Decision Making Trial And Evaluation Laboratory Research Articles

Evaluation and forecasting of water cycle health in the yangtze river economic belt using composite weight and extensible cloud model

The regional water cycle reflects the dual processes of nature and society. A healthy and benign water cycle is a fundamental guarantee for achieving regional sustainable development.The Yangtze River Economic Belt, as a densely populated urban cluster with abundant water resources in China, plays a crucial role in promoting the sustainable development of Chinese urban clusters. It is of significant importance to optimize the water cycle health of the Yangtze River Economic Belt and explore its primary obstacles.This study integrates the natural-social attributes of the water cycle and selects 25 evaluation indicators from six dimensions: Drivers (D), Pressures (P), States (S), Impacts (I), Responses (R), and Management (M), to construct the water cycle evaluation system for the Yangtze River Economic Belt.Combining Analytic Hierarchy Process (AHP), Decision-Making Trial and Evaluation Laboratory (DEMATEL), and the Criteria Importance Through Intercriteria Correlation (CRITIC) method, the comprehensive weights of subjective and objective factors are obtained based on the principle of minimum relative entropy.Based on this, an Extended Cloud Model is constructed to evaluate the water cycle health status of the region from 2012 to 2022.Furthermore, the Obstacle Degree Model is introduced to identify the primary obstacles affecting the water cycle health in the region. Finally, the ARIMA model is applied to forecast the water cycle health status in the region from 2023 to 2030.The results indicate that the comprehensive water cycle health level in the region improved gradually from initially sub-pathological to sub-healthy status from 2012 to 2022.Per capita GDP, industrial water consumption per ten thousand yuan of industrial value added, per capita water consumption, forest coverage rate, sediment transport module, proportion of environmental protection investment to GDP, and proportion of water resources management investment to GDP are the primary obstacles affecting the improvement of health in this region.The forecast indicates that the overall water cycle health status in the region will remain at a sub-healthy level from 2023 to 2030.The research results can provide a scientific reference for further promoting the high-quality development of the Yangtze River Economic Belt, better supporting and serving China's modernization, and providing scientific reference basis.

A Combined Decision-Making Analysis Under Single-Valued Neutrosophic Set for Selecting Favorable Knowledge Management Strategy

The Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are pivotal methods in multi-criteria decision-making, addressing causal relationships among criteria and ranking alternatives, respectively. This paper introduces a novel approach, integrating DEMATEL and TOPSIS under the framework of single-valued neutrosophic set (SVNS) to handle indeterminacy in decision-making. Applied to a knowledge management strategy case, the method utilizes eight criteria and four alternatives derived from the literature. The process involves DEMATEL for expert weight determination and direct-relation matrix creation through linguistic variable influence. Subsequently, TOPSIS is employed to rank alternatives based on distance measures. The combined method identifies the ‘Human Resource Department’ as the most crucial in knowledge management strategy. This integrated approach facilitates organizations in pinpointing vital criteria and alternatives for effective knowledge management. Comparative analyses with existing methods are also presented.

A hybrid multi-attribute decision-making model for the systematic evaluation of exoticism-themed retail spaces from the perspective of consumer experience

Previous studies have provided abundant evidence confirming the significant impact of the physical environment in retail spaces themed with exoticism on brand establishment, consumer attraction, and stimulation of consumption behavior. However, there has been limited research employing systematic approaches to examine the influence of physical environmental elements on consumer experiences in Exoticism-themed retail spaces. Therefore, this study aims to propose a comprehensive and systematic evaluation framework from the perspective of consumer experience. It integrates the Caption Evaluation Method (CEM), Kano model, and Decision Making Trial and Evaluation Laboratory (DEMATEL) technique into a Multiple Attribute Decision Making (MADM) model. This study selects cases from China to demonstrate the superiority of the model. The research findings suggest that in the development and renovation process of integrating exoticism elements into retail environments, more attention should be paid to infrastructure such as street layout and facilities. Real plants, in particular, have the greatest primary influence on other elements. Theoretically, it offers a novel perspective on decision-making concerning the enhancement of the physical environment system in Exoticism-themed retail spaces. Practically, the analytical model developed in this study can provide decision-making insights and experiential references for similar themed retail spaces globally.

Intellectual Capital Evaluation Index Based on a Hybrid Multi-Criteria Decision-Making Technique

In the context of a burgeoning knowledge economy, enterprise intellectual capital has emerged as a pivotal asset for organizational growth. Evaluating it requires a comprehensive and robust index, yet there is no standard methodology for such assessments. Here, we propose an index for evaluating enterprise intellectual capital. We use the Delphi method to delineate a scientific decision structure. A grey-based decision-making trial and evaluation laboratory (DEMATEL) is coupled with an analytic network process (ANP)—i.e., grey DEMATEL-based ANP (GDANP)—to determine the relative weight of indicators. Then, we use the technique for order preference by similarity to an ideal solution to validate the effectiveness and applicability of the proposed evaluation index based on data on thirty new-technology companies in China. This study bridges a critical gap in academic discourse, and we discuss the practical implications for the strategic management of intellectual capital in corporate settings.

Product Design of Balance Bikes Using Decision-Making Trial and Evaluation Laboratory (DEMATEL)

By analyzing the relationship between components of a product, the decision-making trial and evaluation laboratory (DEMATEL) was used to visualize the structure of intricate causal relationships of factors that affect purchase decisions for Balance Bikes. Cluster analysis was employed to visualize the structural relationship. We took three Balance Bike designs to apply DEMATEL in their design development and compared DEMATEL results with the results of the questionnaire survey and interviews. The result showed that DEMATEL can be applied to product design and development. For Balance Bike design, respondents valued aesthetics and delivery more than other indicators including practicality, productivity, functionality, and operability.

Exploring Key Barriers of HACCP Certification Adoption in the Meat Industry: A Decision-Making Trial and Evaluation Laboratory Approach.

Food safety management represents an important concern in contemporary society. The Hazard Analysis Critical Control Point (HACCP) system is a crucial tool for meat producers, preventing and controlling major food safety concerns in the process. This research investigates key barriers to HACCP implementation in the meat industry, employing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) model to identify and categorize these obstacles. Using the insights of 18 experts, a cause-and-effect relationship diagram is generated through which twelve barriers are categorized in terms of their cause and effect and then analyzed. Threshold value is calculated as 0.299. The findings reveal poor management as the primary impediment, followed by challenges in training, knowledge, and dedication. Categorizing the barriers into four groups emphasizes the critical role of effective management and human resources. The study contributes valuable insights to food safety management literature, serving as a practical resource for industry practitioners. Despite limitations in relying on expert opinions and the industry-specific focus, the research lays a foundation for informed decision-making, stressing the importance of effective management in successful HACCP implementation. Future research directions include diversifying geographical representation, exploring practical solutions, and integrating emerging technologies for a better understanding of HACCP adoption challenges.

Evolutionary model and risk analysis of ship collision accidents based on complex networks and DEMATEL

To manage and control ship collision risk, this paper aims to develop a quantitative risk analysis approach based on complex networks and Decision-making Trial and Evaluation Laboratory (DEMATEL), to study the evolution of ship collision accidents. Firstly, through the search, selection and analysis of accident investigation reports of ship collisions in the world, 46 Risk Influential Factors (RIFs) were identified, and a network model for the evolution of ship collision was established. Secondly, a risk analysis of the evolution model for ship collision accidents was conducted by utilizing the topology characteristics of the complex network theory. Thirdly, a robustness analysis was used to identify 10 key RIFs involved in ship collisions from a global network perspective, and the grey relation analysis technique was employed to verify the key RIFs. Finally, the DEMATEL method was utilized to identify 11 key RIFs from a perspective of causality, and corresponding risk control measures were proposed to mitigate those key RIFs. The research results provide theoretical support for identifying the key RIFs and controlling the risk transmission of ship collision, and have practical significance for preventing the occurrence of ship collision accidents and ensuring the safe navigation of ships.

Exploring barriers towards effective coal supply-A non-core perspective using ISM-DEMATEL

While other energy sources are becoming more popular, India is expected to continue to depend on coal in the coming years. This study aims to identify and assess barriers to the efficient supply of coal to non-core industries reliant on this energy source. Employing a hybrid approach combining Interpretive Structural Modeling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL), we analyse the hierarchical structure and causal relationships among barriers affecting the coal sourcing process. The hybrid model emphasizes that the 'biased government approach' and 'lack of mining technology and infrastructure' are crucial barriers. These key barriers exert a substantial influence on other factors within the scope of our study. Unlike earlier studies, this research enhances theoretical understanding by thoroughly exploring barriers to effective coal sourcing strategies. Policymakers engaged in decision-making for the non-core coal-dependent sector in India can benefit from this research. Attention should be directed towards addressing key barriers influencing effective coal sourcing, specifically focusing on ‘biased approach by the government’, ‘lack of mining technology and infrastructure’, ‘social and political constraints’, ‘low research and development’, and ‘lack of skilled personnel’.

Exploring critical determinants influencing businesses’ continuous usage of mobile payment in post-pandemic era: Based on the UTAUT2 perspective

Consumers across demographics have diverse digital lifestyles and rely heavily on e-commerce to purchase necessities including food and beverages. The COVID-19 pandemic has habituated consumers to making mobile payments at an accelerated pace. Despite rising consumer demand for mobile payments, comparable trends in the supply side for businesses remains crucial for the future sustainability of mobile payments. However, businesses must remain willing to accept mobile payments and engage in continued marketing to promote mobile payment. Few studies thus far have applied multi-criteria decision-making analyses to evaluate the critical determinants of intention and motivation to use mobile payment; quantitative analysis methods, such as factor, regression, and variance analyses, are more commonly applied. This study examined the critical evaluation constructs and criteria that affect retailers and service providers in outlying islands using the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as our theoretical basis. It then established a multi-criteria decision-making framework for understanding the factors influencing businesses' mobile payment use. This study uses the multi-criteria evaluation of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and DEMATEL-based Analytic Network Process (DANP) to create network relation maps of the constructs and criteria for the willingness of outlying island retailers and service providers to continue accepting mobile payments. Penghu County is located on the outlying islands of Taiwan and has witnessed a substantial shift in transaction practices among its small and medium-sized enterprises following the outbreak of the pandemic. It experienced a marked decrease in tourist influx during the COVID-19 pandemic, significantly impacting local businesses. This study aimed to better understand the opinions and motivations of businesses in Penghu County regarding mobile payment services. Insights from this perspective should help improve local businesses’ adaptability and usage habits. The findings offer significant strategies for the government to provide specific training programs and subsidies or incentives for businesses adopting mobile payment technologies, ultimately expanding mobile payment services across small businesses that target tourists.

An assessment of barriers to integrate lean six sigma and industry 4.0 in manufacturing environment: case based approach

ABSTRACT The integration of Lean Six Sigma (LSS) with Industry 4.0 (I4.0) has numerous advantages for the organization. LSS and I4.0 integration in manufacturing environments face some challenges, including data integration, a lack of understanding of the strategic implications of integrating LSS and I4.0, security and data privacy issues, return on investment, and a shortage of consultants and trainers. This paper aims to analyse the barriers and their interrelationship that could hinder the manufacturing organisation from embracing LSS with I4.0. Fifteen potential barriers were identified from the literature review and by taking the opinion of experts and decision-makers. The grey decision-making trial and evaluation laboratory (DEMATEL) method was applied to find the influence of each barrier on other barriers. The cause–effect relationship between the barriers was identified and later validated by experts. Using a single case study methodology, this study prioritises the identified barriers that hinder the automotive component manufacturing industry from integrating LSS with I4.0. The top three barriers were ‘lack of long-term vision’, ‘timely and accurate data availability’, and ‘lack of automation’. This paper will help the managers to better understand which barriers could affect the integration of LSS with I4.0.

Smart City Actions Integrated into Urban Planning: Management of Urban Environments by Thematic Areas

Over the years, the imbalance between population needs and urban spaces’ organized development has been accentuated by increasing urbanization. The implementation of smart city actions began in the 1990s, with the development of integrated solutions in cities, harmonizing social, environmental, and economic aspects. This research measures the impact of thematic areas on smart cities’ management performance. The multi-criteria Decision Making Trial and Evaluation Laboratory (DEMATEL) method was used to identify the interdependent relations between smart cities’ thematic areas, structuring a diagram of cause-and-effect relations using threshold quantification values. Considering their degree of importance in smart city management, the thematic areas of technology and innovation, living environment and infrastructure, education and training, and governance and engagement are highlighted. For the degree of influence, the most influential thematic areas are coexistence and reciprocity, living environment and infrastructure, entrepreneurship, and healthcare. Also, the cause-and-effect analysis identified governance and engagement, education and training, and mobility as central thematic areas for smart city management. Finally, the research construct was developed by ranking the thematic areas’ performance in urban space planning.

The Exploring the Relationship between Financial Performance Indicators, ESG, and Stock Price Returns: A Rough Set-based Bipolar Approach

Using a Rough-Set-based approach, this study investigates the connection between financial performance, Environmental, Social, and Governance (ESG) factors, and stock returns. It analyzes data from various companies and industries to examine how ESG metrics interact with financial indicators to influence stock prices. Those findings underscore the importance of considering financial and ESG factors when evaluating a company's performance and market valuation. The Rough-Set-based bipolar model comprises eight decision rules, and the DEMATEL (Decision-Making Trial and Evaluation Laboratory) analysis reveals the influence relationship among the core attributes. Furthermore, the ranking result, based on the bipolar model, is consistent with the market returns, which suggests the validity of this study.

Application of the DEMATEL approach to analyse the root causes of building defects

Building defects are a common phenomenon in the construction industry. The negative consequences of building defects are not limited to waste generation or low resource efficiency. This challenge can have several direct and indirect implications such as cost overruns, unforeseen delays in project completion time, building occupants’ dissatisfaction and health and safety risks. Despite the increase in the number of studies investigating building defects in recent years, little is known about the prioritisation of their causes by considering the interaction among the causes of defects. Therefore, this study employed the decision-making trial and evaluation laboratory (DEMATEL) method to bridge this gap. Experts' opinion was sought using a questionnaire developed in the form of a matrix and semi-structured interview questions. Thereafter, the interactions among the causes of building defects were analysed and the most prominent causes are identified. The study showed that materials, workmanship and design are the major causes of building defects. Despite being specific context, the findings can be the basis for further research in this area with a focus on a range of different building typologies. Furthermore, the recommendations provided will act as a facilitator to minimise the occurrence of building defects. Lastly, the research findings can be considered in the planning stage of any construction project, to ensure the construction quality, reduce potential waste and enhance the circular economy and resource efficiency in the built environment sector.

Analyzing Barriers to Internet of Things (IoT) Adoption in Humanitarian Logistics: An ISM–DEMATEL Approach

Background: Effective humanitarian logistics (HL) is essential in disaster response. The “Internet of Things” (IoT) holds potential to enhance the efficiency and efficacy of HL, yet adoption is slowed by numerous barriers. Methods: This study employs interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) to explore and classify barriers to IoT integration in HL. Results: A total of 12 barriers were identified, classified, and ranked according to their driving power and dependence. Key barriers include lack of standardization, organizational resistance, data quality issues, and legal challenges. Conclusions: Overcoming these barriers could significantly improve relief operations, reduce errors, and enhance decision-making processes in HL. This investigation is the first of its kind into IoT barriers in HL, laying the groundwork for further research and providing valuable insights for HL managers.

Large-scale group hierarchical DEMATEL method with automatic consensus reaching

Decision-making trial and evaluation laboratory (DEMATEL) is widely used because of its ability to effectively analyze nonlinear relationships between factors in complex systems. With the increasing complexity of decision-making problems, large-scale group decision-making (LSGDM) has become the norm. Most existing DEMATEL methods are only suitable for small-scale groups and simple systems. This study, therefore, proposes a large-scale group hierarchical DEMATEL method that considers consensus reaching. The DEMATEL method for LSGDM faces three challenges: large differences in knowledge structures, difficulty coordinating expert opinions, and slow group-consensus convergence. To address these challenges, first, we use hierarchical decomposition to decompose the complex system into simple systems with different levels to reduce the difficulty of decision-making in complex systems. Second, considering the limitations of expert knowledge and experience, we use the basic probability assignment function to extract the opinions of experts at different levels of subsystems and factors. Third, we divide experts into different clusters using K-means clustering to solve the problem of difficult expert-opinion coordination. Fourth, we design two types of consensuses (intrasubgroup and intersubgroup consensus) and an efficient new type of opinion autocorrection mechanism to solve the problem of the slow convergence of intragroup consensus and improve the efficiency of consensus reaching. Finally, we demonstrate the superiority of the proposed method through data analysis and method comparison.

Effective waste management in service industry: Fuzzy-based modelling approach for strategic decision-making.

Hospitals need to identify issues of greater importance on waste management because the implementation of many different strategies may lead to an unconscious increase in costs. Accordingly, the purpose of this study is to define the most effective waste management strategies in the service industry. For this purpose, a novel fuzzy decision-making model is proposed that has two different stages. In this context, six JCI-based indicators are weighted by using sine trigonometric fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology. Additionally, a comparative evaluation has also been conducted with sine trigonometric fuzzy Criteria Importance Through Intercriteria Correlation (CRITIC) technique to check the reliability of the findings. On the other hand, five different strategy alternatives are selected by considering the principles of the integrated waste management hierarchy approach. These items are evaluated by considering sine trigonometric fuzzy Technique for Order Preference by Similarity (TOPSIS). On the other side, these factors are also ranked with the help of sine trigonometric fuzzy Additive Ratio Assessment (ARAS) to test the consistency of the results. The main contribution is that prior strategies can be presented to the hospitals to have appropriate waste management process by defining the most important factors. Criteria weighting and alternative ranking results are the same in all combinations. Therefore, it is seen that the proposed model creates coherent and consistent results. It is defined that efficient storage of waste is the key issue to have effective waste management process. Moreover, 'reduce' is found as the most critical stage of this process.

An integrated fuzzy multi-criteria decision-making model for determining the interdependencies among the african swine fever spread factors

African swine fever (ASF) is a disease outbreak that has substantially impacted agriculture, public health, and food security. This study tackles the lack of knowledge regarding the interactions between multiple factors causing the spread of ASF. We also present a potential for cross-fertilization between applying soft computing to nonmeasurable factors of different natures through investigating the relationships and significance of ASF spread factors in the Philippine setting using Multi-criteria Decision Making (MCDM) methodologies and fuzzy logic. We develop a list of ASF spread factors through a literature review. Fuzzy Analytic Hierarchy Process (AHP)-VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is used for the dimensionality reduction of factors. These elements are then classified as either net causes or net effects using the Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL), which also shows their interdependencies. The results show how socioeconomic characteristics, particularly those connected to biosecurity lapses, significantly influence other parameters through a causal graph. This knowledge can be used to develop effective ASF preventive and management methods as it provides a systematic and unbiased decision-making framework for policymakers, breeders, and manufacturers.

A duo-theme cloud model DEMATEL approach for exploring the cause factors of green supply chain management.

Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods identify cause factors in green supply chain management (GSCM). This study argues that the target method treats affecting factors as unique themes; however, various factors may be mutually antagonistic (i.e., mutually positive or negative) or encompass other meaningful information (e.g., gain/risk, intensify/depress). The factor affecting GSCM implicitly encompasses the economy and ecology (greenness), which may conflict. This new approach can be integrated into the analysis, dividing affecting factors into "cause" and "effect" groups. The organization should focus on affecting factors in the cause group. The findings provide strategic guidance for organizations to practice GSCM. A duo-theme cloud model DEMATEL approach was proposed to divide these affecting factors of GSCM into "economy" and "greenness." The cloud model was applied to overcome the ambiguity and randomness in the concept of uncertainty and allow the integration of mutual qualitative and quantitative mapping. Six factors in the economic aspect and four in the greenness aspect should be classified as the cause group. Organizations should prioritize these ten factors in their GSCM practices. Doing so makes the GSCM problem relatively straightforward and allows for efficacious decision-making. This study proposes a duo-theme cloud model DEMATEL approach to identify cause factors in GSCM.

Discriminant Decision Making of Cardiovascular Diseases Based on Cloud-Based Convolutional Attention Network

Cardiovascular diseases (CVDs) have become the number one killer affecting human health. In order to reduce the burden of medical workers, facilitate government screening of the population and enable patients to conduct their own health status checks, there is an urgent need for a complementary diagnostic system to predict the occurrence of CVD. In this study, a new cloud-based convolutional attention network (C-CAN) model is proposed for the discriminant decision making of CVD. In this model, the indicator data for discriminant decision making of CVD are trained using an improved one-dimensional convolutional neural network (1D CNN) model structure based on the correlation of factors influencing CVD given by decision-making trial and evaluation laboratory (DEMATEL) and cloud models. This 1D CNN model consists of a convolutional pooling module, an attention module and a fully connected module. The cloud model is used to process the original data based on the discriminating opinion of experts, so as to select the important factors that affect CVD. The attention mechanism is effective in augmenting attention to the essential elements of the data and reducing attention to the less important features. Both have similarities in that they are effective in augmenting the important features in the data and combine with each other to achieve better results. Moreover, the C-CAN is compared with decision tree (DT), [Formula: see text]-nearest neighbors (KNN), random forests (RF) and normal CNN according to the CVD dataset from the Kaggle platform. The results show that the classification accuracy, precision, recall and F1 value of C-CAN are all higher than that of all compared models. Further, the proposed model is further externally validated using other imbalanced datasets, and the results indicate that C-CAN has good resilience for imbalanced data. Our findings suggest that C-CAN represents a promising new approach that may somehow address the challenges associated with deep learning (DL) in the medical field.

Unveiling the Dynamics of Consumer Attention: A Two-Stage Hybrid MCDM Analysis of Key Factors and Interrelationships in Influencer Marketing

In recent years, influencer marketing has taken over traditional brand advertisements on social media platforms, combining word-of-mouth marketing with celebrity endorsements. However, there has been limited academic research on the key success factors in influencer marketing. This paper used a hybrid MCDM model that integrates the Delphi method and the decision-making trial and evaluation laboratory (DEMATEL) approach. Through a two-stage empirical study, the research aims to explore the crucial success factors in influencer marketing. This study focuses on the Taiwanese market. The target respondents for the survey are consumers in Taiwan who have purchased products or services based on recommendations from key opinion leaders (KOLs) or internet celebrities. This systematic research framework not only pinpoints key factors that capture consumer attention towards influencers, but it also illustrates the inter-relationship of structure and improvement directions among these factors. According to results, the key factors include influencer reputation, credibility, degree of key opinion influence, attractiveness, popularity, consistency between influencers and brands, fan engagement level, and informativeness. Finally, businesses can consider five aspects to be the purpose of collaboration, product type, target audience for the product, characteristics of the influencer’s creative content, and media attributes. By comprehensively considering these aspects, businesses can determine the appropriate format for collaborative content. This decision can then guide how influencers communicate with consumers, effectively conveying brand information to the relevant target audience. The research findings provide fresh and significant insights in the field of influencer marketing studies.