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The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of imported fresh food supply chains (IFFSCs). This study aims to identify specific risk factors in IFFSCs, demonstrate how these risks are transmitted within the system and provide an analytical framework for managing these risks.

A total of 15 risk factors for IFFSCs through extensive literature review and expert consultation are identified and classified into seven levels using interpretive structural modeling (ISM) to demonstrate the risk transmission path. Fuzzy Matrice d’Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis is then used to analyze the role of each factor.

The interactions of the 15 identified risk factors of IFFSCs, classified into seven levels, are visualized using ISM. The fuzzy MICMAC analysis classifies the factors into four groups, namely, dependent, independent, linkage and autonomous factors, and identifies the relatively critical risk factors in the system.

The findings of this research provide a clear framework for enterprises operating in IFFSCs to understand the specific risks they may face and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the system to facilitate the formulation of appropriate mitigation measures.

This study provides enterprises in IFFSCs with a comprehensive understanding of how the risks can be effectively managed and a basis for further exploration. The theoretical model constructed is also a new effort to address the issues of risk in IFFSCs. The ISM and the fuzzy MICMAC analysis offer clear insights for researchers and enterprises to grasp complex concepts.

The apparel industry of Bangladesh is rethinking lean manufacturing (LM) deployment because of the challenges imposed by the COVID-19 pandemic. Due to COVID-19, LM implementation in the apparel industry has become more difficult. Thus, the purpose of this study is to explore the barriers to implementing LM practices in the apparel industry of Bangladesh in the context of COVID-19 pandemic.

For evaluating the barriers, an integrated framework that combines the Delphi method and fuzzy total interpretive structural modeling (TISM) has been designed. The application of fuzzy TISM has resulted in a structured hierarchical relationship model of the barriers with driving and driven power.

The findings reveal that “lack of synchronization of lean planning with strategic planning”, “lack of proper understanding of lean concept” and “low priority from the top management” are the three top most important barriers of LM implementation in apparel industry.

These findings will help the apparel industry to formulate strategy for implementing the LM practices successfully. The proposed model is expected to contribute to the sustainable development goals (SDGs) such as Responsible Consumption and Production (SDG 12); Decent Work and Economic Growth (SDG 8); Industry, Innovation and Infrastructure (SDG 9) via resilient strategies.

This study is one of few initial efforts to investigate LM implementation barriers during the COVID-19 epidemic in a real-world setting.

This study aims to identify the key enablers for the adoption of Industry 4.0 (I4.0) in the automobile industry of India, which has been severely impacted by COVID-19. Adopting I4.0 will provide organizations greater flexibility and resilience during the COVID-19 pandemic.

Based on the literature review and experts’ opinions, 21 enablers were identified. Further, contextual relationships among the identified factors and a hierarchical digraph was developed by using the total interpretive structural modelling (TISM) technique. Finally, fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis was conducted to classify the enablers into different categories based on their dependence and driving power.

The results indicate that top management support, clarity on government policy, strategic vision on I4.0 and development of new industrial policy are the most influential factors, with the highest driving power placed at the bottom of the TISM hierarchical model. Furthermore, agile workforce, smart HR practices and IT standardization and security are identified as linkage enablers with the most driving and dependency power.

The hierarchical TISM model and fuzzy MICMAC approach provide a comprehensive understanding of the I4.0 implementation process through a visual, logical structure to the managers. It will help the researchers and practitioners understand the contextual relationship among various enablers in fostering the I4.0 adoption process and digital reorganization in the automobile industry during the COVID-19 pandemic.

This study provides a holistic TISM hierarchical framework on I4.0 adoption that will elevate the next maturity level of innovation adoption and may act as a blueprint for automobile industries during the COVID-19 pandemic.

Real estate investment trusts (REITs) have shown great potential in addressing the current contradiction between underinvestment and sustainable development of urban regeneration in China, as well as in further facilitating the transformation and upgrading of China's urban development. In this regard, this study aims to investigate critical success factors (CSFs) and explore the relationships among these factors, and serve as a reference to provide recommendations and strategies for the successful implementation and sustainable development of urban regeneration REITs.

In this study, an integrated total interpretive structural modeling–matriced impact croises multiplication applique (TISM–MICMAC) approach using the TISM technique and MICMAC analysis is then implemented to explore the relationships among CSFs, demonstrate the hierarchical structure and classify these factors into clusters based on calculated driving powers and dependence.

This study has determined a final list of 11 CSFs through literature review and expert survey. The TISM model demonstrates a six-level hierarchical structure encompassing the influence transmission paths of CSFs, in which the most significant factors and links are established, while the MICMAC analysis further classifies CSFs into four clusters as a complement for the findings of the TISM technique.

This study offers practical implications for governments, individual and institutional investors, REITs and property managers, and other stakeholders concluded in urban regeneration REITs. The final list of determined CSFs can serve as the decision points for management and control of the implementation processes, while the findings of the TISM–MICMAC approach can be a significant reference to provide strategies for optimization and enhancement of urban regeneration REITs.

This study is a novel attempt to use both the TISM technique and MICMAC analysis to investigate CSFs for the implementation of urban regeneration REITs, and to address the theoretical and methodological research gaps in the existing literature.

The apparel and textile sector poses a significant environmental challenge due to its substantial contribution to pollution in the form of air, water and soil pollution. To combat these issues, the adoption of sustainable practices is essential. This study aims to identify and analyse the barriers that hinder the progress of sustainability in the apparel and textile industry. By consulting experts in the field, critical barriers were identified and given special attention.

To achieve the research objective, an integrated approach involving Interpretive Structural Modelling (ISM) and fuzzy MICMAC decision-making techniques was employed. The results were further validated through the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method.

The findings highlight that barrier related to clothing disposal, inadequate adaptation to modern technology, challenges affecting sector efficiency and issues related to fashion design are crucial in influencing the remaining six barriers. Based on the outcomes of the DEMATEL method, a comprehensive cause-and-effect diagram was constructed to gain a deeper understanding of these challenges.

This research provides valuable insights for policymakers and stakeholders in the apparel and textile industry. It offers a strategic framework to address and overcome sustainability barriers, promoting the development of a more environmentally responsible and resilient sector.

The purpose of this research is to conduct an in-depth investigation of the barriers apparel and textile sectors. It is feasible that both the management team and the medical experts who provide direct patient care could benefit from this research.

Effective total quality management (TQM) practices rely on the accurate classification of critical success factors (CSFs). The impact matrix cross-reference multiplication technique for classification (MICMAC) or/and fuzzy MICMAC (FMICMAC) can be used to identify key factors in the complex set. However, TQM includes both “hard” and “soft” factors, limiting application of the traditional MICMAC/FMICMAC method.

Previous literature on TQM was reviewed, CSFs were identified, and factors were sorted into soft and hard categories. The combined fuzzy integration and dual-aspect MICMAC (fuzzy dual-aspect MICMAC approach) was then applied to identify, cluster and prioritize the CSFs of TQM.

A total of 20 factors (10 soft and 10 hard) were identified and isolated to assess the manufacturing- and service-related TQM practices of the Pearl River Delta Region of China. Seven driver factors and one linkage factor emerged as the key CSFs that managers should prioritize.

A major limitation of this study is the dependency of the results on the definitions of linguistic labels. If the linguistic definitions of TQM CSFs do not closely correspond to the expert opinion data, then the analysis results may be inaccurate. Additionally, although expert opinions are utilized in the proposed method for comprehensive assessments, these opinions may influence the final results due to their inherent subjectivity.

A novel fuzzy dual-aspect MICMAC approach was developed to identify and classify CSFs for optimal TQM practices. This approach allows clustering of CSFs so that decision-makers can prioritize factors according to their dependence and driving powers. Practitioners should concentrate on the CSFs with higher driving powers for successful TQM.

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 recent pandemic caused by coronavirus disease 2019 (COVID-19) has significantly impacted the operational performances of pharmaceutical supply chains (SCs), especially in emerging economies that are critically vulnerable due to their inadequate resources. Finding the possible barriers that continue to impede the sustainable performance of SCs in the post-COVID-19 era has become essential. This study aims to investigate and analyze the barriers to achieving sustainability in the pharmaceutical SC of an emerging economy in a bid to help decision-makers recognize the most influential barriers.

To achieve the goals, two decision-making tools are integrated to analyze the most critical barriers: interpretive structural modeling (ISM) and the matrix of cross-impact multiplications applied to classification (MICMAC). In contrast to other multi-criteria decision-making (MCDM) approaches, ISM develops a hierarchical decision tool for decision-makers and cluster analysis of the barriers using the MICMAC method based on their driving and dependency powers.

The findings reveal that the major barriers are in a four-level hierarchical relationship where “Insufficient SC strategic plans to ensure agility during crisis” acts as the most critical barrier, followed by “Poor information structure among SC contributors,” and “Inadequate risk management policy under pandemic.” Finally, the MICMAC analysis validates the findings from the ISM approach.

This study provides meaningful insights into barriers to achieving sustainability in pharmaceutical SCs in the post-COVID-19 era. The study can help pharmaceutical SC practitioners to better understand what can go wrong in post-COVID-19, and develop actionable strategies to ensure sustainability and resilience in practitioners' SCs.

By replacing traditional fossil fuels, renewable energy (RE) has the potential to become an outstanding sustainable energy supply. However, owing to technological, economic, social and legal constraints, RE is still in its early stages of development. Hence, this paper aims to analyze the sustainable development hindrances in the RE supply chain (RESC).

Twenty-three hindrances to the sustainable development of the RE industry were investigated in this research, which included a review of the expert opinion and literature. Then, a mutual relationship between the hindrances by integrating interpretive structural modeling and decision-making trial and evaluation laboratory in fuzzy environment was established. Furthermore, using the cross-impact matrix multiplication applied to a classification analysis, these hindrances were grouped.

The findings show that the important hindrances are “lack of standards for the RESC (H19), lack of entrepreneurship support (H21), lack of incentives/subsidies to encourage RE producers to compete (H30) and lack of governmental support for sustainable supply chain solutions (H31).

This research provides unique insights into the area of sustainability in RESC. To the best of the authors’ knowledge, this is the first paper to analyze the sustainability hindrances in the RESC.

A better understanding of the characteristics and capabilities of women entrepreneurs can significantly improve their chances of success. Therefore, three studies were conducted for this exploratory paper. We have discovered the characteristics of entrepreneurial intelligence among female entrepreneurs through semi-structured interviews based on conventional content analysis. According to the second study, qualitative meta-synthesis was utilized to identify characteristics of women's entrepreneurial intelligence at the international level. As a third study, we examined the evolutionary relationships of entrepreneurs' intelligence components following the discovery and creation of opportunities.

The present paper was based on three studies. In the first study, 15 female entrepreneurs were interviewed using purposive sampling in the Guilan province of Iran to identify the characteristics of entrepreneurial intelligence at the national level. An inductive content analysis was performed on the data collected through interviews. Using Shannon entropy and qualitative validation, their validity was assessed. In the second study, using a qualitative meta-synthesis, the characteristics of women's entrepreneurial intelligence were identified. Then the results of these two studies were compared with each other. In the third study, according to the results obtained from the first and second studies, the emergence, priority and evolution of entrepreneurial intelligence components in two approaches to discovering and creating entrepreneurial opportunities were determined. For this purpose, interviews were conducted with 12 selected experts using the purposeful sampling method using the fuzzy total interpretive structural modeling (TISM) method.

In the first research, this article identified the components of entrepreneurial intelligence of women entrepreneurs in six categories: entrepreneurial insights, cognitive intelligence, social intelligence, intuitive intelligence, presumptuous intelligence and provocative intelligence. In the second study, the components of entrepreneurial intelligence were compared according to the study at the national level and international literature. Finally, in the third study, the evolution of the components of entrepreneurial intelligence was determined. In the first level, social intelligence, presumptuous intelligence and provocative intelligence are formed first and social intelligence and provocative intelligence have an interactive relationship. In the second level, entrepreneurial insight and cognitive intelligence appear, which, in addition to their interactive relationship, take precedence over the entrepreneur's intuitive intelligence in discovering entrepreneurial opportunities. With the evolution of the components of entrepreneurial intelligence in the opportunity creation approach, it is clear that intuitive intelligence is formed first at the first level and takes precedence. At the second level, there is cognitive intelligence is created. At the third level, motivational intelligence and finally, at the last level, entrepreneurial insight, social intelligence and bold intelligence.

This study has the potential to discover credible and robust approaches for further examining the contextualization of women's entrepreneurial intelligence at both national and international levels, thereby advancing new insights. By conceptualizing various components of entrepreneurial intelligence for the first time and exploring how contextual factors differ across nations and internationally for women's entrepreneurship, this paper challenges the assumption that the characteristics of women's entrepreneurial intelligence are uniform worldwide. It also depicts the evolution of the components of entrepreneurial intelligence.