The COVID-19 pandemic accelerated the adoption of work-from-home (WFH) practices, raising important questions about their long-term implications for organisational performance. This issue is particularly salient for multinational enterprises (MNEs) and international small and medium-sized enterprises (SMEs), where digitalisation has significantly reshaped work arrangements. This study evaluates the advantages and disadvantages of WFH and their differential impacts on MNEs and international SMEs. A comparative analysis was conducted using expert pairwise judgements, assessed through linguistic terms with weakened hedges (LTWHs) within the Best–Worst Method (BWM) framework. The LTWHs approach enables experts to articulate nuanced and flexible preferences, extending traditional linguistic scales by softening the strength of evaluations. This makes it particularly suited for capturing subjective assessments of WFH impacts under conditions of uncertainty. The findings indicate substantial variation in WFH adoption, with private sector organisations demonstrating approximately 50 % greater willingness to adopt WFH compared to public authorities. The analysis further highlights the distinct advantages and disadvantages that shape the performance outcomes of MNEs and international SMEs. By introducing a novel hesitant fuzzy linguistic preference approach, this study develops a comprehensive framework for assessing the organisational consequences of WFH. The results offer valuable insights for managers and policymakers seeking to strike a balance between flexibility, productivity, and resilience in the design of post-pandemic work strategies. • Paper investigates the implications of work-from-home using digitalisation. • This is done on the performance of multinational enterprises and international SMEs. • Findings reveal a difference between the public and private sectors in their adoption of WFH. • Study highlights pros and cons of WFH, demonstrating their varying impacts.

Transport decarbonisation requires allocating limited resources across competing strategies. The Avoid–Shift–Improve framework categorises these strategies to reduce transport's reliance on fossil fuels. This study develops an Avoid–Shift–Improve driven network data envelopment analysis (DEA) framework to measure county-level progress toward transport decarbonisation. The framework also serves as a decision-support tool for allocating resources efficiently to advance transport decarbonisation. Using data from Ireland's 26 counties, we integrate DEA with a Stackelberg leader–follower game and the best–worst method. The DEA component provides an objective, mathematically defined measure of relative efficiency. The best–worst method incorporates expert judgement on the economic and environmental impact of actions within the Avoid–Shift–Improve hierarchy. This hierarchical approach, reflecting expert consensus, prioritises transformational measures that reduce travel demand (Avoid), followed by strategies that shift remaining trips to low-carbon modes (Shift), while technological improvements (Improve) play a more limited role. Results reveal disparities in county performance. Dublin leads due to its relatively well-developed public transport and active mobility infrastructure. Smaller counties such as Longford and Leitrim also perform strongly despite their rural character. By contrast, large-area counties including Cork, Mayo, and Kerry underperform, reflecting structural challenges of dispersed settlement and high car dependency. The analysis highlights that larger counties achieve lower efficiency scores, while links with emissions and expenditure are weaker, underscoring the role of spatial scale and carbon lock-in in shaping outcomes. The framework is scalable to other regional and national contexts and can support economically rational, socially inclusive climate policy. • Develops a network DEA model for hierarchical importance of subunits in parallel structure. • Employs the Avoid–Shift–Improve framework to assess transport decarbonisation progress. • Integrates DEA, Stackelberg game, and BWM to evaluate emission-reduction strategies. • Prioritises and harmonises transport climate actions to maximise decarbonisation gains. • Provides a decision-support tool for policy-driven transport decarbonisation.

Institutional drivers and sustainability priorities in blockchain adoption for an emerging economy

The rapid expansion of air cargo transportation has necessitated fleet expansion to meet growing demand. Due to the high capital costs associated with new aircraft acquisitions, attention has increasingly shifted toward used aircraft as a cost-effective alternative. However, selecting an appropriate used aircraft from a range of heterogeneous options is a critical multi-criteria decision-making challenge. To address this issue, this study introduces an integrated decision-making framework for used aircraft selection by combining the technique for order preference by similarity to ideal solution (TOPSIS) and the best–worst method (BWM) in a hesitant fuzzy environment. First, in response to the limitations of existing distance measures, a novel distance measure for hesitant fuzzy sets (HFSs) is proposed that explicitly incorporates the hesitation degree to better capture uncertainty. Subsequently, this measure is incorporated into a modified hesitant fuzzy TOPSIS (M-HFTOPSIS) to enable a more precise evaluation of alternatives. The hesitant fuzzy BWM (HFBWM) is employed to calculate criteria weights, and the proposed M-HFTOPSIS is used to rank the alternatives. A case study involving ten criteria from technical, economic, and environmental perspectives is conducted to validate the effectiveness of the proposed method. Comparative results demonstrate that the proposed approach provides reasonable and reliable outcomes and that the enhanced HFS distance measure effectively models the differences between hesitant fuzzy sets.

ABSTRACT As the maritime industry gradually opens its doors to female professionals, the pressing issue of human rights preservation for women seafarers has emerged as a critical concern. Women entering this industry often encounter unique sociocultural and professional challenges that hinder the protection of their human rights. Recognising this concern, the present study focuses on the preservation of seafarers’ human rights, with particular attention to women. The Best Worst Method (BWM) has been employed to evaluate the extent to which these rights are preserved for women seafarers. The findings reveal the need for a steady improvement in areas such as physical conditions of seafarers, prevention of accidents and injuries, equal gender opportunities, security for seafarers, their families, and allies, compliance and enforcement (MLC, ILO, national laws and regulations).

Abstract The increasing frequency of supply chain disruptions has made resilience and digital integration essential. Indeed, firms that succeeded during the COVID-19 pandemic demonstrated advanced digital and supply chain resilience capabilities, enabling them to mitigate adverse consequences. Addressing this forefront topic in operations management, the article introduces an innovative max–min best–worst method to guide digital transformation in global supply chains. This approach aims to maximize resilience capabilities and minimize the impact of supply chain disruptions. The method is examined through an empirical case study of a highly digitalized furniture company. The findings emphasize the importance of redundancy, a robust risk management culture, and adaptability, with raw material delays and cyberattacks being the most significant threats. Moreover, prioritizing the adoption of big data, digital platforms, blockchain, and advanced control systems is crucial for addressing ongoing geopolitical disruptions. Finally, the study discusses both theoretical and managerial implications and suggests avenues for future research.

The rapid diffusion of industry 4.0 technologies has substantially transformed the maritime transportation sectors by enabling data-driven operations, enhanced connectivity, and more intelligent decision-making processes. Digital technologies such as the Internet of Things (IoT), simulation systems, and advanced data analytics are increasingly reshaping operational structures in maritime logistics, positioning technological transformation as a strategic priority for firms. However, the weighting and prioritization of components emerging with industry 4.0 technologies remain an underexplored area in the literature. The primary motivation of this study is to determine the weights of these industry 4.0 components using the Bayesian Best Worst Method (BWM) and to reveal their corresponding credal ranking levels. In this context, the present study aims to evaluate and prioritize the critical industry 4.0 components influencing technological transformation processes using the Bayesian BWM. Bayesian BWM is preferred over alternative Multi Criteria Decision Making (MCDM) approaches due to its ability to explicitly model uncertainty within a probabilistic framework, generate more consistent weighting results, and flexibly incorporate decision-makers’ judgments. The findings reveal that safety and security (0.2945) constitute the most influential main component, underscoring the necessity of robust digital infrastructures and reliable systems within highly digitalized operational environments. Among the sub-components, data privacy (0.1301) demonstrates the highest global weight, highlighting the growing importance of safeguarding sensitive information in data-intensive digital systems. The results further indicate that autonomous operation and coordination play significant roles in facilitating efficient digital operations, particularly through real-time equipment monitoring and IoT-based operational visibility. Moreover, sustainability (0.1968) emerges as the second most important component, suggesting that organizations increasingly assess technological investments not only in terms of operational efficiency but also with respect to long-term resilience. Within this dimension, continuous training (0.0614) is identified as the most influential component, indicating that the success of digital transformation depends not only on technological infrastructure but also on the development of human capabilities. With the increasing digitalization of the maritime industry, protection against cyber threats has become essential for ensuring operational continuity and safeguarding data integrity. In this regard, adopting proactive cybersecurity strategies and continuously monitoring and updating systems are of critical importance. In the digital transformation of maritime transportation, integrating sustainability considerations is essential to ensure long-term operational efficiency and environmental responsibility. These practical implications are particularly relevant for policymakers, port authorities, and shipping companies seeking to enhance both digital capabilities and sustainable performance.

Marine pollution in coastal regions is a growing environmental concern, demanding integrated strategies for sustainable coastal management and spatial planning. This study assesses the spatial intensity and inland sources of marine pollution along the Visakhapatnam coast, extending from Bheemunipatnam in the north to Gangavaram in the south. A novel marine pollution index (MPI) was developed using a 15 km offshore buffer zone by integrating five marine parameters Chlorophyll-a, total suspended matter (TSM), sea surface temperature (SST), proximity to industrial and port areas, and proximity to drainage systems. All datasets were harmonized to a 1 km spatial resolution and objectively weighted using the Best Worst Method (BWM), a multi-criteria decision-making approach. Results identify proximity to drains/canals (0.4158) and industrial/port areas (0.2366) as the dominant contributors to marine pollution, followed by TSM (0.1577), Chlorophyll-a (0.1183), and SST (0.0717). To link marine impacts with inland drivers, a complementary geospatial analysis incorporating seven terrestrial parameters was conducted. The results reveal Visakhapatnam Urban as the primary source region, followed by Pedagantyada and Gajuwaka, while Bheemunipatnam shows minimal influence. This integrated marine-terrestrial framework offers a transferable decision support tool for identifying pollution hotspots and guiding targeted coastal management interventions.

Congestion in urban transportation is a significant challenge, often exacerbated by increasing private vehicle use and limitations in public transport. This study introduces a two-stage approach combining multi-criteria assessment and traffic simulation to examine current conditions and propose improvements. Initially, data on five primary and twenty-one secondary factors affecting public transport choice are assessed using the Best–Worst Method (BWM). The findings reveal that convenience is prioritized by working professionals, while travel cost is most important to students. A baseline simulation model is established using a case study at Kaset Intersection in Bangkok. Incorporating weighted preferences into the simulation aims to enhance public transport and encourage private car users to switch modes through potential traffic management policies. Additionally, a micro-simulation assesses the impacts of decreased traffic density, revealing that a reduction in traffic density can shorten overall travel time by about 2.04 s, based on regression analysis. The results suggest policies to improve public transport, reduce traffic density, and enhance urban transport system performance.

This study aimed to prioritize the key success factors influencing the professional development of elementary school teachers in the Department of Education Region VIII, addressing the absence of a context specific and empirically grounded prioritization framework. Although prior studies have identified various factors affecting teacher professional development, most examine these elements descriptively and provide limited guidance on how they should be strategically prioritized in practice. This study contributes to the literature by introducing a decision oriented framework that applies the fuzzy Best–Worst Method (FBWM) to systematically determine the relative importance of professional development factors within an educational leadership context. Using FBWM within a multi criteria decision making approach, fifteen professional development success factors were evaluated based on expert judgment. Data were obtained from nine highly qualified Principal IV practitioners holding doctoral degrees who provided pairwise fuzzy comparisons to establish relative importance and consistency. The results identified administrative support, professional learning communities, continuous professional learning, evaluation and feedback mechanisms, and access to resources as the most influential drivers of effective professional development. Factors such as cultural competence training, research engagement, and customized development plans received lower priority, indicating areas requiring strategic strengthening. By translating conceptual professional development constructs into quantifiable priority weights, the study offers a structured decision support tool for education leaders and contributes to more evidence informed professional development planning in elementary education systems.

Chemical supply disruptions can compromise compliance with water treatment regulations and service continuity. This study proposes an integrated multi-criteria decision-making framework that combines the Best–Worst Method (BWM) and VIKOR to prioritize mitigation strategies for water treatment chemical supply chain disruptions. A case application at Shanghai’s Yangshupu Water Plant demonstrates the approach. BWM-based weighting shows that compliance/public health (0.26) and service continuity (0.18) dominate decision priorities (44% combined), followed by recoverability/flexibility (0.16) and supply vulnerability (0.14). Using these weights, VIKOR ranks mitigation alternatives and identifies a compromise set. Under baseline conditions ($$\:v=0.5$$), dual sourcing and supplier prequalification achieve the best compromise performance ($$Q=0.083$$, $$\:S=0.563$$), while safety stock and reorder redesign minimize worst-case regret $$(R=0.140)$$; the acceptable advantage condition is not satisfied ($$0.135<0.167$$), leading to a compromise set $$\:\left\{A1,A2\right\}$$. Sensitivity and scenario tests confirm that the shortlist is robust, with safety stock becoming top-ranked under prolonged logistics disruption and QA/QC strengthening rising under quality failures. The proposed framework provides transparent, defensible support for utility resilience planning.

The European Union dominates global wine exports and production. The EU’s wine sector is primarily supported by the Common Agricultural Policy (CAP) and Geographical Indications systems (GIs). On an international point of view, the sector is experiencing remarkable economic repercussions due to US tariffs. In order to overcome the identified challenges, it is crucial for wineries to implement a tailored sales distribution strategy, particularly for small wineries. The distribution landscape for small wineries is characterized by limited resources, diverse channel options and rapidly changing market conditions, making the selection of an optimal mix both complex and critical for profitability and resilience. This study formulates the choice of distribution strategy as a multi criteria decision making (MCDM) problem and introduces a hybrid framework that combines the Best–Worst Method (BWM) for deriving consistent criterion weights with the novel Axial Distance based Aggregated Measurement (ADAM) technique for robust alternative ranking. Seven evaluation criteria, economic profitability, resource availability, implementation feasibility, strategic alignment, market opportunity, competitive advantage, and flexibility, are applied to five distribution strategies: direct sales; online and social media channels; local partnerships; distributor partnerships; and participation in festivals and events. Expert assessments generate the decision matrix and weight vectors, yielding a final ranking that places local partnerships highest, followed by direct sales, online channels, distributor partnerships, and festivals. The results demonstrate the value of community-based collaborations and experiential marketing, while the hybrid MCDM approach offers a transparent, adaptable tool for strategic decision-making. Limitations linked to expert subjectivity and criterion scope are discussed, and avenues for incorporating sustainability and dynamic updates are outlined.

High-quality datasets are increasingly recognized as foundational inputs to economic development, industrial upgrading, and public governance. A rigorous evaluation system for data asset quality is therefore needed to improve data governance and to enable value realization in circulation. Focusing on three representative circulation scenarios—data interaction, data exchange, and data trading—this study develops an indicator system from technical, business, and benefit-oriented dimensions. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to identify causal relationships among indicators and key drivers. To integrate multi-expert judgments under uncertainty, hesitant linguistic variables and evidence theory are adopted, and the Best–Worst Method (BWM) is applied to derive more consistent indicator weights. The resulting weights are combined with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to obtain a comprehensive ranking of data asset quality with scenario-adjustable emphasis. A traffic-flow dataset from a data technology enterprise is used to demonstrate applicability and effectiveness. The proposed framework advances scenario-adaptive data quality evaluation and supports enterprise data governance, data transaction pricing, and the implementation of high-quality dataset policies.

This study investigates strategic decision-making for integrating artificial intelligence–based predictive safety systems into occupational health and safety (OHS) management. The aim is to develop and apply a rigorous, transparent multi-criteria decision framework that helps organizations select among competing AI-driven safety solutions under uncertainty. The core research question is: Which AI-based predictive safety alternative offers the best balance of safety improvement, organizational feasibility, and strategic fit for OHS management? An integrated fuzzy MCDM approach combines Fuzzy Best–Worst Method (FBWM) to elicit criterion weights with MARCOS to prioritize alternatives evaluated by domain experts across technical performance, human factors, legal/regulatory fit, cost, and implementation readiness. The analysis highlights the dominant influence of safety impact and technological readiness on final rankings, while cost and legal compliance act as moderating considerations. Sensitivity tests across weighting schemes indicate stable priority orders without critical rank reversals, supporting managerial robustness. The findings provide actionable guidance for investment and OHS committees, demonstrate the practicality of a hybrid fuzzy model for high-risk settings, and clarify both the study’s aim and its central research question for future replications.

Enhancing sustainability in urban areas relies heavily on achieving an optimal increase in building density. However, identifying suitable sites for high-rise urban developments could be challenging. One approach to tackle this problem involves the utilization of multi-indicator land suitability analysis in the process of land-use planning. This approach plays an essential role in mitigating the fragmentation of natural habitats, promoting social integration, reducing air pollution, and preserving water and natural resources at both local and regional levels. This study aims to evaluate infill land suitability for high-rise urban development in Ahvaz, Iran. Methodologically, this study introduces an integrated Boolean–fuzzy geographic information systems framework combined with the fuzzy best–worst method, which enhances decision reliability by systematically handling uncertainty in expert judgments while reducing inconsistency compared to conventional weighting methods such as analytic hierarchy process. The findings indicate that approximately 54 percent of the city’s land areas are unsuitable for high-rise urban development. Furthermore, among the remaining suitable areas, about 30 percent were identified as priorities for high-rise development. Priority areas are primarily located in densely populated neighborhoods with higher socioeconomic status and better access to services. This study is significant because analyzing the suitability of urban land plays a crucial role in formulating effective land-use policies that promote sustainable urban development. It also helps prioritize management actions and identify spatial development patterns that yield maximum efficiency.

Underground tunnel construction projects using tunnel boring machines (TBMs) require a holistic risk perspective. Such projects face various risks arising from social, economic, political, workforce, and regulatory aspects during project execution. It is necessary to develop preventive strategies for managing these risks and thereby ensure timely project delivery, cost efficiency, and safety. In this study, we aimed to develop a comprehensive hybrid decision-making framework for analyzing risks in TBM-based tunnel construction projects. The proposed approach integrates the best–worst method (BWM), data envelopment analysis (DEA) model-based risk assessment, and the preference ranking organization method for enrichment evaluation (PROMETHEE). The BWM was applied to determine the weights of decision criteria with fewer comparisons and improved consistency. Subsequently, the DEA model was then used to compute local risk scores under multiple input and output conditions. Finally, PROMETHEE was employed to analyze the risks based on positive and negative outranking flows. The proposed approach was applied to a realistic metro construction project in Bangkok. The findings indicated that the proposed approach effectively compromised all the decision-making attributes to manage the uncertainties. The proposed methodology can support project managers, stakeholders, engineers, and relevant authorities in identifying high-priority risks and implementing effective mitigation strategies to enhance risk management in tunnel construction.

ABSTRACT Interest in electric vehicles has increased significantly due to energy efficiency and carbon emission reduction goals within smart urbanization strategies. Determining the optimal locations of electric vehicle charging stations (EVCS) is therefore a critical decision problem, as limited stations must efficiently serve users while minimizing access time. This study proposes an integrated analytical approach for EVCS location planning. In the first stage, the Best–Worst Method (BWM) is employed to identify the most appropriate criteria for EVCS placement. In the second stage, a game theory model is developed using expert-evaluated factors, where strategies are formed through pairwise combinations of these factors. In the third stage, an objective function is defined to minimize access costs by optimizing the number of charging stations under increasing demand conditions. Due to conflicting objectives in EVCS network design, a multi-objective programming approach is applied. Scenario analyses demonstrate the robustness and practical applicability of the proposed approach.

Under the restructuring of global industrial chains, enhancing the resilience and sustainability of aquatic products supply chains has become a critical priority for industrial upgrading. These supply chains face mounting challenges from environmental pressures, public health incidents, market volatility, and shifting policy landscapes. Adopting a synergistic perspective that integrates resilience and sustainability, this study develops a comprehensive evaluation indicator system encompassing seven dimensions: production, processing, circulation, sales, consumption, external environment, and services. Through the Delphi method, the Best–Worst Method (BWM), and fuzzy comprehensive evaluation, eight key indicators are identified, including Scientific Control of Aquatic Products Catching and Feeding (A2), Inventory Quality Assurance and Adjustment (D2), Policy and Regulations (F1), and Logistics Technology Level (C2). An empirical assessment of three representative enterprises—Company A (China Aquatic), Company B (ASIASEA Group), and Company C (Dalian Zhihui Fishery Group)—demonstrates that Company A achieves the highest comprehensive score (87.76, rated as strong), while Company B (81.23) and Company C (77.42) are both rated as relatively strong. Comparative analysis reveals differentiated weaknesses, such as logistics digitalization gaps and market negotiation limitations, leading to targeted recommendations in upstream production optimization, logistics technology investment, policy engagement, and market capability enhancement. This research contributes to the literature by establishing an empirically validated, multidimensional framework for assessing and guiding the coordinated upgrading of global aquatic products supply chains, offering actionable insights for both practitioners and policymakers.

The shipping industry plays a critical role in global trade, but it also faces major sustainability challenges that affect important global goals like SDG 14 (Life Below Water) and SDG 13 (Climate Action). This study creates a framework to promote sustainable change using Industry 4.0 technologies, such as IoT and Blockchain. The approach includes a literature review and expert consultation to identify and categorize 36 technological enablers across four areas: environmental, social/organizational, managerial, and supply chain. To rank these enablers, we used the Robust Best Worst Method (RBWM), and we validated the results with machine learning classifiers to ensure the framework's reliability and accuracy. Our key findings show that renewable energy integration, eco-friendly design, blockchain applications, and planning algorithms are the most important factors for sustainability. This new framework provides practical guidance for policymakers and industry stakeholders to improve sustainable practices. It aims to support informed investment and operational choices, especially in developing economies where Industry 4.0 adoption is not fully explored.

Background: Supplier selection in shipbuilding is a high-stakes decision problem due to stringent quality requirements, compressed construction schedules, and elevated project risks. This study develops a systematic decision-support framework for selecting shipbuilding material suppliers while enhancing supply-chain resilience. Methods: A hybrid multi-criteria decision-making framework integrating the Best Worst Method (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed. BWM is used to derive consistent criteria weights with fewer pairwise comparisons, while TOPSIS ranks supplier alternatives based on their distances from ideal and negative-ideal solutions. Results: Quality emerges as the most influential criterion (weight = 0.460), followed by risk-related factors, underscoring the importance of compliance, reliability, and risk mitigation in shipbuilding procurement. The TOPSIS results indicate that Supplier 3 achieves the highest closeness coefficient (Ci = 0.592), followed by Supplier 4, Supplier 2, and Supplier 1, with strong consistency observed in expert judgments. Conclusions: The proposed BWM–TOPSIS framework is rigorous, transparent, and replicable, supporting a Quality–Risk-Oriented multi-sourcing strategy to enhance supply continuity and operational resilience.