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Most marketing researchers use rating scales to understand consumer preferences. These have a range of problems, which can be greatly ameliorated by the use of a new technique, best‐worst scaling (BWS). The purpose of this paper is to demonstrate the BWS method by an empirical example, which demonstrates the steps to design and analyze a BW study.

A brief critique of ratings and rankings is presented. Then the basic concept of BWS is described, followed by how to use the BW method to explore how Australian and Israeli consumers choose wine in a retail store. The paper demonstrates the design of the questionnaire as well as the steps to analyze and present the results.

The BWS approach can be easily implemented for research in wine business especially for multicultural comparisons as it avoids scale confounds. After transformation of the best and worst scores of each respondent for each attribute, the data can be analyzed directly using various statistical methods and can be expressed as choice probabilities.

The advantage of BWS is its ability to compare attributes using B−W and B/W scores. The BW method provides a better discrimination of the attributes analyzed.

The simplicity of the analysis and graphical presentation makes a significant contribution to practitioners as the B−W counts and probabilities of attributes are easy to obtain and understand.

This paper presents BWS method in a form that researchers and practitioners can use and adopt for research and market surveys. The paper presents an empirical example using BWS method to determine the importance of wine cues while consumers are choosing wine in a retail store.

This study aims to identify and prioritize the challenges to adopting blockchain in supply chain management and to make its taxonomic model. Moreover, validate whether these challenging factors exist in the real world and, if they exist, then in what percentage.

This research adopted the fuzzy best-worst method (F-BWM), which integrates fuzzy set theory with the best-worst method to identify and prioritize the prominent challenges of the blockchain-based supply chain by developing a weighted multi-criteria model.

A total of 20 challenges (CH's) were identified. Lack of storage capacity/scalability and lack of data privacy challenges were found as key challenges. The findings of this study will provide a robust framework of the challenges that will assist academic researchers and industry practitioners in considering the most significant category concerning their working area.

Blockchain provides the best solution for tracing and tracking where RFID has not succeeded. It can improve quality management in a supply chain network by improving standards and speeding up operations. For inventory management, blockchain provides transparency of documentation for both parties within no time.

To the best of the authors' knowledge, no previous research has adopted the fuzzy best-worst method to prioritize the identified challenges of blockchain implementation in the supply chain. Moreover, no study provides a taxonomic model for the challenges of implementing a blockchain-based supply chain.

The purpose of this paper is to apply a very simple but powerful analysis of the variance‐covariance matrix of individual best‐worst scores to detect which attributes are determining utility components and drive distinct consumer segments.

First an analysis of variance and covariance is used to find attributes which are perceived to have different importance by different consumers and which jointly drive consumer segments. Then we model consumer heterogeneity with Latent Clustering and identify utility dimensions of on‐premise wine purchase behaviour with a principal component analysis.

Four consumer segments were found on the UK on‐premise market, which differ in the relative strength of five wine choice utility dimensions: ease of trial, new experience, restaurant advice, low risk food matching and cognitive choice. These segments are characterised by sociodemographics as well as wine and dine behaviour variables.

Attributes with high variance signal respondents’ disagreement on their importance and indicate the existence of distinctive consumer segments. Attributes jointly driving those segments can be identified by a high covariance. Principal component analysis condenses a small number of behavioural drivers which allow an effective interpretation and targeting of different consumer segments.

This paper's analysis opens new doors for marketing research to a more insightful interpretation of best‐worst data and attitude scales. This information gives marketing managers powerful advice on which attributes they have to focus in order to target different consumer segments.

This is the first study considering individual differences in BW scores to find post hoc segments based on revealed differences in attribute importance.

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.

The global resolution of embracing dynamic and intertwined production systems has made it necessary to adopt viable systems like circular economy (CE) to ensure excellency in the business. However, in emerging countries, it is challenging to implement the CE practices due to the existing problems in the supply chain network, as well as due to the vulnerable financial condition of the business after the deadly hit of COVID-19. The main aim of this research is to determine the barriers to implementing CE considering the recent pandemic and suggest strategies to organizations to ensure CE for a cleaner environment and greener economy.

After an extensive literature review and validation from experts, 24 sub-barriers under the class of 6 main barriers are finalized by Pareto analysis, which is further analyzed via the best-worst method to determine the weight and rank of the barriers Further, fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to rank the proposed startegies to overcome the analysed barriers.

The results identified “unavailability of initial funding capital”, “need long time investment”, “lack of integrating production system using advance technology” and “lack of strategic planning” as the most acute sub-barriers to CE implementation. Further, fuzzy TOPSIS method is used to suggest the best strategy to mitigate the ranked barriers. The results indicated “integrated design facility to CE”, “ensuring large scale funding for CE facility” as the best strategy.

This study will motivate managers to implement CE practices to enjoy proper utilization of the resources, sustainable benefits in business, and gain competitive advantage.

Periodically, a lot of work is done on CE practices but none of them highlighted the issues in the domain of the leather products industry (LPI) and COVID-19 toward achieving sustainability in production and consumption. Thus, some significant barriers and strategies to implement CE for achieving sustainability in LPI are highlighted in this study, which is a unique contribution to the literature.

Recognizing the factors affecting employees’ job motivation is one of the necessities that can improve people’s performance and increase their effectiveness. This study aims to determine the factors affecting job motivation and to examine effective strategies to increase motivation through identifying internal and external factors.

In this descriptive study, the statistical population was the employees of the largest petrochemical company in Iran. The questionnaire was randomly distributed to the organization’s employees and managers based on Herzberg’s motivation-hygiene theory. To analyze the obtained data, first, the best and the worst factors were identified using SPSS software and then were ranked using best–worst method (BWM).

The results demonstrated that the highest rank among the motivational factors of employees is related to working environment conditions and the lowest rank is related to career advancement and development indicator. In the second stage, the best strategies for motivational factors were determined using the fuzzy goal programming method. The findings showed that 12 out of the 17 proposed solutions have the highest motivation among employees, the implementation of which can increase employee productivity in the petrochemical company under study.

Further to the best of the authors’ knowledge, job motivation factors in the petrochemical industry have never been examined and ranked by using the BWM method so far. Also, the goal programming approach has never been applied to determine strategies for increasing job motivation and ultimately productivity.

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.

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 study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.

The paper proposes a fuzzy best–worst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.

It is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.

This study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.

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.