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Urbanization and globalization in India have led to the depletion of resources and degradation of the environment to meet the demands. Because of these issues, researchers and practitioners have begun to study various strategies to reduce the level consumption of resources to utilize it for present and future needs. In pursuit of finding solutions to the problems, sustainable building construction is found as the best key to avoid depletion of resources. Sustainable material selection is found as a vital strategy in construction. The paper aims to discuss this issue.

A three-phase methodology is proposed for framing the assessment model for construction industries to select materials for construction. In the first phase, a total of 23 sub-criteria of triple bottom line (TBL) and four brick materials as alternatives were identified. The second phase finds the weights and ranks of criteria and sub-criteria using the best worst methodology (BWM) the third phase involves ranking of materials concerning sub-criteria weights determined in phase II using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

The objective of study is fixed to identify the criteria list for the selection of material in construction industries from the literature review especially for Indian construction industries; to rank the criteria for selection of materials with the help of the BWM approach; and to prioritize the identified materials in the view of sustainability with the help of Fuzzy TOPSIS in construction industries perspective. This study analyzed and choosing right sustainable materials by the three pillars of sustainability which are the environment, economic and social, also called TBL, for Indian construction companies by framing a sustainable material assessment model.

The results of this study facilitate to frame an assessment model for evaluating and selecting sustainable building materials.

This study attempts to analyse and rank the impact of work-related issues arising within the Indian construction industry in the context of the existing pandemic. Furthermore, this is the first attempt to provide strategies to overcome issues among a workforce that is highly contractual in nature and is currently witnessing the uncertainties of the pandemic's aftermath. To the best of the author's knowledge, few studies have highlighted the combined analysis of job insecurity, psychological stress and emotional exhaustion in the Indian construction industry.

The “Best Worst Methodology” (BWM) has been used in this study to analyse and rank the key factors that eliminate negative characteristics among contractual construction employees. The BWM, outlined by Rezaei (2015, 2016), is a popular “multi-criteria decision analysis technique” due to its advantage of consistent results and lesser pairwise data requirements.

The study identified and ranked the socioeconomic impact of the three waves of the COVID-19 pandemic on construction sector employees in the Indian subcontinent. The results indicate that job insecurity has the most prominent impact, which ultimately produces psychological stress and emotional exhaustion among employees.

To achieve the objective of identifying and prioritising the criteria of adverse socioeconomic impacts during the pandemic and outlining plans of action for the construction industry, ten experts (civil engineers/managers) from ten different construction projects were involved in a mixed-method case study, which has never been explored in the Indian construction sector.

Recent years have witnessed a significant rise in Indian healthcare establishments (HCEs) which indicate that there is a constant need to improve the healthcare quality services through the adoption and implementation of TQM enablers. The purpose of this paper is to identify such enablers and then propose a ranking model for TQM implementation in Indian HCEs for improved performance.

The study identifies 20 TQM enablers through comprehensive literature survey and expert’s opinion, and classifies them into five main categories. The prominence of these enablers is established using a recently developed novel multi-criteria decision making (MCDM) method, i.e. best-worst method (BWM). The importance of the various main category and sub-category enablers is decided on the basis of their weights which are determined by the BWM. In comparison to other MCDM methods, such as analytical hierarchy process, BWM requires relatively lesser comparison data and also provides consistent comparisons which results in both optimal and reliable weights of the enablers considered in this paper. Further, a sensitivity analysis is also carried out to ensure that the ranking (based on the optimal weights) of the various enablers is reliable and robust.

The results of this study reveal that out of five main category enablers, the “leadership-based enablers (E1)” and the “continuous improvement based enablers (E5)” are the most and the least important enablers, respectively. Similarly, among the 20 sub-category enablers, “quality leadership and role of physicians (E14)” and “performing regular survey of customer satisfaction and quality audit (E52)” are the most and the least dominating sub-category enablers, respectively.

This study does not explore the interrelationship between the various TQM enablers and also does not evaluate performance of the various HCEs based on the weights of the enablers.

The priority of the TQM enablers determined in this paper enables decision makers to understand their influence on successful implementation of the TQM principles and policies in HCEs leading to an overall improvement in the system’s performance.

This study identifies the various TQM enablers in HCEs and categorizes them into five main categories and ranks them using the BWM. The findings of this research are quite useful for management of the HCEs to properly understand the relative importance of these enablers so that managers can formulate an effective and efficient strategy for their easy and smooth implementation which is necessary for continuous improvement.

Improving the medical oxygen supply chain (MOSC) is important to cope with the uneven demand and supply seen in the MOSC when India faced the second wave of COVID-19. This improvisation increases the supply chain (SC) maturity and consequently the efficiency and resiliency to tackle oxygen shortage across the country and to prevent another similar scenario from ever happening. The purpose of this study is to identify and prioritize the solutions to overcome the issues faced by the MOSC during the second wave of COVID-19 cases in India and in turn reduce the extent of casualties in the expected third wave.

This paper uses best worst method (BWM) and fuzzy technique for order performance by similarity to ideal solution to classify the sub-criteria for solutions to solve major SC issues. BWM is used to determine the weights of the sub-criteria and fuzzy technique for order performance by similarity to ideal solution for the final ranking of the solutions to be adopted.

The result of this study shows that the Internet of Things based tagging system is the best solution followed by horizontal and vertical integration of SC in making a resilient and digitized MOSC capable of handling general bottlenecks during a possible third wave.

The research provides insights that can enable the personnel involved in MOSC. Proper understanding will help the practitioners involved in the SC to effectively tailor the operations and to allocate the resources available in an effective and dynamic manner by minimizing or eliminating the pre-existing bottlenecks within the SC.

The proposed framework provides an accurate ranking and decision-making tool for the implementation of the solutions for the maturity of the MOSC.

Increased demand for new batteries and strict government protocols have stressed the battery industries to collect and recycle used batteries for economic and environmental benefits. This scenario has forced the battery industries to collect used batteries and establish the formal battery recycling plant (BRP) for effective recycling. The starting of BRP includes several strategic decisions, one of the most critical decisions encountered is to find the best sustainable location for BRP. Hence, this paper aims to address the complexity of the issues faced during the BRP location selection through a hybrid framework.

In this study, the criteria are identified under socio-cultural, technical, environmental, economic and policy and legal (STEEP) dimensions through literature review and experts' opinions. Then, the hybrid methodology integrating fuzzy decision making trial and evaluation laboratory (DEMATEL), best worst method (BWM) and technique for order preference by similarity to an ideal solution (TOPSIS) has been proposed to find the inter-relationship between criteria, the weights of criteria and the best alternative.

The identified five main criteria and 26 sub-criteria have been analyzed through fuzzy DEMATEL, and found that the policy and legal criteria have more inter-relationship with other criteria. Then from BWM results, it is found that the support from government bodies has attained the maximum weightage. Finally, the second alternative has been identified as a more suitable location for establishing BRP using TOPSIS. Further, it is found from the results that the support from government bodies, the impact of emissions, availability of basic facilities and community health are the essential criteria under STEEP dimensions for establishing BRP.

In addition to the various existing sustainable criteria, this study has also considered a set of policy and legal criteria for the evaluation of locations for BRP. Further, the hybrid MCDM method has been proposed in this study for selecting the best alternative. Thus, this study has yielded more insights to the decision-makers in choosing a sustainable location for BRP.

Today, the high cost of e-learning systems’ implementation and the difficulty of managing the infrastructures motivate educational institutions toward application of cloud-based e-learning systems. This new system should be aligned with the academics’ aims and pedagogical principles to be beneficial for learners and instructors. Therefore, the vendor selection of learning systems is one of the most important processes to migrate toward cloud-based e-learning. The purpose of this paper is to develop a new framework to facilitate the vendor selection of cloud-based e-learning systems in the cloud market.

To identify the initial criteria as to the vendor selection of cloud-based e-learning services, a literature review is done. To enrich the initial criteria, a focus group of experts is investigated, and the framework developed; then, a survey analysis is conducted to validate the proposed framework. The extracted criteria and sub-criteria are weighted and prioritized using best-worst method (BWM).

The results indicate that the main dimensions of vendor selection framework as regards cloud-based e-learning systems are managerial, technological and pedagogical factors. The rank orders and weights of the mentioned aspects and their sub-criteria are calculated using the BWM.

The proposed framework helps managers to get a big picture of requirements as to cloud-based e-learning and more effectively to select appropriate vendors in this initiative. In the vendor selection process, managers must pay attention to technological issues as well as managerial and pedagogical considerations.

Cloud-based e-learning systems are getting increasingly essential to offer training courses more efficiently in educational institutions. Although the intersection between cloud computing and e-learning has increasingly grown in both practical and academic contexts, there are little studies on how educational institutions and organizations could be able to select appropriate cloud-based e-learning systems. This paper explores the ignored but critically important subject of cloud-based e-learning. The main contribution of this paper is to propose a novel and integrated framework containing the important aspects of vendor selection in cloud-based e-learning services. The proposed framework comprises managerial, technological and pedagogical aspects simultaneously as well as sub-criteria denoting each aspect.

Recent years have seen an increased demand for healthcare services, presenting a need to improve service quality through the deployment of sustainable Lean Six Sigma (LSS). This study aims to identify critical success factors (CSFs) of sustainable LSS and prioritize them based on their intensity of importance for the effective implementation of sustainable LSS in the healthcare environment.

The present study identified 33 leading CSFs through a comprehensive literature review and expert experience and classified them into six major categories based on organizational functions. The primacy of these CSFs is established using the best-worst-method (BWM) approach. The significant advantage of this approach is that the decision-maker identifies both the best and worst criteria among alternatives prior to pairwise comparisons, leading to fewer pairwise comparisons and saving time, energy and resources. It also provides more reliable and consistent rankings.

The findings of the present study highlight the economic and managerial (E&M) CSFs as the most significant CSFs among the major category criteria of sustainable LSS-CSFs, followed by organizational (O), knowledge and learning (K&L), technological (T), social and environmental (S&E), and external factors (EF). Similarly, management involvement and leadership to implement sustainable LSS (E&M1), structured LSS deployment training and education (K&L2), and availability of required resources and their efficient utilization (O2) are ranked as the topmost CSFs among sub-category criteria of sustainable LSS-CSFs.

The prioritization of sustainable LSS-CSFs determined in this study can provide healthcare managers, researchers and decision-makers with a better understanding of the influence on effective deployment of sustainable LSS, resulting in improved service quality in hospitals.

This paper is an original contribution to the analysis of CSFs in an Indian healthcare institute, utilizing the BMW method for ranking the sustainable LSS-CSFs. The advantage of utilizing and distinguishing the performance of this approach compared to other MCDA approaches in terms of (1) least pairwise comparison and violation, (2) consistency (3) slightest deviation and (4) conformity.

The purpose of paper is to develop a performance evaluation framework for manufacturing industry to evaluate overall manufacturing performance.

The best-worst method (BWM) is used to aid in developing a performance evaluation framework for manufacturing industry to evaluate their overall performance.

The proposed BWM-based manufacturing performance evaluation framework is implemented in an Indian steel manufacturing company to evaluate their overall manufacturing performance. Operational performance of the organization is very consistent and range between 60% and 70% throughout the year. Management performance can be seen high in the 1st and 2nd quarter of the financial year ranging from 70% to 80%, whereas a slight decrease in the management performance is observed in the 3rd and 4th quarter ranging from 60% to 70%. The social stakeholder performance has a peak in first quarter ranging from 80% to 100% as at start of financial year.

This paper utilized BWM, a MCDM method in developing a performance evaluation index that integrates several categories of manufacturing and evaluates overall manufacturing performance. This is a novel contribution to BWM decision-making application.

In this study, an integrated decision-making model consisting of decision-making trial and evaluation laboratory (DEMATEL), best worst method (BWM) and a modified version of evaluation based on distance from average solution (EDAS) methods is proposed for supplier selection problem in a public procurement system considering sustainable development goals.

DEMATEL and BWM methods are used to determine weights of the criteria that are defined for the supplier selection problem. Weight aggregation method is applied to combine the weights obtained from these two methods. A modified version of EDAS method is then used in order to rank the alternative suppliers.

The proposed decision-making model is investigated for a supplier selection problem for a hospital in Spain. The validity of the results is checked using comparison with other decision-making methods and several performance analysis tests.

The proposed multi-criteria decision-making (MCDM) model contributes to the healthcare supply chain management (SCM) and aims to lead the policy makers in selecting the best supplier.

There is no such study that combines DEMATEL and BWM together for weight generation. The application of the modified EDAS method is also new. In real time situations, the decision experts may confront to the difficulty of using BWM while identifying the best and the worst criteria choices. The idea of using DEMATEL is to aid the experts to make them enable in distinguishing between the best/worst criteria and handle BWM easily.

This research work has developed an integrated fuzzy Delphi and neutrosophic best–worst framework for selecting the sustailient (sustainable and resilient) supplier for an additive manufacturing (AM)-enabled industry.

An integrated fuzzy Delphi method (FDM) and neutrosophic best–worst method (N-BWM) approach is developed. 34 supplier evaluation criteria falling under 4 groups, that is, traditional, sustainable, resilient, and AM specific, are identified and validated using the FDM. Afterward, the weights of each criterion are measured by N-BWM. Later on, the performance evaluation is carried out to determine the best-suited supplier. Finally, sensitivity analysis is performed to know the stability and robustness of the proposed framework.

The outcome indicates the high performance of the suggested decision-making framework. The analysis reveals that supplier 4 (S4) is selected as the most appropriate for a given firm based on the FDM and N-BWM method.

The applicability of this framework is demonstrated through an industrial case of a 3D-printed trinket manufacturer. The proposed research helps AM decision-makers better understand resiliency, sustainability, and AM-related attributes. With this, the practitioners working in AM business can prioritize the supplier selection criteria.

This is the primitive study to undertake the most critical aspect of supplier selection for AM-enabled firms. Apart from this, an integrated FDM-N-BWM framework is a novel contribution to the literature on supplier selection.