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Small-to-medium-sized enterprises (SMEs) in e-commerce often invest in information technology (IT) to stay competitive. However, whether and how IT capability (ITC) translates into financial performance requires further research. This paper examines the role of ITC in enabling value proposition innovation (VPI) as an important mechanism that improves financial performance for Chinese e-commerce SMEs during the COVID-19 pandemic. We argue that ITC is critical for enabling innovation because it elevates SMEs’ understanding of changing customer needs, especially when SMEs operate on multiple e-commerce platforms (multihome).

We used partial least squares structural equation modeling (PLS-SEM) and tested the hypotheses that ITC mediated by VPI and moderated by multihoming increases the financial performance of e-commerce SMEs through a survey among 206 Chinese SMEs operating on Taobao.

We find that not only higher levels of ITC lead to better financial performance, but also that the effect is fully mediated by VPI. Moreover, the effect of ITC on innovation is enhanced when vendors operate on multiple platforms.

The study identifies VPI as an important mechanism through which SMEs can leverage their ITC to adapt, innovate and thrive in competition. Our work suggests that using technology to develop innovative ideas and identify opportunities (which are reflected in VPI) is key to success and that doing so is more likely when vendors multihome. Thus, this study contributes to the innovation literature by explicating a concrete link between ITC, multihoming, VPI and increased financial performance. Different e-commerce stakeholders, including SME owners, IT and service providers and e-commerce platforms, can benefit from the findings of this work.

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 aims to provide a practical and novel solution for the complex multi-criteria decision-making (MCDM) problem of airline route selection, which is characterized by conflicting criteria, alternative routes, and complex judgments.

This study proposes a hybrid MCDM approach using Interval-valued Pythagorean Fuzzy AHP and Interval-valued Pythagorean Fuzzy weighted aggregated sum product assessment (WASPAS) methods. Decision analysis is applied to select a new route between different alternatives through selection criteria. Pythagorean Fuzzy AHP is used for weighting criteria, and Pythagorean Fuzzy WASPAS is used for assessing alternatives. The pair-wise linguistic comparisons of selection criteria are transferred into Pythagorean fuzzy numbers (PFNs) to weigh each criterion’s importance.

The pair-wise linguistic comparisons of selection criteria are transferred into PFNs to weigh each criterion’s importance. The results of these comparisons show that the main criteria, cost (43% weight) and demand (33% weight), impact route selection decisions more than social/economic conditions (15% weight) and competitiveness (9% weight). Regarding the criteria, the five routes alternative were evaluated by the route development experts, and the best route was selected with Pythagorean Fuzzy WASPAS.

The proposed model is used for a route selection problem of Turkish Airlines, the airline that flies to the most countries in the world.

To the best of the authors’ knowledge, this study is the first to use the Interval-valued Pythagorean Fuzzy AHP combined with Interval-valued Pythagorean Fuzzy WASPAS to solve the route selection problem. This hybrid MCDM methodology presents a novel and feasible solution for selecting the new route for airlines.

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.

Unlike the design or planning phases, the construction phase necessitates the coordination of multiple project stakeholders, real-time problem-solving and effectively managing physical resources under fluctuating site conditions. However, recent green rating systems are not yet designed to accommodate the unique nature of this stage. Thus, this study aims to develop an empirical measurement model to evaluate the performance of green construction sites (GCS), focusing on the Vietnamese construction industry.

Initially, a list of assessment criteria was identified by reviewing previous studies and semi-structured interviews. After that, a questionnaire survey was used to collect practitioners’ views regarding GCS performance. The 155 valid responses were analyzed employing the fuzzy synthetic evaluation (FSE) method.

The findings showed that the performance of GCS in Vietnam is at a moderate level, with a GCS index of 5.48. Indeed, the human-related factor was the most significant performance group for GCS in Vietnam. Construction site, energy, innovation and material-related factors were among the average performance factors. Moreover, water and waste-related factors were among the lowest performance factors.

This study highlights the importance of human-related factors, such as worker safety and environmental training, in achieving effective GCS performance. It is recommended that governments should enforce stricter guidelines on using low-embodied carbon materials, sustainable sourcing and effective waste management to improve GCS performance. Neighboring countries can also use valuable insights from this study to enhance their GCS performance.

The Green Deal strategy of the European Union (EU) as well as the increasing concerns over resource scarcity worldwide has put forward such concepts as the circular economy. This paper seeks to compare the progress of the development of the circular economy across the EU Member States. Such analysis is helpful in guiding the circular economy support policies.

This paper develops a quantitative framework for analysis of the implementation of the circular economy objectives in the EU Member States. The framework proposed includes three multi-criteria decision making methods representing reference point and outranking approaches. The use of multiple methods allows exploiting the differences in the underlying aggregation principles.

Germany, Sweden and the Netherlands appear as the most advanced in the sense of circular economy development. The results indicate that the water-locked small countries and the new EU Member States are among the lowest performing ones. The flows of the waste need to be monitored more tightly in order to increase the circularity. The development of recycling facilities can also increase circularity irrespectively of the economic development level (e.g. the case of Bulgaria).

The paper contributes to the discussion regarding the circular economy by proposing an indicator system and the multi-criteria analysis framework. The proposed indicator system covers input use (circularity), trade flows and recycling processes. The proposed framework can be applied to track the progress of different countries in implementing the targets of the circular economy.

Mismanagement of municipal solid waste (MSW) has led to resource depletion, ecosystem degradation and socio-economic risks, emphasizing the necessity to promote sustainable circular economy (SuCE) practices. This article identifies relevant enablers and unveils their interconnected roles in facilitating a municipal solid waste management (MSWM) system aligned with the principles of SuCE.

To identify pertinent enablers for MSWM systems based on SuCE principles, the study conducted a literature review and engaged experts from waste management sectors and academia to consolidate the final list of enablers. Following this, the study investigated the inter-relationships among these enablers using both Interpretive Structural Modeling (ISM) integrated and fuzzy Matrix Impact Cross-Reference Multiplication Applied to a Classification (fuzzy MICMAC) with MATLAB.

The study identified a comprehensive set of 23 enablers and categorized them into four control measures instrumental for the effective integration of SuCE principles in the context of the Indian MSWM system. Notably, the analysis highlighted that “authoritative pressure” and “policy transformation” are the highly influential enablers.

The study emphasizes actionable implications for stakeholders in the Indian MSWM sector. Policymakers and governmental bodies can enforce “authoritative pressure,” “policy transformation” and “budget allocation”. Meanwhile, managers can focus on “promotion of SuCE awareness”, “formalizing the informal workforce”, “infrastructure modernization”, “stakeholder engagement and alignment”, “segregation strategy”, “disruption preparedness”, and “local-based strategy”.

This study proposed a unique ISM diagraph and cluster of enablers to enhance the novelty of the study.