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During pre-vaccine era, pharmaceutical supplies [self-care essentials (SCEs)] have been proved to be a major deflector, protector and safety guard against novel coronavirus disease (COVID-19). Hence, the objective of the study is to provide a comprehensive socio-technological decision-making framework based on multiple criteria for selecting the suppliers of pharmaceuticals, such as SCEs, by multi-brand enterprises (distributors) in the pandemic environment.

A hybrid methodology of Bayesian best worst method (BWM) and multi-attributive border approximation area comparison (MABAC) method has been applied for carrying out the study. Bayesian BWM has been applied for computing the importance of criteria identified for the selection of SCEs' suppliers during pandemic environment and MABAC method evaluated the suppliers of the SCEs.

In the study, the authors have identified eight criteria such as disinfection and sanitization of vehicles, social conscience of suppliers, brand (Technological recognition) of SCEs and logistics and distribution network, among others, which are critical to the selection of a supplier for the supply of SCEs. The application of the proposed hybrid model revealed that lead time and quality of SCEs are of utmost concern for pharmacies in a pandemic environment. Among the ten suppliers, results showed that Suppliers 2, 4 and 5 have been ranked first for supplying hand wash, hand sanitizer and face mask, respectively.

The proposed model has helped the multi-brand distributors of pharmaceuticals in selecting suppliers during the ongoing crisis of COVID-19. In addition to that, in future the outcomes of the study would be helpful for multi-brand distributors as well as pharmacies and hospitals in selecting the best suppliers. Policy makers will be able to make and revise the policies immediately with the help of the proposed decision-making framework.

The paper makes a novel contribution towards theory with the criteria identified for selecting best suppliers during the pandemic COVID-19. Additionally, the proposed hybrid model helps multi-brand distributors of pharmaceuticals in making decisions that lead to a huge social and economic success in pandemic time.

The emerging markets are facing a lot of risks and disruptions across their supply chains (SCs) due to the deadly coronavirus disease 2019 (COVID-19) pandemic. To mitigate the significant post-COVID-19 consequences, organizations should modify their existing strategies and focus more on the key flexible sustainable SC (SSC) strategies. Still now, a limited number of studies have highlighted about the flexible strategies what firms should adopt to reduce the rampant effects in the context of emerging markets.

This study presents an integrated approach including Delphi method, Bayesian, and the Best-Worst-Method (BWM) to identify, assess and evaluate the importance of the key flexible SSC strategies for the footwear industry in the emerging market context.

The results found the manufacturing flexibility through automation integration as the most important flexible SSC strategy to improve the flexibility and sustainability of modern SCs. Also, developing omni-channel distribution and retailing strategies and increasing the level of preparedness by using artificial intelligent are crucial strategies for overcoming the post-COVID-19 impacts.

The novelty of this research is that the research connects a link among flexible strategies, SCs sustainability, and the impacts of the COVID-19 pandemic. Moreover, the research proposes a novel and intelligent framework based on Delphi and Bayesian-BWM to identify and analyze the key flexible SSC strategies to build up sustainable and robust SCs which can withstand in the post-COVID-19 world.

The imperative to conserve resources and minimize operational expenses has spurred a notable increase in the adoption of lean manufacturing within the context of the circular economy across diverse industries in recent years. However, a notable gap exists in the research landscape, particularly concerning the implementation of lean practices within the pharmaceutical industry to enhance circular economy performance. Addressing this void, this study endeavors to identify and prioritize the pivotal drivers influencing lean manufacturing within the pharmaceutical sector.

The outcome of this rigorous examination highlights that “Continuous Monitoring Process for Sustainable Lean Implementation,” “Management Involvement for Sustainable Implementation” and “Training and Education” emerge as the most consequential drivers. These factors are deemed crucial for augmenting circular economy performance, underscoring the significance of management engagement, training initiatives and a continuous monitoring process in fostering a closed-loop practice within the pharmaceutical industry.

The findings contribute valuable insights for decision-makers aiming to adopt lean practices within a circular economy framework. Specifically, by streamlining the process of developing a robust action plan tailored to the unique needs of the pharmaceutical sector, our study provides actionable guidance for enhancing overall sustainability in the manufacturing processes.

This study represents one of the initial efforts to systematically identify and assess the drivers to LM implementation within the pharmaceutical industry, contributing to the emerging body of knowledge in this area.

Social media is widely used to capture citizens' opinions and topics deemed important. The importance or interest social media users attribute to a topic is traditionally measured by tweet frequency. This approach is practical but overlooks other user engagement tools such as retweets, likes, quotes, and replies. As a result, it may lead to a misinterpretation of social media signals. This paper aims to propose a method that considers all user engagement indicators and ranks the topics based on the interest attributed by social media users.

A multi-criteria decision-making framework was proposed, which calculates the relative importance of user engagement tools using objective (information entropy) and subjective (Bayesian Best-Worst Method) methods. The results of the two methods are aggregated with a combinative method. Then, topics are ranked based on their user engagement levels using Multi-Objective Optimization by Ratio Analysis.

The proposed approach was used to determine citizens' priorities in transport policy, and the findings are compared with those obtained solely based on tweet frequency. The results revealed that the proposed multi-criteria decision-making framework generated more comprehensive and robust results.

The proposed method provides a systematic way to interpret social media signals and guide institutions in making better policies, hence ensuring that the demands of users/society are properly addressed.

This study presents a systematic method to prioritize user preferences in social media. It is the first in the literature to discuss the necessity of considering all user engagement indicators and proposes a reliable method that calculates their relative importance.

Today, social media is counted as an integral part of marketing strategies, which has led to a paradigm change in this field. As reported, social media marketing has been growing over the recent five years and is predicted to be exponentially growing in the future. However, despite the huge promise and intention to adopt social media marketing strategies by organisations, there remain challenges regarding the successful implementation of these new marketing programmes. Accordingly, marketing managers’ awareness of the success factors of social media marketing is essential to return investment in this area. Due to the little research been accomplished in this field, this paper aims to identify the success factors of social networks’ marketing and to rank the factors by using of interval best-worst method (BWM).

To serve the research aims, an extant literature review is accomplished and a focus group approach is conducted to identify the main success factors and sub-factors. To analyse the focus group discussions, a qualitative content analysis approach is applied. Interval BWM is used to calculate the weights of each identified factor.

In the final framework, six main success criteria, including strategy, process, technology, content, performance evaluation and people are identified, for each sub-criteria are developed. The interval BWM results suggest the content criterion as the most important success factor in developing a social media marketing strategy.

First, this research provides a comprehensive insight into the success factors and best practices of social media marketing. This is the first to draw on the critical factors affecting the success of social media marketing, considering people in the organisation such as top management, employees and customers, strategy, process and performance evaluation focussing on the change management requirements for applying social media marketing and technology as the technical factor of the adoption process, simultaneously. Identifying critical success factors of social media marketing will help marketing managers to avoid falling into the trap of developing social media strategies based on less important areas and ignoring the critical ones. Besides, owing to the limited resources of organisations in implementing social media marketing strategies, prioritising and weighing the success factors will lead to a focus on more important areas.

Whilst the related studies have mostly concentrated on the capabilities and activities required to conduct social media marketing and the few research investigated the critical success factors most concentrated on the customer and the content-related factors, the finding of this research goes beyond that and suggests technical, process and human aspects simultaneously in the implementation process in a holistic view.

The study aims to identify and evaluate relevant criteria in the proposal and support of public administration policies for preventive maintenance comprised in a conservation approach to built heritage and aligned with local sustainable development of the historic center of the city of Sobral, in Brazil.

A novel multicriteria decision model adopting the Bayesian best-worst method is presented and its application and results are described. Though a systematic procedure, criteria were selected in order to protect the tangible and intangible values of cultural heritage, as well as its sustainable development. Then experts evaluate these criteria through an elicitation instrument.

The results show that for the decision problem over preventive maintenance, social contribution and historical record of built heritage are more important than its structural vulnerability, while architecture is less relevant. Due to the low restrictions, the subcriterion related to this property has the least influence. The weights can assist in the characterization of measures and policies for the protection of the built cultural heritage.

The use of a novel decision-making method in cultural heritage is an important initiative, given the frequent use of simple and inefficient methods. The identified and weighted criteria are important data to characterize the scenario and the topic. The results contribute to protection and development of the built heritage, encouraging the implementation of preventive conservation in the historic center, conferring to the public administration valuable information to support and propose initiatives.

The low level of financial literacy among young adults is a pressing concern at both individual and country levels. Therefore, there is a dire need to understand the best-worst antecedents of financial literacy and how they influence each other.

A two-phased multicriteria decision-making (MCDM) technique consisting of best-worst method and interpretive structural modeling (BWM-ISM) was employed for pair-wise comparison, assigning weights, ranking and establishing the relationship among antecedents of financial literacy.

Results suggest that use of Internet (SF1), role of financial advisors (SF3) and education level of individuals (DS7) are top ranked antecedents, whereas masculinity/feminity, language and power distance in society are the least ranked antecedents of financial literacy. Findings will help both academicians and practitioners focus on the key factors and make efforts to increase financial literacy by minimizing resource usage.

The current study provides clarity among antecedents of financial literacy by following BWM-ISM approach for the first time in the financial literacy context.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0746

Acquainting organisations regarding the concepts of Total Quality Management (TQM) and its implementation is one measure that effectively improves their global position and performance. Kaizen is one of the concepts of TQM, which focuses on low-cost organisational transformational methods and often saves consuming significant resources (time, capital, etc.). Using Kaizen in organisational transformation sets efficient guidelines to improve processes agility and leanness and increase manufacturing productivity. Hence, this study aims to identify the key success factors in Kaizen projects and presents a score function that measures the readiness level of organisations to implement Kaizen projects.

A literature review first extracts the key success factors in Kaizen projects. Afterwards, the selected factors are screened via the fuzzy Delphi method using expert opinions from the manufacturing sector of an emerging economy. Subsequently, their importance is cross-examined by the Bayesian best–worst Method (BBWM). The BBWM is one of the most recent multiple criteria decision-making (MCDM) methods that lead to stable, dynamic and robust pairwise comparisons. After analysing the weights of the key factors, a score function is designed so that organisations can understand how much they are ready to launch Kaizen projects.

According to the findings, “Training and education” and “Employee attitude” played an important role in the success of Kaizen projects. The literature extracted 22 success factors of Kaizen projects, and 10 factors were eliminated through the fuzzy Delphi method. Twelve success factors in Kaizen projects were evaluated and investigated through the BBWM. Matching to this method, “Training and education” and “Employee attitude” weighed 0.119 and 0.112, relatively. Furthermore, “Support from senior management” was the least important factor.

To the best knowledge of the authors, this is the first research in which the success factors of Kaizen projects have been identified and analysed through an integrated multi-layer decision-making framework. Although some studies have investigated the key success factors of Kaizen projects and analysed them through statistical approaches, research that examines the success factors of Kaizen projects through MCDM methods is yet to be reported. Moreover, the score function that measures the level of readiness of each organisation for the successful implementation of Kaizen projects is a unique contribution to this research.

The purpose of this study is to identify the risks that exist in halal meat supply chain activities and to carry out a risk assessment using the fuzzy best-worst method (FBWM) along with mitigating risks using the risk mitigation number (RMN).

The method used is to collect several literature reviews related to the halal meat supply chain, which has information relevant to the risks of the meat industry in Indonesia. Then, a focus group discussion was held with several experts who play a role in the meat industry in Indonesia, and 33 identified risks were identified in halal meat supply chain activities. The proposed methodology uses FBWM and RMN in conducting risk assessment and mitigation in the meat industry in Indonesia.

The analysis reveals that priority risk is obtained by using the global weight value on the FBWM, and then risk mitigation is carried out with RMN. Priority mitigation strategies can mitigate some of the risks to the meat industry in Indonesia. The proposed mitigation strategy is designed to be more effective and efficient in preventing risks that can interfere with product halalness in halal meat supply chain activities in the Indonesian meat industry.

The implications of this study highlight the need for collaboration among stakeholders, improved risk assessment methodologies and the expansion of research into other halal supply chains. By addressing these implications, the halal industry can enhance its integrity, consumer confidence and overall contribution to the global market.

This research provides an integrated approach to identifying, analyzing, assessing and mitigating risks to the meat industry in Indonesia.

To design a novel hybrid approach to illustrate a reciprocal alignment to integrate future products and technologies. This mixed qualitative-quantitative method aims to optimize the final product portfolio and production technologies alignment in the food industry.

A list of products and technologies is extracted and evaluated by experts employing Market Attractiveness and Ease of Implementations Matrix (MA-EI) for products and attractiveness and technological Capability Matrix (A-C) for technologies. Weights of high-scored alternatives are attained applying the Z-number extension of Best Worst Method (ZBWM). After the product-technology matrix is formed and the alignment scores of each pair are determined by experts. Subsequently, final scores are computed, and a framework is proposed by electing high-ranked products and technology of each cluster to form the aligned product and technology portfolios of a food and hygiene industry company.

By employing an uncertain multicriteria decision-making approach besides product and technology matrices in a food industry corporation, among 40 technology and product, 13 products by 6 technologies are proposed. Thus, only six technology are necessary to manufacture the highly important and effective products.

The combination of product and technology analysis matrixes with an uncertain decision-making approach is considered as a novel approach in this research. Moreover, the distinctness between the present study and other researches is the concurrent unified aspect of product portfolio and technology optimization and its implementation in the planning discussion, especially in the food industry.