Fuzzy Decision Making Trial And Evaluation Laboratory Research Articles

Unlocking net-zero supply chain performance system through life cycle assessment: Empirical evidence from rubber industry

The ongoing transition from a supply chain (SC) to net-zero emission (NZE) in SC performance must align with Sustainable Development Goal 13 to address climate change using life cycle assessment (LCA). Effective management of LCA significantly impacts an organization's competitiveness. With a lack of technology oversight and its connections with other enabling parameters that increase competitiveness, practices in the manufacturing industry remain limited. This study identifies and evaluates key enablers for LCA adoption in the manufacturing industry to achieve NZE in SC. Based on 123 responses, an exploratory factor analysis (EFA) was employed, followed by the Best-Worst Method (BWM). Further, a Fuzzy-Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) was used. The results revealed ‘organizational integration’, ‘data availability and quality, and ‘policy and regulation’ as significant group enablers that support NZSC to compete in the market. It provides insights for industry practitioners and policymakers on optimizing sustainable SC practices. The essential practical recommendations for resource optimization and carbon footprint reduction are also suggested.

A Generalized Method for Deriving Steady-State Behavior of Consistent Fuzzy Priority for Interdependent Criteria

Interdependent criteria play a crucial role in complex decision-making across various domains. Traditional methods often struggle to evaluate and prioritize criteria with intricate dependencies. This paper introduces a generalized method integrating the analytic network process (ANP), the decision-making trial and evaluation laboratory (DEMATEL), and the consistent fuzzy analytic hierarchy process (CFAHP) in a fuzzy environment. The Drazin inverse technique is applied to derive a fuzzy total priority matrix, and we normalize the row sum to achieve the steady-state fuzzy priorities. A numerical example in the information systems (IS) industry demonstrates the approach’s real-world applications. The proposed method derives narrower fuzzy spreads compared to the past fuzzy analytic network process (FANP) approaches, minimizing objective uncertainty. Total priority interdependent maps provide insights into complex technical and usability criteria relationships. Comparative analysis highlights innovations, including non-iterative convergence of the total priority matrix and the ability to understand interdependencies between criteria. The integration of the FANP’s network structure with the fuzzy DEMATEL’s influence analysis transcends the capabilities of either method in isolation, marking a significant methodological advancement. By addressing challenges such as parameter selection and mathematical complexity, this research offers new perspectives for future research and application in multi-attribute decision-making (MADM).

An integrated group decision support framework utilizing Pythagorean fuzzy DEMATEL–CoCoSo approach for medicine cold chain logistics provider selection

PurposeThe purpose of this study is to establish a synthetic group decision framework based on the Pythagorean fuzzy (PF) set to select the optimal medicine cold chain logistics provider (MCCLP). Fierce market competition makes enterprises must constantly improve every link in the process of enterprise sustainable development. The evaluation of MCCLP in pharmaceutical enterprises is an important link to enhance the comprehensive competitiveness. Because of the fuzziness of expert cognition and the complexity of the decision procedure, PF set can effectively handle the uncertainty and ambiguity in the process of multi-criteria group decision decision-making (MCGDM).Design/methodology/approachThis paper develops an integrated group decision framework through combining the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique and combined compromise solution (CoCoSo) approach to select a satisfactory MCCLP within PF circumstances. First, the PF set is used to process the ambiguity and uncertainty of the cognition ability of experts. Second, a novel PF knowledge measure is propounded to measure the vagueness of the PF set. Third, a comprehensive criterion weight determination technique is developed through aggregating subjective weights attained utilizing the PF DEMATEL approach and objective weight deduced by knowledge measure method. Furthermore, an integrated MCGDM approach based on synthetic weight and CoCoSo method is constructed.FindingsThe outcomes of sensibility analysis and comparison investigation show that the suggested decision framework can help decision experts to choose a satisfactory MCCLP scientifically and reasonably. Accordingly, the propounded comprehensive decision framework can be recommended to enterprises and organizations to assess the MCCLP for their improvement of core competitiveness.Originality/valueMCCLP selection is not only momentous for pharmaceutical enterprises to improve transportation quality and ensure medicine safety but also provides a strong guarantee for enterprises to improve their core competitiveness. Nevertheless, enterprises face certain challenges due to the uncertainty of the assessment environment as well as human cognition in the process of choosing a satisfactory MCCLP. PF set possesses a formidable capability to address the uncertainty and imprecision information in the process of MCGDM. Therefore, pharmaceutical enterprises can implement the proposed method to evaluate the suppliers to further improve the comprehensive profit of enterprises.

Data-driven natural capital accounting model in Indonesia: Impacts of environmentally related economic activities on ecological processes and services

In this study, natural capital accounting (NCA) is enhanced by constructing a data-driven hierarchical model that can help improve policy decision-making. NCA enables the management and transparency of natural capital and ecological processes and services, resulting in better societal activities and well-being in Indonesia's regions; however, it does not account for environmentally related economic activities that may negatively influence natural capital, processes and services. In this study, a data-driven approach is used to address this issue. Valid attributes are identified, and a thorough NCA hierarchical model that incorporates synchronized accounting on ecology, environment-related economic activities and societal well-being is developed. This approach utilizes content and bibliographic analyses, the entropy weighted method (EWM) and the fuzzy Delphi method (FDM), which are used to validate the data-driven NCA measurements. Exploratory factor analysis (EFA) is utilized to cluster the measurements and assess their reliability. The fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is used to construct the NCA hierarchical structure by considering the cause-and-effect interrelationships among attributes. The valid prioritized attributes are determined, and a hierarchical structure is built to support government decision-making processes. The findings suggest that water resource accounting, forestry production, raw material extraction, resource allocation management and resource consumption management are the criteria that fluctuate the most for practitioners and governments.

Critical success factors for the adoption of AVs in sustainable urban transportation

Before integrating Autonomous Vehicles (AVs) into transportation systems, it's important to evaluate various aspects to facilitate their adoption. Numerous factors are likely to be crucial in this process. This research investigates the critical success factors influencing the adoption of AVs in sustainable urban transportation through a hybrid fuzzy Multiple-Attribute Decision-Making (MADM) approach. The primary aim is to identify key criteria and sub-criteria perceived by transport experts, shedding light on the complex dynamics of AV adoption. Methodologically, a fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) is employed to unveil cause-and-effect relationships among the main criteria, determining their respective weights. Subsequently, fuzzy Analytic Network Process (ANP) is applied to calculate the weights of criteria and sub-criteria based on the fuzzy DEMATEL results. The study considers seven major factor dimensions: economic, technical, operational, environmental, safety and risk, social and regulatory, and user acceptance. Key findings underscore user acceptance, financial costs, and environmental impact as the most pivotal criteria, emphasising their significance in the eyes of transportation experts. Contrarily, technological and operational criteria hold the lowest weight in the considered criteria for AV adoption in transportation networks. This research provides valuable insights into critical barriers and opportunities in the adoption of AVs, contributing to the advancement of sustainable urban transportation.

Identifying Key Barriers to Green Transition Development in China’s Express Industry Based on the Fuzzy DEMATEL Method

The “green transformation of the express” delivery industry in this study primarily refers to the adoption of green energy and environmentally friendly technologies in express delivery packaging, transportation, and recycling systems. This transformation can significantly enhance energy efficiency and reduce emissions in the express system, promoting the sustainable development of the entire industry. However, the progress of green transformation in China’s express delivery industry has been impeded by various barriers. To address this, we propose a barrier analysis framework based on the Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology to explore key obstacles to green transformation in the express industry. Our research aims to answer three main questions: (1) What are the key barriers to green transformation in China’s express delivery industry? (2) How do these barriers interact and influence each other? (3) What strategic measures can be implemented to overcome these barriers? We first compile a list of barriers, innovatively proposing two new ones: “inadequate green standards in the express industry” and “suboptimal green packaging technology”. Considering the ambiguity in expert input and the complex interactions among barriers, we employ fuzzy DEMATEL within an Interval Type 2 Fuzzy Sets (IT2FSs) environment to investigate the significance and causality of these barriers. This approach distinguishes our study from previous research by providing a more nuanced understanding of barrier interactions in the specific context of China’s express delivery industry. Based on our analysis, we identify eight critical barriers and propose corresponding strategic measures.

Navigating critical barriers for green bond markets using A fuzzy multi-criteria decision-making model: Case study in Vietnam

As potent financial instruments channeling capital into climate and environmental projects, green bonds' issuance is frustrated by myriad obstacles. Therefore, this study aims to explore the critical barriers to green bond issuance in Vietnam. This study elucidates the salient barriers by utilizing an integrated multi-criteria decision-making methodology synthesizing the Fuzzy set theory-based Delphi, Decision-Making Trial and Evaluation Laboratory (DEMATEL), and DEMATEL-based Analytic Network Process (DANP). An extensive literature review of green bonds delineates five key dimensions related to impediments: Policy, Market, Financial, Capacity, and Awareness, with 38 discrete indicators representing potential obstacles. Firstly, employing a Fuzzy Delphi survey of 16 experts, 32 indicators are distilled for further analysis. Then, Fuzzy DEMATEL modeling evaluates the interrelationships and interactions among barriers. Finally, DANP is applied to obtain the relative importance of key barriers. Results unveil the five most formidable barriers as a weak regulatory framework and infrastructure (PO1), Limited availability of green bond issuance guidelines and templates (PO2), Insufficient incentives or tax benefits for green bond issuers (PO3), Limited coordination and alignment with international green bond standards (PO5), Lack of investor confidence in the quality and credibility of green projects (AW4), and Capacity constraints among issuers, particularly in smaller organisations or government agencies (CA1). The research contributes to the broader literature on green finance and offers valuable insights for promoting sustainable finance practices in Vietnam and other developing economies.

Embracing Sustainability in Green Supplier Evaluation: A Novel Integrated Multi-Criteria Decision-Making Framework

Nowadays, industrial production is a critical issue for companies as public consciousness is rising because of environmental impacts. Like most other industries, the ready-made garment (RMG) industry faces immense pressure to be more competitive in this increasingly competitive market and proactively reduce waste and ecological footprint impacts. Besides, suppliers’ environmental performance and image have far more effect on a corporation’s environmental sustainability than the corporation’s internal environmental efforts. Moreover, there is a shortage of environmental consciousness in emerging economies; thus, green supply chain practices are lagging. Therefore, the green supplier evaluation criteria should be identified and focused on during sustainable procurement. This study analyses quantitative and qualitative factors and explores the relationship between green supplier preference principles using the combination of the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS). Initially, the supplier’s green performance was measured by identifying the criteria from relevant literature. After that, the Fuzzy DEMATEL technique measures the related criteria weights, and the Fuzzy TOPSIS method utilizes these criteria weights to rank suppliers among alternative suppliers. The findings will guide policymakers to improve supplier quality, increase profitability through improved brand recognition, and attract consumers who prioritize environmentally friendly products.

Heuristic biases influencing individual stock investment decisions: a hybrid fuzzy DELPHI-AHP-DEMATEL approach

PurposeStock investing choices of individual investors are predominantly influenced by heuristic biases, leading to sub-optimal choices. Accordingly, this study aims to identify, categorize, validate, prioritize, and find causality among the heuristic biases shaping stock investment decisions of individual investors.Design/methodology/approachThis research offers original contribution by employing a hybrid approach combining fuzzy DELPHI method (FDM), fuzzy analytical hierarchy process (FAHP), and fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) techniques to validate, prioritize, and find causality among the heuristic biases.FindingsTwenty sub-heuristic biases were identified under five main heuristic bias categories. Out of which, 17 were validated using FDM. Further, availability and representativeness within main heuristic categories, and availability cascade and retrievability within sub-heuristic biases were prioritized using FAHP. Overconfidence and availability were identified as the causes among the five main biases by F-DEMATEL.Practical implicationsThis study offers the stock investors a deeper understanding of heuristic biases and empowers them to make rational investment decisions.Originality/valueThis paper is the inaugural effort to identify, categorize, validate, prioritize and examine the cause-and-effect relationship among the heuristic biases.

Sustainable food supply chain: exploration, identification, and analysis of the critical drivers for the foxnut (Makhana) industry

Purpose The purpose of this paper is to recognize and prioritize the critical drivers (CDs) essential for establishing a sustainable foxnut supply chain (SFNSC) aligned with the sustainable development goals (SDGs) set forth by the United Nations. The objective is to make a meaningful contribution to the longevity and well-rounded sustainability of the foxnut industry by scrutinizing pivotal factors that endorse triple bottom line (TBL) sustainability aspect throughout the supply chain. Design/methodology/approach A systematic approach, integrating literature reviews and government reports, identified potential CDs for a sustainable foxnut supply chain. Expert opinions refined the list with the help of fuzzy-Delphi method (FDM), and the final CDs were analyzed with fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) to establish their causal relationships and hierarchical importance. Findings The study identifies the top three CDs for a SFNSC: “Branding of the product”, “The Global increase in demand” and “Value addition of the foxnut”. Moreover, “Storage infrastructure”, “Mechanized processing” and “Proper transportation facilities” also contribute to the sustainability of the foxnut supply chain. Research limitations/implications The results hold significance for various stakeholders in the foxnut industry, encompassing producers, policymakers and researchers. The identified CDs can guide decision-making and resource allocation to improve the sustainability of the foxnut supply chain. The study's framework and methodology can also be applied to other industries to promote sustainable practices and achieve SDGs. Originality/value This study enhances understanding of CDs for an SFNSC. FDM and F-DEMATEL techniques analyze causal relationships and rank key factors. The SFNSC model may help other major foxnut producers to become more sustainable.

A comprehensive approach to enhance wood cutting productivity: Integration of spherical fuzzy DEMATEL and artificial neural networks

Productivity plays a pivotal role in profitability and success of business. In this study, the wood cutting activity in Indian sawmills is selected. This study replicates the novel approach by integrating spherical fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) and artificial neural networks (ANN) to improve the wood cutting productivity in Indian sawmills. The measure of betterness is selected as net productivity rate (NPR), a time-based labor productivity measure. The methodology unfolds in two crucial steps. First, SF-DEMATEL is employed to unearth influential factors affecting wood cutting, delving into their interrelationships through fuzzy logic. This process provides relationships between key determinants and their interconnected dynamics. Secondly, an ANN, a machine learning algorithm, is harnessed to predict wood cutting performance based on these identified factors. The ANN is trained using historical or simulation data, paving the way for predictions under diverse scenarios. The novelty of this approach lies in its holistic precision. The results showcase that lifting index and log weight emerge as primary influencers on productivity, with NPR, occupational risk index, and perceived exertion ranking lower. In the grand tapestry of factors, the study unveils universal driving forces, such as the weight of the log and lifting index. The ANN model, attaining a remarkable RMSE = 0.0478 and R2 = 0.9783 for training set and for training data and RMSE = 0.0487 and R2 = 0.9727 for testing data. This contributes to the comprehensive ranking comparison of factors derived from both Fuzzy DEMATEL and ANN. In summation, the fusion of Fuzzy DEMATEL and ANN unravels the intricacies of wood cutting dynamics. By identifying key factors and predicting performance, this approach provides a transformative gateway to enhance wood cutting quality and efficiency, thereby elevating the overall productivity of the woodworking industry.

Identification of Key Drivers for Performance Measurement in Sustainable Humanitarian Relief Logistics: An Integrated Fuzzy Delphi-DEMATEL Approach

Sustainable humanitarian relief logistics (SHRL) is gaining attention due to increased disasters, unpredictable demand, large volumes, high delivery stakes, and limited resources, evaluated through adaptable performance drivers. This study presents a novel hybrid framework for SHRL, combining the Fuzzy Delphi Method (FDM) and Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL). Initially, FDM is utilized to reach a consensus among experts concerning key performance indicators (KPIs) for humanitarian logistics and supply chains. By incorporating the inherent uncertainty and vagueness in expert judgments, FDM refines the list of key performance indicators that reflect the real-life conditions and constraints in disaster operations. Finally, the fuzzy DEMATEL approach was used to analyze the interrelationships among factors, identifying cause-and-effect behavior and ranking them, forming a robust theoretical framework. Based on the acquired results, the KPIs attached to the Quality (P1) aspect of the proposed framework have gained significant importance and are the main cause in a cause-and-effect relationship which impacts and is helpful to improve the performance of humanitarian organizations in all phases of disaster management. The KPIs prompt delivery (D1), and delivery accuracy (D2) are more significant, while capacity building and training (D19) and delivery compliance (D15) are least significant in SHRL scenarios. This research is expected to support humanitarian organizations in enhancing their capabilities, thereby improving the effectiveness and efficiency of aid delivery in disaster-stricken areas.

Prioritising Yarn Quality Via Varying Top Roller Hardness: A Fuzzy Application

In recent years, the spinning or yarn manufacturing sector has seen remarkable technological advancements, emphasizing high-quality yarn production, especially for cotton combed ring spun yarns. In the case of better quality ring spun combed yarn, each part of the ring frame is significant. To fulfil this issue, this study focuses on understanding the impact of the drafting roller hardness on cotton-combed ring-spun yarn quality. It delves into how ten top and bottom roller hardness combinations influence yarn quality parameters. The cotton combed yarns underwent rigorous testing after spinning on ring frames with varying drafting roller hardness. Key quality parameters studied included yarn Imperfection Index (IPI), Unevenness (Ue%), Hairiness (Hi), Elongation at break (Eb%), and Count Strength Product (CSP), and Tenacity (Te). The primary insight was that extreme roller hardness adversely affects all parameters except Elongation at the break value obtained for the shore hardness value of 80/83. Yarns produced using medium-hardness rollers showcased fewer imperfections due to more consistent fibre flow and minimized drafting slippages, resulting in an even twist. These findings are pivotal for the yarn manufacturing industry, hinting at potential refinements in production methods. The usage of the Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) methodology also determined that Te CSP) and IPI values significantly influence the combed yarn quality when adjusting roller hardness, whereas Hi is the least influential. Therefore, this study offers twofold insights: determining the optimal roller hardness and identifying the most affected quality parameters.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

PurposeIn order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.Design/methodology/approachFirst, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.FindingsThe cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/valueThis study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

Are we ready for metaverse adoption in the service industry? Theoretically exploring the barriers to successful adoption

The Metaverse has emerged as a convergence of technologies and conceptions with the potential to reshape how we interact, participate, learn, and entertain ourselves in the digital age. It has gained substantial attention recently due to its immersive, interconnected, and virtual environment for meeting evolving customer and market demands. As technology advances, firms implementing the Metaverse may gain a competitive edge and create new revenue streams. However, the service sectors still face challenges in adopting and successfully implementing Metaverse. Therefore, we attempted to establish a theoretical foundation to understand the challenges of Metaverse adoption in the service sector and to develop an integrated framework to analyse the barriers using the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Decision-making trial and evaluation laboratory (F-DEMATEL) techniques. To achieve this, we first identify barriers from extensive literature and categorise them into six broad categories based on experts’ opinions. Then, we ranked each category and the barriers constituting them based on their criticality through FAHP. The Security and Privacy category has the highest weight among the six categories of barriers, followed by Governance and Standardisation, Infrastructure, User Behaviour and psychology, Feasibility, and Organisational culture and Stakeholder commitment. Further, we developed the causal-effect relationships among the six categories and analysed those categories through the degree of prominence and relationships. We found that Security and Privacy is one of the most influencing barriers. Then, we analysed our proposed decision-making model under different scenarios through sensitivity analysis. This study offers valuable insights into the relationships among the barriers and how they can impact progress towards achieving specific goals or objectives.

Driving factors for the adoption of green finance in green building for sustainable development in developing countries: The case of Ghana

AbstractWhile there are many motivating factors for green finance (GF) implementation, a comprehensive taxonomy of these variables is lacking in the literature, especially for green buildings (GBs). This study aims to analyze the criticality and interdependence of GF‐in‐GB's driving factors. This study develops a valid set of factors to justify the interrelationships among the drivers. The drivers of GF‐in‐GB are qualitative in nature, and uncertainties exist among them due to linguistic preferences. This study applies the fuzzy Delphi method to validate eight drivers under uncertainties. Fuzzy Decision‐Making Trial and Evaluation Laboratory (FDEMATEL) with qualitative information is used to determine the interrelationships among the drivers. The drivers were grouped under two categories: prominent drivers and cause‐effect drivers. The findings revealed that “increased awareness of GF models in GB” and “preferential capital requirements for low‐carbon assets” are the top two most prominent/important drivers of GF‐in‐GB. In Ghana, the top three cause group drivers are “climate commitment,” “improved access to and lower cost of capital,” and “favorable macroeconomic conditions and investment returns.” Drivers with the highest prominence values have the potential to affect and/or be affected by other drivers; therefore, managers and policymakers should prioritize promoting or pursuing these drivers in the short term. On the other hand, it is important to pay more than equal attention to the drivers with the highest net cause values because they have the largest long‐term impact on the entire system. The theoretical and practical implications of the study are discussed, enhancing understanding and decision‐making in GF‐in‐GB.

Assessing and prioritizing drivers and strategies for transition to a green energy for sustainable development in China

This study addresses the critical global challenge of climate change, primarily caused by the overconsumption of fossil fuels. Recognizing the urgent need for a transition to green energy (GE) sources such as wind, solar, hydro, and biomass, this research focuses on identifying effective strategies for fostering a sustainable and low-carbon energy future in China. The study employs a combination of the Analytical Hierarchy Process (AHP) and fuzzy Decision-making Trial and Evaluation Laboratory (DEMATEL), both well-established multi-criteria decision-making (MCDM) methods, to analyze various drivers, sub-drivers, and strategies crucial for this transition. Through an extensive literature review, we identified several key drivers and strategies aiding the shift towards GE for sustainable development. The AHP method was applied to evaluate and rank the major drivers and sub-drivers, such as policy, financing, and infrastructure and innovation, that are crucial for China's successful transition to GE. Simultaneously, fuzzy DEMATEL was utilized to prioritize vital strategies, including public awareness and education, financial incentives and support mechanisms, and policy and regulatory frameworks. The findings reveal that, in addition to strong policy and financial support, public awareness and education are critical for advancing GE development in China. This study underscores the importance of integrating various drivers and strategies for effective green energy development, aiming to mitigate the environmental impacts of fossil fuel use.

Analyzing the influential factors of process safety culture by hybrid hidden content analysis and fuzzy DEMATEL

Due to the complex nature of safety culture and process industries, several factors influence process safety culture. This paper presents a novel framework that combines the hidden content analysis method with Decision Making Trial and Evaluation Laboratory (DEMATEL) and Fuzzy logic to achieve a comprehensive set of influential factors and their relationship. The proposed methodology consists of two primary stages. Firstly, combined methods of literature review and Delphi study were used to identifying influential factors of process safety culture. Secondly, the Fuzzy-DEMATEL approach is employed to quantify and determine the relationships between different influential factors. A diverse pool of experts’ opinions is leveraged to assess the impact of each factor on others and process safety culture. In the first stage, 18 factors identified as influential factors on process safety. The findings of second stage revealed that eight variables were identified as causes, while ten variables were classified as effects. Also, the Organization management's commitment to safety factor had the greatest influence among all of the factors. As well as, the most significant interaction was associated with the risk assessment and management aspect. The integrated approach not only identified the influential factors, but also elucidates the cause-effect relationships among factors. By prioritizing factors and understanding their interconnections, organizations can implement targeted safety measures to improve process safety culture. Its effectiveness in quantifying qualitative data, identifying influential factors, and establishing cause-effect relationships make it a valuable tool for enhancing safety culture in process industries.

Analysis of Factors Affecting Space Teleoperation Safety Performance Based on a Hybrid Fuzzy DEMATEL Method

Teleoperation is one of the highest-risk activities in manned space activities. An appropriate set of influencing factors is essential for the safety assessment of teleoperation. The examination of the impact of a single factor or two factors on space teleoperation as done in the existing works suffers from the lack of a systematic analysis of factors. To address this gap, a hybrid α-level set incorporated in the hesitant fuzzy decision making trial and evaluation laboratory (DEMATEL) method was adopted in this study to identify and analyze teleoperation factors. The findings are as follows: (a) A system of influencing factors for teleoperation safety performance was constructed based on an extensive literature review and using the Delphi method with the aid of 30 space experts. The system covered 16 factors grouped based on individual, crew, machine, and context perspectives. (b) The α level affects the screening results of the pivotal influencing factors. For the average α level, five pivotal influencing factors, namely, team communication, basic cognitive ability, display interface information, control mode design, and task complexity, are identified from the 16 influencing factors. The present study represents a valuable effort in extracting the crucial influencing factors that affect teleoperation safety performance. It provides valuable insights from an ergonomic viewpoint and aids in determining priorities for enhancing operational safety.

Identifying and ranking the critical success factors of fintech adoption: a fuzzy DEMATEL approach

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.