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Contract incompleteness with deficiency, inconsistency, defectiveness, and ambiguity in contract clauses, which can cause misunderstandings and misinterpretations, may result with disputes in projects. This study aims to investigate contract incompleteness factors with a hybrid fuzzy multi-criteria decision approach.

Contract incompleteness factors were ranked by fuzzy VIKOR (Visekriterijumska Optimizacija I Kompromisno Resenje) method, and the most significant factors were subjected to fuzzy decision-making trial and evaluation laboratory (DEMATEL) to examine their causal relationships. The study is not limited to ranking the identified factors solely, since their cause-effect interactions are also essential for proper risk management in construction projects.

Hybrid use of multi-criteria analysis reveals that ambiguity in enforceability including excessive demands and significant amendments in the scope of works are the top two causal contract incompleteness factors, while lack of implementation details and focus of focal point, and insufficient supporting and technical documents are the most affected ones.

Contractual causes of disputes due to contract incompleteness factors other than requirements of the contracts have been rarely investigated in the literature. The research is one of the first studies in the literature investigating the causal relationship among factors in construction contracts, which might lead to project disputes. Findings are expected to improve contract drafting, eventually contributing to effective risk management in construction projects.

Nowadays, designing environmentally compatible buildings with acceptable performance in terms of cost, materials, and energy efficiency is considered crucial for developing sustainable cities. This research aims to identify and rank the most influential factors in the application of Building Information Modeling (BIM) systems in the smartification of green and sustainable buildings.

The present research is applied and descriptive. In this study, we identified the most influential factors in the application of Building Information Modeling (BIM) systems through library studies and expert opinions. Data were collected using a questionnaire, and a combination of the one-sample t-test method with a 95% confidence level and the fuzzy VIKOR method was employed for analysis.

The results show that the most influential factors in the application of Building Information Modeling (BIM) systems in the Smartification of green and sustainable buildings, in order, are: “Energy saving and consumption reduction,” “Increased productivity and efficiency,” “Life-cycle assessment (LCA),” “Eco-friendly design,” “Integration with IoT and other technologies.”

In this study, while addressing the intersection of BIM technology, green building principles, and smart building objectives to optimize the performance of buildings during their life cycle, the most influential factors in the use of this system were ranked based on the criteria of “impact level,” “importance level,” and “availability of necessary tools” for implementation in Kerman. Moreover, solutions for more effectively utilizing this system in the smartification of green and intelligent buildings were proposed.

This paper aims to examine and compare extant literature on the application of multi-criteria decision-making (MCDM) techniques in humanitarian operations (HOs) and humanitarian supply chains (HSCs). It identifies the status of existing research in the field and suggests a roadmap for academicians to undertake further research in HOs and HSCs using MCDM techniques.

The paper systematically reviews the research on MCDM applications in HO and HSC domains from 2011 to 2022, as the field gained traction post-2004 Indian Ocean Tsunami phenomena. In the first step, an exhaustive search for journal articles is conducted using 48 keyword searches. To ensure quality, only those articles published in journals featuring in the first quartile of the Scimago Journal Ranking were selected. A total of 103 peer-reviewed articles were selected for the review and then segregated into different categories for analysis.

The paper highlights insufficient high-quality research in HOs that utilizes MCDM methods. It proposes a roadmap for scholars to enhance the research outcomes by advocating adopting mixed methods. The analysis of various studies revealed a notable absence of contextual reference. A contextual mind map specific to HOs has been developed to assist future research endeavors. This resource can guide researchers in determining the appropriate contextual framework for their studies.

This paper will help practitioners understand the research carried out in the field. The aspiring researchers will identify the gap in the extant research and work on future research directions.

To the best of the authors’ knowledge, this is the first literature review on applying MCDM in HOs and HSCs. It summarises the current status and proposes future research directions.

The United Nations member countries adopted a set of 17 sustainable development goals (SDGs) to achieve a better and more sustainable future for all. It encourages the use of sustainable practices during new product development (NPD). Competitiveness has put pressure on organizations to maintain their market share and look for new approaches related to NPD. The current study aims to focus on creating a framework that can help to achieve the SDGs by adopting agile new product development (ANPD) practices and Industry 4.0 technologies.

From the literature, various ANPD practices, Industry 4.0 technologies, performance metrics, their interconnection and their contribution toward achieving SDGs are extracted. The weights of selected Industry 4.0–ANPD practices are computed by robust best worst method (RBWM), and the Fuzzy-VIKOR method is used to rank the selected performance metrics. To test the robustness of the developed framework, sensitivity analysis is also performed.

The results show that among the various Industry 4.0–ANPD practices “Multi-skilled employees” have the highest weight followed by “Customer requirement analysis and prioritization.” Whereas for performance metrics, “The number of innovative products launched per year” is ranked first, with the “Average time between two launches” at second place.

This research contributes to the adoption of ANPD practices and Industry 4.0 technologies for the achievement of the business SDGs. The shortlisted Industry 4.0–ANPD practices will help in resolving the social and environmental issues. The set of performance metrics will help practitioners and managers to evaluate the performance of ANPD in the context of business SDGs.

This study adds to the understanding related to Industry 4.0–ANPD practices adoption. And to the best of the authors’ knowledge, it is believed that no similar work has been done previously and by using industry insights into technology components, this work contributes to valuable insights into the subject.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.

The purpose of this study is to analyze the impacts of the COVID-19 pandemic on the performance of companies using a hybrid Multi-Criteria Decision-Making (MCDM) approach. Specifically, the study examines Türkiye’s Top 500 Industrial Enterprises to analyze their performance before and during the pandemic, and to capture their performance in determining investment and production strategy.

To achieve the study’s objectives, the Fuzzy Best-Worst Method (F-BWM) was used to obtain importance levels of performance indicators, decreasing the vagueness in experts’ decision-making preferences. The Measurement Alternatives and Ranking According to Compromise Solution (MARCOS) method was used to rank enterprises based on their performance.

The COVID-19 pandemic has clearly had a substantial impact on the performance of Türkiye’s top 500 industrial enterprises. While some companies suffered decreased sales, others reported that their revenues increased or remained constant during the outbreak. The results reveal that the pandemic caused a shift in the initial ranking outcomes for the first two enterprises.

The study’s limitations include the sample size and the time period under consideration, which may have an impact on the generalizability of the findings.

Decision-makers’ investment, employment and operational decisions were influenced by the impact of the COVID-19 pandemic. The results provide insights for decision-makers on how to achieve higher growth and performance under the pressure of the pandemic.

The study’s practical consequences help decision-makers understand how to attain higher growth and performance in the face of the epidemic.

The originality of this study lies in using a hybrid MCDM approach to examine the impact of the COVID-19 pandemic on company performance. A hybrid MCDM approach is proposed to help decision-makers make the best possible investment and implementation decisions.

As environmental protection and sustainable development become more widely recognized, greater emphasis has been placed on the significance of green supplier selection (GSS), which can support businesses both upstream and downstream in enhancing their environmental performance while preserving their strategic competitiveness. Therefore, this paper aims to propose a new framework to study GSS.

Firstly, this paper establishes a GSS evaluation criteria system including product competitiveness, green performance, quality of service and enterprise social responsibility. Secondly, based on the spherical fuzzy sets (SFSs), the Average Induction Ordered Weighted Averaging Operator-Criteria Importance Through Inter Criteria Correlation (AIOWA-CRITIC) method is used to determine the subjective and objective weights and the combination of weights are determined by game theory. In addition, the GSS framework is constructed by the Cumulative Prospect Theory-Technique for Order Preference by Similarity to Ideal Solution (CPT-TOPSIS) method. Finally, the validity and robustness of the framework is verified through sensitivity comparative and ablation analysis.

The results show that Y3 is the most promising green supplier in China. This study provides a feasible guidance for GSS, which is important for the greening process of the whole supply chain.

Under spherical fuzzy sets, AIOWA and CRITIC are used to determine weights of indicators. CPT and TOPSIS are combined to construct a decision model, considering the ambiguity and uncertainty of information and the risk attitudes of decision-makers.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

Hospital structures serve to protect and improve public health; however, they are recognized as a major source of environmental degradation. Thus, an effective performance evaluation framework is required to improve hospital sustainability. In this context, this study presents a holistic methodology that integrates the sustainability balanced scorecard (SBSC) with fuzzy Delphi method and fuzzy multi-criteria decision-making approaches for evaluating the sustainability performance of hospitals.

Initially, a comprehensive list of relevant sustainability evaluation criteria was considered based on six SBSC-based dimensions, in line with triple-bottom-line sustainability dimensions, and derived from the literature review and experts’ opinions. Then, the weights of perspectives and their respective criteria are computed and ranked utilizing the fuzzy analytic hierarchy process. Subsequently, the hospitals’ sustainable performance values are ranked based on these criteria using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution.

A numerical application was conducted in six public hospitals to exhibit the proposed model’s applicability. The results of this study revealed that “Patient satisfaction,” “Efficiency,” “Effectiveness,” “Access to care” and “Waste production,” respectively, are the five most important criteria of sustainable performance.

The new model will provide decision-makers with management tools that may help them identify the relevant factors for upgrading the level of sustainability in their hospitals and thus improve public health and community well-being.

This is the first study that proposes a new hybrid decision-making methodology for evaluating and comparing hospitals’ sustainability performance under a fuzzy environment.

Policymakers are developing national strategic plans to encourage organizations to adopt Construction 4.0 technologies. However, organizations often adopt the recommended technologies without aligning with organizational vision. Furthermore, there is no prioritization on which Construction 4.0 technology should be adopted, including the impact of the technologies on different criteria such as safety and health. Therefore, this study aims to evaluate Construction 4.0 technologies listed in a national strategic plan that targets the enhancement of safety and health.

A list of Construction 4.0 technologies from a national strategic plan is evaluated using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Then, the data are analyzed using reliability, fuzzy TOPSIS, normalization, Pareto, sensitivity, ranking and correlation analyses.

The analyses identified six Construction 4.0 technologies that are critical in enhancing safety and health: Internet of Things, autonomous construction, big data and predictive analytics, artificial Intelligence, building information modeling and augmented reality and virtualization. In addition, six pairs of Construction 4.0 technologies illustrate strong relationships.

This study contributes to the existing body of knowledge by ranking a list of Construction 4.0 technologies in a national strategic plan that targets the enhancement of safety and health. Decision-makers can use the study findings to prioritize the technologies during the adoption process. Also, to the best of the authors’ knowledge, this study is the first to evaluate the impact of Construction 4.0 technologies listed in a national strategic plan on a specific criterion.