain performance, but less attention has been paid to the impact of the match between SCCPs and IMMs on supply chain performance. And even fewer studies have addressed how to select a suitable SCCP in different IMMs. This study provides a unique contribution to the practice of SCC and expands the understanding of supply chain management in Industry 4.0.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

Logistics service provider (LSP) selection involves multiple criteria, alternatives and decision makers. Group decision-making involves vagueness and uncertainty. This paper aims to propose a novel fuzzy method for assessing and selecting agile, resilient and sustainable LSP, taking care of the inconsistency and uncertainty in subjective group ratings.

Eighteen agile, resilient, operational, economic, environmental and social sustainability criteria were identified from the literature and discussion with experts. Interval-valued Fermatean fuzzy (IVFF) sets are more flexible and accurate for handling complex uncertainty, impreciseness and inconsistency in group ratings. The IVFF PIvot Pairwise RElative Criteria Importance Assessment Simplified (IVFF-PIPRECIAS) and IVFF weighted aggregated sum product assessment (IVFF-WASPAS) methods are applied to determine criteria weights and LSP evaluation, respectively.

Collaboration and partnership, range of services, capacity flexibility, geographic coverage, cost of service and environmental safeguard are found to have a greater influence on the LSP selection, as per this study. The LSP (L3) with the highest score (0.949) is the best agile, resilient and sustainable LSP in the manufacturing industry.

Hybrid IVFF-based PIPRECIAS and WASPAS methods are proposed for the selection of agile, resilient and sustainable LSP in the manufacturing industry.

The model can help supply chain managers in the manufacturing industry to easily adopt the hybrid model for agile, resilient and sustainable LSP selection.

The paper also contributes to the social sustainability of logistics workers.

To the best of the authors’ knowledge, IVFF-PIPRECIAS and IVFF-WASPAS methods are applied for the first time to select the best agile, resilient and sustainable LSP in a developing economy context.

This study aims to develop cause-and-effect relationships among the critical success factors (CSFs) of fintech adoption and rank these CSFs based on their importance in the model.

To achieve the objectives of the study, the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) approach was used. The data was collected from 16 experts using a questionnaire.

The findings demonstrated the interrelationships among the CSFs. In total, 16 critical factors were recognized as causal factors, and the remaining eight were considered effect factors. The CSFs were ranked based on their importance in fintech adoption.

This study is novel as it investigates CSFs of fintech adoption using FDEMATEL, and it contributes to understanding the nature of these factors and how they affect fintech adoption. The findings propose a significant basis to deepen fintech adoption and deliver a clue to design a practical framework for fintech adoption.