Application of Fuzzy DEMATEL Method for Analyzing Occupational Risks on Construction Sites

Funding Innovation and Risk: A Grey-Based Startup Investment Decision

As found in behavioral decision theory, venture capitalists (VCs) rely on heuristics and bias, owing to their bounded rationality, either by limited alternatives or information and resources. India's booming startup scene challenges VCs in decision-making owing to information overload from numerous evolving ventures, which hinders informed judgment. VC investment behavior, due diligence, and cognitive factors related to decision-making have always drawn the attention of researchers. We provide an alternative approach for an optimal decision by VCs by identifying the attributes that influence investment or funding decisions at an early stage of a venture in tech-based industries. Through a literature review, we identify eight attributes, both on internal and external criteria, that venture investors consider when making investment decisions. Based on interviews with 20 experts, we further identify eight key tech-based sectors. Using grey system theory, we then determine the rankings of eight tech startups for investors' early-stage investment decisions. This study presents a linguistic variable-based approach of grey numbers to decide weights and ratings, the grey possibility degree to compare and rank different tech startups, and based on the results, suggests the ideal tech startup. We find that agritech ranks first; thus, investors should prefer venturing into such startups for early-stage investment. E-commerce and edutech ranked second and third, respectively, followed by electric vehicle infrastructure, insurtech, fintech, space tech, and software as a service.

Exploring determinants of formation of cognitive anchors from altruistic messages: A fuzzy DEMATEL approach

Altruistic communication by non-profit organizations plays a crucial role in shaping individuals' perceptions and beliefs about altruism. One of the indicators of effective communication is the anchoring of the messages. Therefore, understanding the underlying determinants of anchoring in altruistic communication is essential. Despite the importance of anchoring in the communication of altruism, extant research has not done much to examine the determinants of anchoring in altruistic communication. This paper investigates the determinants of anchoring in non-profit organizations' altruistic communication through the lens of the dual process theory. It applies the Fuzzy Decision Making Trial and Evaluation Laboratory (F-DEMATEL) method to analyze the causal and effect factors. Data were gathered from 12 social communication experts based in Taiwan. Out of the 12 proposed determinants, three factors, namely consistency, cultural consideration, and emotional anchoring, were established as significant causal factors. Consistency had causal effects on five other factors, namely, the use of metaphors, the use of antinomies, thematic anchoring, understanding the cognitive ability of the audience, and crafting engaging information. Cultural consideration had causal effects on feedback, naming, use of antinomies, thematic anchoring, emotional anchoring, and repetition. Emotional anchoring had causal effects on thematic anchoring, use of antinomies, use of metaphors, consistency, naming, feedback, understanding the cognitive ability of the audience, and repetition. On the other hand, feedback, naming, and use of antinomies were established as significant effect factors. The study's findings offer crucial contributions to the social communication literature and provide important insights for social communication practitioners.

Scrutinizing student dropout issues in MOOCs using an intuitionistic fuzzy decision support system

Massive open online courses (MOOCs) are a recent e-learning programme that has received widespread acceptance among several colleges. Student dropout from MOOCs is a big worry in higher education and policy-making circles, as it occurs frequently in colleges that offer these types of courses. The majority of student dropouts are caused by causes beyond the institution’s control. Using an IF-DEMATEL (Intuitive Fuzzy Decision-making Trial and Evaluation Laboratory) approach, the primary factors and potential causal relationships for the high dropout rate were identified. The most effective aspects of massive open online courses (MOOCs) are identified using IF-DEMATEL and CIFCS. Moreover, it explains the interconnectedness of the various MOOC components. As an added measure, a number of DEMATEL techniques are used to conduct a side-by-side comparison of the results. Decisions made by the educational organisation could benefit from the findings. According to the research, there are a total of twelve indicators across four dimensions that are related to online course withdrawal amongst students. Then, experienced MOOC instructors from various higher education institutions were invited to assess the level of influence of these characteristics on each other. Academic skills and talents, prior experience, course design, feedback, social presence, and social support were identified as six primary characteristics that directly influenced student dropout in MOOCs. Interaction, course difficulty and length, dedication, motivation, and family/work circumstances have all been found to play a secondary part in student dropout in massive open online courses (MOOCs). The causal connections between the major and secondary factors were traced and discussed. The results of this study can help college professors and administrators come up with and implement effective ways to reduce the high number of students who drop out of massive open online courses (MOOCs).

S, M. N. Modelling the drivers of responsiveness of automotive supply chain using an integrated fuzzy DEMATEL-ISM approach

Purpose

The automotive industry appears to overcome much of its obstacles, despite the constant struggle facing COVID-19. The pandemic has resulted in significant improvements in the habits and conduct of consumers. There is an increased preference for personal mobility. In this dynamic environment with unexpected changes and high market rivalry, automotive supply chains focus more on executing responsive strategies with minimum costs. This paper aims to identify and model the drivers to the responsiveness of automotive supply chain.

Design/methodology/approach

Seventeen drivers for supply chain responsiveness have been identified from the extensive literature, expert interview. An integrated methodology of fuzzy decision-making trial and evaluation laboratory–interpretive structural modelling (DEMATEL–ISM) is developed to establish the interrelationship between the drivers. The cause–effect relationship between the drivers was obtained through fuzzy DEMATEL technique, and a hierarchical structure of the drivers was developed using the ISM technique.

Findings

The result of the integrated methodology revealed that strategic decision-making of management, accurate forecasting of demand, advanced manufacturing system in the organisation and data integration tools are the critical drivers.

Research limitations/implications

This study has conceptual and analytical limitations. In this study, a limited number of drivers are examined for supply chain responsiveness. Further research may examine the role of other key performance indicators in the broad field of responsiveness in the automotive supply chain or other industry sectors. Future study can uncover the interrelationships and relative relevance of indicators using advanced multi-criteria decision-making methodologies.

Originality/value

The authors proposed an integrated methodology that will be benefitted to the supply chain practitioners and automotive manufacturers to develop management strategies to improve responsiveness. This study further helps to compare the responsiveness of the supply chain between various automotive manufacturers.

Financial Network Analysis on the Performance of Companies Using Integrated Entropy–DEMATEL–TOPSIS Model

In this paper, we propose a multi-criteria decision making (MCDM) model by integrating the entropy–DEMATEL with TOPSIS model to analyze the causal relationship of financial ratios towards the financial performance of the companies. The proposed model is illustrated using the financial data of the companies of Dow Jones Industrial Average (DJIA). The financial network analysis using entropy–DEMATEL shows that the financial ratios such as debt to equity ratio (DER) and return on equity (ROE) are classified into the cause criteria group, whereas current ratio (CR), earnings per share (EPS), return on asset (ROA) and debt to assets ratio (DAR) are categorized into the effect criteria group. The top three most influential financial ratios are ROE, CR and DER. The significance of this paper is to determine the causal relationship of financial network towards the financial performance of the companies with the proposed entropy–DEMATEL–TOPSIS model. The ranking identification of the companies in this study is beneficial to the investors to select the companies with good performance in portfolio investment. The proposed model has been applied and validated in the portfolio investment using a mean-variance model based on the selection of companies with good performance. The results show that the proposed model is able to generate higher mean return than the benchmark DJIA index at minimum risk. However, short sale is not allowed for the applicability of the proposed model in portfolio investment.

A New Self-Assessment “TQM Hybrid MCDM Fuzzy Model” For Enhancing the KPIs in Mega Universities

The key performance indicators (KPIs) are an effective part of benchmarking for measuring the performance of universities aiming to raise the university quality services level. The problem is that there is no general standard analytical evaluation model for assessing KPIs criteria in higher education institutes for evaluating mega universities, and there are inadequacies in the treatment of specific current educational quality standards. In this paper, a hybrid fuzzy analytical model for TQM self-assessment to enhance KPIs in mega universities is proposed. The proposed model produces important weights for TQM-KPIs that are recommended for helping higher authorities at the university for enhancing the demanded total quality services and aid the university to achieve its strategic objectives and to be one of the highly ranked universities. In addition, the European Foundation for Quality Management (EFQM) excellence model enabler criteria were modified and used. Two additional enabler criteria for representing the mega university and the world ranking were proposed. The “TQM Hybrid Fuzzy Model” proposed integrates three fuzzy multiple criteria decision-making (MCDM) submodels including (1) the fuzzy analytic hierarchy process (FAHP), (2) the fuzzy decision-making trial and evaluation laboratory (FDEMATEL), and (3) the fuzzy multiple criteria decision analysis (FVIKOR). Each of these submodels is computed and evaluated analytically. A comparison between them is achieved. Finally, the whole “TQM Hybrid F-MCDM Model” proposed is proved and evaluated analytically and programmed using Excel. Hence, by comparison, it is found that the proposed hybrid fuzzy model is the most effective model for evaluating the TQM-KPIs for enhancing mega universities’ organizational assessment. Results show that the subcriteria that occupied the first three positions are the managed and improved distance learning sources and materials, policy, and strategies that are based on the present and future needs and expectations of stakeholders.

Modelling the causes of boiler accidents: implications for economic and social sustainability at the workplace

This study aims to examine the causes of boiler accidents in the context of the ready-made garment (RMG) industry of Bangladesh as an emerging economy. On the basis of a comprehensive review of the existing literature, previous accident reports, and technical discussion with relevant personnel in the industries and regulating authorities, a total of 14 causes of boiler accidents were identified. This study merged neutrosophic (N) theory with the analytic hierarchy process (AHP) for prioritizing the causes of boiler accidents. Finally, to examine the reliability of the results, a robustness analysis was performed. The findings reveal that the lack of standard legislation, non-standard boiler operation, use of expired, non-registered, and non-certified boilers, faulty design of boilers, and the shortage of skilled boiler operators are the top five notable causes of boiler accidents in the RMG industry. The findings provide valuable insights for industrial managers and policymakers to formulate strategies to reduce boiler accidents.

The logistics service providers during the COVID-19 pandemic: The prominence and the cause-effect structure of uncertainties and risks

Uncertainties and risks play a central role in creating vulnerabilities for logistics service operations. Over the years, Logistic Service Providers (LSPs) have learned how to ensure resilience to confront uncertainties and risks triggered by adverse events. However, quite unlike any seen in recent times, the COVID-19 pandemic brings about unavoidable uncertainties and risks for the logistics industry. Yet, there is no common approach to contextualize how they interact together. We incorporate an empirical research design and make a threefold contribution: first, we identify uncertainties and risks that LSPs encounter during the COVID-19 pandemic and investigate their prominence. Second, we unveil intertwined schemes of afore-identified uncertainties and risks and augment the understanding of their cause-effect structure. Third, we provide an uncertainty and risk assessment guideline for LSPs affected by threats emerging from unforeseeable crises. In this study, we combine qualitative work and the fuzzy DEMATEL method. Qualitative thematic analysis of in-depth interviews reveals the most important uncertainties (COVID-19 measures, employee welfare, forecast horizon, demand change, and government regulations) and risks (COVID-19 risk, delivery delays, supply chain disruptions, financial failure, and product returns) for LSPs. The fuzzy DEMATEL method shows that COVID-19 measures and COVID-19 risk are highly prominent and influence other factors. The results indicate that demand change, government regulations, and supply chain disruptions are net causers, and employee welfare, financial failure, forecast horizon, delivery delays, and product returns are net receivers. Distinctly, employee welfare is the most affected factor, empirically confirming that major risks for LSPs are related to the human factor. More investigation in our results suggests that supply chain disruptions and demand change, two factors triggered by the COVID-19 pandemic, influence financial failure and forecast horizon, two factors associated with operational performance.

Exploring Technical Decision-Making Risks in Construction Megaprojects Using Grounded Theory and System Dynamics

Technical decision-makings (TDMs) are a vital part of the decision-makings in construction megaprojects, facing high risks brought by technical complexity, dynamic environment, and subject cognition. Identifying technical decision-making risks (TDMRs) and exploring their interactions are important in megaproject management. Due to the high complexity of TDMs in megaprojects, TDMRs are complex and diverse. However, there is a lack of research on exploring the systematic TDMRs in megaprojects. To address this gap in knowledge, this paper aims to better understand the dynamic complexity of TDMRs in megaprojects by identifying the risks and exploring their interactions from a dynamic and systematic perspective. Grounded theory (GT) and system dynamics (SD) were adopted for this research. First, the GT was used to identify TDMRs in megaprojects and create a conceptual model depicting the relationships among TDMRs. Then, an SD model characterizing the causal structure of the TDMRs system in megaprojects is developed in both qualitative and quantitative manners. The developed model involves interrelationships among environmental risks, decision-making process risks, and decision-making execution process risks. After the validation of the model, a model simulation is conducted to predict the dynamic evolution process of the TDMRs. As a result, a multilayer risk list consisting of 42 index layer risk indicators, 13 field layer risk indicators, and 3 standard layer risk indicators is identified. The SD modeling results show that these multilevel TDMRs interact dynamically and have intricate influences on the total risk level of TDMs in megaprojects. The results of this study could be useful for decision-makers to identify and mitigate TDMRs in megaprojects.

Assessing Effectiveness of Humanitarian Activities against COVID-19 Disruption: The Role of Blockchain-Enabled Digital Humanitarian Network (BT-DHN)

The COVID-19 pandemic has affected more than 214 countries across the world, disrupting the supply of essential commodities. As the pandemic has spread, humanitarian activities (HAs) have attempted to manage the various situation but appear ineffective due to lack of collaboration and information sharing, inability to respond towards disruption, etc. This study aims to determine and provide insights into the critical factors that may enhance the effectiveness of HAs during the pandemic. A systematic literature review was undertaken to explore critical factors and validated by experts using the fuzzy–Delphi method. These were further assessed to identify the cause-and-effect relationship by means of the fuzzy decision-making trial and laboratory (DEMATEL) method. The results show that building a blockchain-enabled digital humanitarian network (BT-DHN) is the most significant factor during the pandemic. The use of digital platforms for sharing real-time information enhances the effectiveness of HAs. This study offers stakeholders, policymakers, and decision-makers the opportunity to consider these factors in strategic planning to deal with pandemic disruption.

Critical Success Factors for Safety Program Implementation of Regeneration of Abandoned Industrial Building Projects in China: A Fuzzy DEMATEL Approach

The regeneration of abandoned industrial buildings (RAIBs) has received extensive attention in urban renewal efforts to achieve urban sustainable development goals. Meanwhile, the construction safety performance of RAIBs is a major challenge with increasing RAIB projects in China. Safety programs have been considered as one of the proactive methods to effectively reduce accidents and injuries in the construction industry. Various studies have conducted critical success factors (CSFs) that influence the effective implementation of safety programs in new buildings. However, the CSFs affecting the construction safety program implementation of RAIBs were ignored. The aim of this study is to determine CSFs that affect the safety program implementation of RAIB projects. First, sixteen factors were identified combining characteristics of RAIBs with literature reviews and experts’ opinion. Second, the fuzzy set theory and decision-making trial and evaluation laboratory (DEMATEL) approach are proposed to identify the influencing degree of the factors and categorize these factors into cause-and-effect groups. Then, according to the causal diagram, management support (C1), allocation of authority and responsibility (C3), control of subcontractor (C5), personal attitude (C9), and safety inspections and hazard assessment (C14) are identified as the CSFs for the safety program implementation of RAIBs’ construction. This study guides the managers and stakeholders to especially concentrate on these CSFs in order to improve the efficiency of the safety program implementation of RAIB projects with limited resources. This study also will contribute to the improvement of safety performance and to the sustainable development goal of RAIB projects.

Key performance indicator based dynamic decision-making framework for sustainable Industry 4.0 implementation risks evaluation: reference to the Indian manufacturing industries

Global corporate giants are keen to adopt Industry 4.0 (I4.0) owing to its continuous, impactful, and evident benefits. However, implementing I4.0 remains a significant challenge for many organizations, mainly due to the absence of a systematic and comprehensive framework. The risk assessment study is key to the flawless execution of any project is a proven fact. This paper aims to develop a KPIs-based sustainable integrated model to assess and evaluate risks associated with the I4.0 implementation. This research paper has developed the I4.0 risks evaluation model through fifteen expert interventions and an extensive systematic literature review. This research, based on sixteen KPIs evaluates six risks impacting the organization's decision to adopt I4.0. Initially, the Fuzzy Decision-Making Trial and Evaluation Laboratory method is used to map the causal relationship among the KPIs. Further, the additive ratio assessment with interval triangular fuzzy numbers method is used to rank the risks. The study revealed that information technology infrastructure and prediction capabilities are the most crucial prominence and receiver KPIs. Simultaneously, technological and social risks are found to be highly significant in the I4.0 implementation decision-making process. The developed model meticulously supports the manufacturer's, policymaker, and researchers' viewpoint toward I4.0 implementation in the present and post COVID-19 pandemic phases in manufacturing companies. The comprehensive yet simple model developed in this study contributes to the larger ambit of new knowledge and extant literature. The integrated model is exceptionally based on the most prominent risks and a wider range of KPIs that are further analyzed by aptly fitting two fuzzy MCDM techniques, which makes the study special as it perfectly takes care of the uncertainties and vagueness in the decision-making process. Hence, this study is pioneering and unique in context to I4.0 risks prioritization aiming to accelerate I4.0 adoption.

A novel approach to assess occupational risks and prevention of hazards: the house of safety & prevention

Risk management is the key factor to obtain safety in the working environment and its effectiveness increases with accuracy assessment and robust analysis. However, it is hard to succeed because of uncertainties in the working environment. Therefore, there are a lot of risk assessment methods in the literature to assess occupational health and safety risks. The traditional risk assessment methods handle each activity in the working environment separately and they do not consider the interactions among them. Furthermore, in these methods, potential outcomes of the risk parameters are considered based on the most possible outcome although there may be more than one potential outcome. Differ from the traditional methods, The House of Safety method has been proposed to consider all potential outcomes and handle the interactions among the activities. In this study, an extension of The House of Safety is proposed to consider interactions among potential risks and to determine the most effective prevention method based on the potential risks. Hence, this extension provides an evaluation of the whole system. The proposed model has been developed by integrating Fuzzy Inference System (FIS), Fuzzy Analytical Hierarchy Process (FAHP), and DEMATEL into Quality Function Deployment (QFD). In this direction, FIS is used to determine activity-related probabilities, “FAHP” is utilized to identify all possible damage potentials of risks, and the DEMATEL is used to clarify interactions among risks. Finally, all information produced by these methods were aggregated to obtain total risk scores by using QFD. In addition, a second home has been created to link prevention and risks. Therefore, an effective prevention plan has been made to eliminate priority risks with all effective parameters. This stage provides the opportunity for optimum prevention plan against risk or risk groups dominating the system at the same time. In this study, unlike traditional methods including a partial risk assessment perspective, an integrated method that takes into account the risks on their own and the interactions between them is proposed in the literature, and the proposed approach has been applied to an open pit mine.

A new model for risk assessment in glass manufacturing using Risk Matrix based IVIF-TOPSIS method

The glass manufacturing includes operations, such as batch forming using raw materials, melting, forming, annealing, quality check and package. Due to risky processes in glass manufacturing, significant health hazards for workers are present in the glass industry. Risk assessment is effective way to prevent accidents and protect workers from serious accidents during glass manufacturing. To assess health hazards associated with glass manufacturing, in this study Risk Matrix and The Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method are integrated under Interval-Valued Intuitionistic Fuzzy (IVIF) environment to prioritize risk factors and suggest required preventive and protective measures. Suggested preventive and protective measures provide technical, economic and environmental challenges for glass manufacturing firms. Once the importance weight of risk parameters in Risk Matrix’ are determined, the risk factors are assessed by performing IVIF-TOPSIS method during glass manufacturing. In order to verify the validity and stability of the proposed risk assessment model, sensitivity and comparative analysis are accomplished at the end of the study.

Designing DEMATEL method under bipolar fuzzy environment

Bipolar fuzzy graph is more precise than a fuzzy graph when dealing with imprecision as it is focusing on the positive and negative information of each vertex and edge. Nowadays, researchers have utilized bipolar fuzzy graphs in decision-making problems. Bipolar fuzzy competition graphs aid to compute the competition between the vertices in bipolar fuzzy graphs. To depict the best competitions among the competitions of bipolar fuzzy graphs, the best bipolar fuzzy competition graph can be defined using bipolar fuzzy α-cut and the strength of the competition between the vertices can also be determined. Fuzzy graphs are used well to frame modelling in real-time problems. In particular, when the real-time scenario is modelled using the bipolar fuzzy graph, it gives more precision and flexibility. At present, researchers have focused on decision-making techniques with bipolar fuzzy graphs. The DEMATEL method is one of the powerful decision-making tools. It effectively analyses the complicated digraphs and matrices. The fuzzy DEMATEL technique can convert the interrelations between factors into an intelligible structural model of the system and divide them into cause and effect groups. Therefore, this study attempts to design the DEMATEL method under the bipolar fuzzy environment. To illustrate this proposed technique, the problem of identifying the best mobile network is taken. With this method, the benefits and drawbacks of networks are measured and a complicated bipolar fuzzy directed graph can be transformed into a viewed structure.

Barriers to implementing digitalization in the Indian construction industry

Purpose

This research paper aims to analyze the critical barriers to implementing digitalization in the Indian construction industry as Indian construction companies are lagging in the implementation of digital technologies in the work environment.

Design/methodology/approach

In this research paper, a qualitative research approach is adopted, and multiple detailed interviews are conducted with industry and academic experts. Further, multi-criteria decision-making (MCDM) techniques are used to finalize the prioritization among various alternatives. The fuzzy-decision-making trial and evaluation laboratory (Fuzzy-DEMATEL) and interpretive structural modeling (ISM) techniques are employed to find the exact relationship among the identified alternatives.

Findings

This study identifies 14 critical barriers from an extensive literature review and multiple interviews with industry professionals, and further driving and critical barriers are identified.

Research limitations/implications

In this research paper, an exploratory study with a limited number of respondents from a large Indian construction company is carried out. Further, a detailed longitudinal analysis can be done to assess the subjectivity of the participants with more advanced statistical tools. However, this research discusses several points pertaining to the implementation of digitalization in the construction industry. The research further identifies the critical barriers to digitalization in the Indian construction industry.

Practical implications

The finding of the study has two-pronged implications. First, it provides a road-map to the construction industry by highlighting the engagement of top management as the key focus area for successful digitalization. Second, the finding also shows similarity of the digitalization process to the adoption of process improvement techniques like lean and total quality management (TQM), wherein the top management plays a crucial role in ushering in the implementation of a disruptive change.

Originality/value

The research is unique in two ways. First, this is one of the very few attempts to understand digitalization in the Indian context. Second, the research also demonstrates that the combination of fuzzy DEMATEL and ISM techniques can be successfully employed in the emerging field of construction digitalization research.

Modelling the interdependent relationships among epidemic antecedents using fuzzy multiple attribute decision making (F-MADM) approaches

With the high incidence of the dengue epidemic in developing countries, it is crucial to understand its dynamics from a holistic perspective. This paper analyzes different types of antecedents from a cybernetics perspective using a structural modelling approach. The novelty of this paper is twofold. First, it analyzes antecedents that may be social, institutional, environmental, or economic in nature. Since this type of study has not been done in the context of the dengue epidemic modelling, this paper offers a fresh perspective on this topic. Second, the paper pioneers the use of fuzzy multiple attribute decision making (F-MADM) approaches for the modelling of epidemic antecedents. As such, the paper has provided an avenue for the cross-fertilization of knowledge between scholars working in soft computing and epidemiological modelling domains.

An Approach to Assess Sustainable Supply Chain Agility for a Manufacturing Organization

Worldwide business organizations realize that agility of sustainable supply-chain is a requisite need for survival in a dynamic, competitive, and unpredictable market. The contribution of this research is to explore and evaluate sustainable agility in supply chains for a dairy manufacturing organization located in Saudi Arabia. Other contributions of this research are to update the literature about the different factors contributing to achieve agile supply chain, propose conceptual framework and assessment approach incorporating the relationships between sustainable supply-chain capabilities, enablers, and attributes, and shortlisting the agility barriers and how they would facilitate manufacturing organizations’ performance. The paper presents supply chain agility evaluation approach, which covers identification of agile supply-chain capabilities and drivers. It also presents a conceptual model and a framework to define agility level and barriers within the supply-chain. In the paper, fuzzy logic approach is preferred, owing to its capability to incorporate and deal with problems involving impreciseness and vagueness phenomena. Threshold-value in this study for the case organization is set to 0.24829. The outcome of the adopted approach indicates that 21 attributes performed below the threshold value; these attributes are further categorized as agility barriers. These are the barriers within their supply chain that impact the agility-level. For the case organization, the foremost priority is to enhance maintainability and serviceability to make it flexible and inexpensive to establish an agile responsive supply chain. At the same time, it should have priority to focus on development and integration of their core competencies to deal with cross-functional and cross-enterprise issues in supply chain. For the case organization, the agility level was found “very agile,” although it is below the “extremely agile.” Thus, a study was developed to understand the behaviour of the supply chain agility and assess/evaluate it to support decision makers in order to develop a strategic solution for different organizational barriers.

Pareto Optimal Decisions in Multi-Criteria Decision Making Explained with Construction Cost Cases

In multi-criteria decision-making (MCDM) problems the decision-maker is often forced to accept a not ideal solution. If the ideal choice exists, it would be certainly chosen. The acceptance of a non- ideal solution leads to some inadequate properties in the chosen solution. MCDM methods help the decision-maker to structure his needs considering different units, in which the properties of the solutions are expressed. Secondly, with MCDM tools the assessment of the available solutions can be calculated with consideration of the decision-maker’s needs. The incorporation of the cost criterion into the decision maker’s preferences calculation, and the solution assessment calculation, deprives the decision-maker of the ability to calculate the financial result of the decision he must make. A new multi-criteria decision making with cost criterion analysed at the final stage (MCDM-CCAF) method is developed based on principle of Pareto optimal decisions. It is proposed to exclude the cost criterion from the MCDM analysis and consider it at the final phase of the decision-making process. It is illustrated by example solutions with consideration of cost criterion and without it. It is proposed to apply the invented post-processing method to all MCDM analyses where the cost criterion of analysed variants is considered.

Development of a Weight Factor Method for Sustainability Decisions in Building Renovation. Case Study Using Renobuild

Energy efficiency investments have become strategically important for the European Union. In particular, energy efficient renovation and investment in the existing building stock have become major challenges. Renovation of a building should involve a holistic and integrated design process, which considers all aspects of sustainability. The aim of this work is to suggest a mathematical model that weighs economic, social and ecological aspects into a measure that supports housing owners/decision makers to find the optimal renovation alternative from their perspective, taking factors such as budget, energy consumption, etc. into consideration. Multi-criteria decision-making (MCDM) concerns structuring and solving multiple-criteria decision problems. MCDM has become popular in energy planning as it enables the decision maker to pay attention to all the criteria available and make the appropriate decision as per the priority of the criteria. In this study, the concept is introduced based on economic, social and ecological aspects assessed during a renovation project. A pedagogical example illustrates the suggested numerical system for comparing different renovation alternatives. The suggested method will facilitate decision-making processes in renovation projects and will allow decision makers to choose the best renovation alternatives that are in line with their business ideas and principles.

An Extension of the Failure Mode and Effect Analysis with Hesitant Fuzzy Sets to Assess the Occupational Hazards in the Construction Industry

The construction industry is considered as one of the most dangerous industries in terms of occupational safety and has a high rate of occupational incidents and risks compared to other industries. Given the importance of identifying and assessing the occupational hazards in this industry, researchers have conducted numerous studies using statistical methods, multi-criteria decision-making methods, expert-based judgments, and so on. Although, these researchers have used linguistic variables, fuzzy sets and interval-valued intuitionistic fuzzy sets to overcome challenges such as uncertainty and ambiguity in the risk assessment conducted by experts; the previous models lack in efficiency if the experts are hesitant in their assessment. This leads to the inability to assign a specific membership degree to any risk. Therefore, in this research, it is tried to provide an improved approach to the Failure Mode and Effects Analysis (FMEA) method using an Multi-Criteria Decision-Making (MCDM) method based on the hesitant fuzzy set, which can effectively cope with the hesitance of the experts in the evaluation. Also, Stepwise Weight Assessment Ratio Analysis (SWARA) method is applied for risk factor weighing in the proposed approach. This model is applied to a construction industry case study to solve a realistic occupational risk assessment. Moreover, a comparison is made between the results of this model and those obtained by the conventional FMEA and some other aggregation operators. The results indicate that the newly developed approach is useful and flexible to address complex FMEA problems and can generate logical and reliable priority rankings for failure modes.

Priorities of Urban Transport System Stakeholders According to Crowd Logistics Solutions in City Areas. A Sustainability Perspective

This article presents assessment results for the priorities of different urban logistics stakeholders in order to launch crowd logistics (CL) solutions within a city area. Additionally, this analysis was conducted by using various dimensions of sustainable city development. The unique character of this study lies in presenting the priorities of the interviewed stakeholders. Therefore, the purpose of this publication is to analyze the priorities of various stakeholders in relation to the introduction of CL solutions within a city area. The reason for such analysis is the rapid development of a large number of sharing-economy initiatives, including services. For this study, a few methods were used to achieve the research goal. Firstly, text mining and text analysis methods were implemented to analyze the opinions of the interviewees. Then, two chi-square tests allowed us to determine whether the groups of stakeholders are statistically different or not. Finally, the AHP (analytic hierarchy process) and DEMATEL (decision making trial and evaluation laboratory) methods were applied in order to define the priorities of the stakeholders. The needs of particular groups of respondents were derived from the interview based on the questionnaire. The results of the analysis allow for defining the demand characteristics for CL solutions by presenting the areas that are crucial for particular groups of stakeholders.

What Is the Critical Factor and Relationship of Urban Regeneration in a Historic District?: A Case of the Nanluoguxiang Area in Beijing, China

Although investigations and evaluations are being carried out, there is little research on the relationship between urban regeneration factors, in which various stakeholders participate, and various projects are underway simultaneously. Therefore, this study aims to understand the critical factors and influence relationship among factors in urban regeneration projects in the Nanluoguxiang area, where such projects have been carried out for the historical and cultural preservation in Beijing, China. The implications of this study are as follows: First, both factor public space environmental improvement and building restoration and improvement were analyzed as the most important factors. Second, the elements of the public space environmental improvement and the building restoration and improvement were closely related, influenced by policies and plans, guidelines and criteria, investigation and research, and the subsidy support project. Third, despite the fact that the project was promoted with the aim of revitalizing culture, it was analyzed that the influence of cultural elements on other factors was very minimal because of excessive commercialization.

An Evidential Model for Environmental Risk Assessment in Projects Using Dempster–Shafer Theory of Evidence

One of the goals of sustainable development is to achieve economic and social growth according to environmental criteria. Nowadays, impact assessment is an efficient decision making method in planning and management with environmental perspectives. Environmental risk assessment is a tool to reduce the impacts and consequences of various activities on the environment in order to achieve sustainable development. One of the commonly used environmental risk assessment methods is the probability–impact matrix method, which is known as a quantitative method for risk assessment of projects. In this method, numerical estimates of probability and impact of risk occurrence are very difficult, and these factors are associated with uncertainty. When uncertainty exists, data integration is of great importance, for which the fuzzy inference system and evidence theory are known as effective methods. Unavailability of experts’ opinion and the exponential growth of the number of required fuzzy rules associated with the risk factors are two drawbacks of fuzzy inference. Dempster–Shafer’s theory of evidence is one of the popular theories used in intelligent systems for modeling and reasoning under uncertainty and inaccuracy. In this paper, an evidential model for project environmental risk assessment is proposed based on the Dempster–Shafer theory, which is capable of taking into account the uncertainties. The proposed model is used to assess the environmental risks of Maroon oil pipelines in Isfahan. In addition, the proposed model is used in the case of tunneling risk assessment taken from the subject literature. To evaluate the validity of the proposed evidential model, the results are compared in two case studies, with the results of the conventional risk assessment method and the fuzzy inference system method. The comparative results show that the proposed model has a high potential for project risk assessment under an uncertain environment.

Sustainable Investment: Interrelated among Corporate Governance, Economic Performance and Market Risks Using Investor Preference Approach

Prior studies are lacking on the drivers of sustainable investment. Hence, this study examines the relationship between the social aspects, environmental aspects, economic benefits, market conditions, and corporate governance issues on sustainable investment. Sustainable investment has been rising since the last decade. However, sustainable investment is preceded by ethical investment, green investment, and socially responsible investment. In order to understand the sustainability of an investment before decision-making, it proposed a set of attributes to measure its sustainability using investor’s linguistics preferences. The proposed attributes are interrelated and based on investor’s linguistic preferences. The study employs the fuzzy set theory to handle the uncertainty resulting from the vagueness of linguistic terms and applies decision making trial and evaluation laboratory (DEMATEL) to determine the nature of interrelationships among sustainable investment attributes. The result indicates that corporate governance, economic performance, and market risks are the causal aspects of sustainable investment. In addition, this study found that transparency, anti-corruption, and board diversity were the two most important criteria of corporate governance. Furthermore, the three most important criteria of economic performance presented the model were excess return, market value, and shareholder loyalty. The theoretical and practical implications of sustainable investment are discussed.

An Integrated Decision-Making Method Based on Neutrosophic Numbers for Investigating Factors of Coastal Erosion

The recent boom of various integrated decision-making methods has attracted many researchers to the field. The recent integrated Analytic Network Process and Decision Making Trial and Evaluation Laboratory (ANP–DEMATEL) methods were developed based on crisp numbers and fuzzy numbers. However, these numbers are incapable of dealing with the indeterminant and inconsistent information that exists in real-life problems. This paper proposes improvements to the integrated ANP–DEMATEL method by bringing together the neutrosophic numbers, the ANP method, and the DEMATEL method, which are later abbreviated to NS-DANP. The proposed NS-DANP method can handle the indeterminacy elements in the decision-making environment, as the single-valued neutrosophic numbers are used in the decision analysis. This proposed NS-DANP modification method includes linguistic variables representing the single-valued neutrosophic numbers (SVNNs), and also introduces the single-valued neutrosophic weighted averaging (SVNWA) aggregation operator to aggregate the decision makers’ judgments instead of the typical averaging method. The applicability of the proposed method is illustrated by a case study of the coastal erosion problem along the Peninsular Malaysia coastline, where 12 factors were considered. Three experts of coastal erosion from different organizations were invited to elicit their linguistic judgments on the cause–effect of the coastal erosion. The seven-step decision approach was developed to acquire the weightage of each coastal erosion factor. The outcome of this study reveals that coastal development is the riskiest factor toward coastal erosion. The weight of factors and the cause–effect diagram could be very helpful for government and stakeholders to project a better mitigation plan for the coastal erosion problem. Comparative analysis is also provided to check the feasibility of the proposed method.

Assessing and Prioritising Delay Factors of Prefabricated Concrete Building Projects in China

Prefabricated construction has been widely accepted as an alternative to conventional cast-in-situ construction, given its improved performance. However, prefabricated concrete building projects frequently encounter significant delays. It is, therefore, crucial to identify key factors affecting schedule and explore strategies to minimise the schedule delays for prefabricated concrete building projects. This paper adopts the decision-making trial and evaluation laboratory (DEMATEL) model and analytic network process (ANP) method to quantify the cause-and-effect relationships and prioritise the key delay factors in terms of their importance in the Chinese construction industry. The DEMATEL model evaluates the extent to which each factor impacts other factors. The quantified extents are then converted into a prioritisation matrix through ANP. The delay factors of prefabricated construction projects are selected and categorised based on a literature review and an expert interview. Questionnaires are then implemented to collect the data. The results reveal that the issue of inefficient structural connections for prefabricated components is found to be the most significant factor and most easily affected by other delay factors. This research also suggests prioritising major delay factors, such as ‘lack of communication among participants’ and ‘low productivity’, in the Chinese construction industry during scheduling control. Overall, this research contributes an assessment framework for decision making in the scheduling management of prefabricated construction.

Critical Behavioral Risk Factors among Principal Participants in the Chinese Construction Industry

The main participants in construction projects are the client, contractors, material suppliers, and consultants such as the project supervisor. They play the most important roles in implementing construction projects, and their behavior has a significant impact on the project’s performance. Because each participant has their own particular interests, by virtue of proprietary information advantage, each individual participant is driven to achieve maximum benefit, which can result in improper behavior with respect to each other. The risk of this resulting in moral hazard and adverse selection based on information asymmetry is called behavioral risk among principal construction participants. Behavior is affected by various risk factors; successful implementation of construction projects depends on effective management of the key risk factors. This paper identifies and ranks the critical behavioral risk factors from the perspective of principal construction participants in the Chinese construction industry. The data used for analysis is based on an interview and questionnaire survey. Factor analysis is conducted with the assistance of SPSS17.0. Forty-one potential behavioral risk factors are identified, with 30 of those being critical, including “client changes project objective or investment direction”, “designer uses technological capability advantage to obtain profit”, and others. These findings contribute to the understanding of risk management in the construction industry in China. They also serve as a useful reference for further studies on the subject.

Assessment of Postural Load during Melon Cultivation in Mediterranean Greenhouses

Health and safety at work directly influence the development of sustainable agriculture. In the agricultural sector, many farm workers suffer musculoskeletal disorders caused by forced posture. The objective of this research is to assess working postures during melon cultivation in Almería-type greenhouses. The Ovako Working Posture Assessment System (OWAS) has been used with pictures of the tasks. The variables studied by multiple correspondence analysis were as follows: Subtask, Posture code, Back, Arms, Legs, Load, Risk, and Risk combination. The OWAS analysis showed that 47.57% of the postures were assessed as risk category 2, 14.32% as risk category 3, 0.47% as risk category 4, and the rest as risk category 1. Corrective measures should be implemented immediately, as soon as possible, or in the near future, depending on the risks detected.