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The main objective of this paper is to justify the implementation of blockchain (BC) over the traditional method deployed in the supply chain (SC) after using the fuzzy–analytic network process (fuzzy-ANP) application. Over the past two decades, the overall product cost is affected by the SC at a global level. Organizations are working on their existing SC for improving their performance. BC technology is a newly emerging technology and magnetizes the attention of researchers and industrialists. This technology is still at the initial stage, and only little investigation is available in the literature and it has not been much investigated by researchers.

Literature and expert opinion interpretation in BC characteristics are further analyzed and modeled using fuzzy–interpretive structural modeling (fuzzy-ISM), fuzzy-MICMAC and fuzzy-ANP. The combined approach of both fuzzy-ISM and fuzzy-MICMAC is applied to identify the common drivers to integrate the BC technology in the light of efficient supply chain management (SCM).

Comparative analysis between traditional and BC-based supply chain (BCSC) using fuzzy-ANP is carried out, considering the common driving characteristics. The proposed integrated (combined) approach of fuzzy-ISM, fuzzy-MICMAC and Fuzzy-ANP found that integration of BC with SCM is better prioritized than traditional supply chain management (TSCM). The findings in the article endorse that the TSCM can be made efficient by integrating the BC technology considering five most driving characteristics, namely, data safety and decentralization, accessibility, documentation, data management and quality.

The current proposed research work identifies 12 characteristics after studying numerous literature reviews and having a discussion with SC experts with knowledge of BC. The integrated approach of fuzzy-ISM and fuzzy-MICMAC is implemented here. After that, fuzzy-ANP is used to give ranking among BCSCM and TSCM. The study carried out in this article motivates industries to implement BC in their SC system. It will reduce the transaction cost, documentation work, save time and eliminate human error at the national and international levels. The common characteristics identified in this proposed work would help in managerial decisions for the adoption of BC to ensure that the system becomes more transparent, easily traceable and finally improve the performance.

Big Data can be utilised for efficient use of resources and to provide better services to the resident in order to enhance the delivery of urban services and create sustainable build environment. However, the adoption of Big Data faces many challenges at the implementation level. Therefore, the purpose of this paper is to identify the challenges towards the efficient application of Big Data in smart cities development and analyse the inter-relationships.

The 14 Big Data challenges are identified through the literature review and validated with the expert’s feedback. After that the inter-relationships among the identified challenges are developed using an integrated approach of fuzzy Interpretive Structural Modelling (fuzzy-ISM) and fuzzy Decision-Making Trial and Evaluation Laboratory (fuzzy-DEMATEL).

Evaluation of interrelationships among the challenges suggests that diverse population in smart cities and lack of infrastructure are the significant challenges that impede the integration of Big Data in the development of smart cities.

This study will enable practitioners, policy planners involved in smart city projects in tackling the challenges in an optimised manner for the hindrance free and accelerated development of smart cities.

This research is an initial effort to develop an interpretive structural model of Big Data challenges for smart cities development which gives a clearer picture of how the identified challenges interact with each other.

Sustainability is a very important factor to be considered in the supply chain (SC) of any industry. Agricultural industry needs to be addressed even more importantly with the tools of sustainability as it concerns the life of millions. This paper explores the critical barriers (CBs) in the sustainable supply chains (SSCs) of makhana industry located in the northern part of India and seeks to design a model for the researchers and the managers who want to work in this industry.

Initially, the CBs were identified with the help of an extensive literature review of sustainability in SCs for agri-industry and discussion with makhana industry experts (consisting of managers and senior managers) and academicians (consisting of professors and research scholars). The study uses the multi-criteria decision-making (MCDM) technique, namely interpretive structural modeling (ISM) and fuzzy ISM to develop the model. The study finally validates the model using Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis.

The obtained results indicate that, in the SSC of makhana industry, the role of “Lack of adoption of organic agricultural management techniques” (CB2), “Lack of modern techniques (CB4)”, “Multiple intermediaries” (CB5), “Weak socio-economic conditions” (CB7) and “Lack of proper knowledge” (CB1) are very significant. These barriers are needed to be addressed first as they have the highest driving power and other barriers are directly driven by these CBs.

The paper has included seven experts, and the interrelationship between CBs has been developed on the basis of their knowledge and discussion, so the results may be a little bias. Moreover, the paper has obtained the results using the ISM and fuzzy ISM by considering ten CBs; the researchers can explore this research by including more CBs and validate the results using other MCDM techniques like fuzzy-decision making trial and evaluation laboratory (DEMATEL), fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Best Worst Method (BWM).

This study is unique as per industry point of view and may help the researchers and managers to explore the field of makhana.

A spurt in the usage of additive manufacturing (AM) is observed in industrial applications to produce final parts along with rapid prototyping and rapid tooling. Despite the potential benefits of on-demand and on-location production of customised or complex shape parts, widespread implementation of this disruptive production technology is not yet visible. The purpose of this paper is to examine the various barriers to implement AM in the Indian automotive sector and analyse interrelations among them.

Based on the extant literature and discussions with industry experts, ten major barriers are identified. The authors use a modified Fuzzy interpretive structural modelling (Fuzzy-ISM) method to derive strengths of relationships among these barriers, develop hierarchical levels, and thereafter group and rank these barriers.

ISM diagraph is developed to demonstrate how the barriers drive one another. Production technology capabilities and government support emerge as the most critical factors, with high driving power and medium dependence.

While identified barriers may be similar across the automotive industry, generalisation of results for interrelationships and ranks in other industries may be limited.

The findings may be useful to managers to develop suitable mitigation strategies, and take more informed decisions, with individual focus, level focus or cluster focus.

Findings clearly establish that the role of management and government is crucial in mitigating workers’ resistance to AM implementation.

This paper contributes to AM literature by the structured presentation of the barriers to implement AM in the Indian automotive sector. It also extends the Fuzzy-ISM method by presenting calculation of indirect relations using the appropriate max-product composition and in ranking the barriers.

Various publications have extensively documented the advantages of a circular economy in ensuring sustainability and limiting climate change. Despite academic records emphasising the need to adopt this business strategy, entrepreneurs in developing countries prefer linear economies. This reluctance is attributable to several factors, including insufficient infrastructure and technology, limited financial access, inadequate education systems and the prevalence of informal enterprises. Therefore, a thorough analysis of the underlying economic, political and social conditions is required to identify the drivers of circular economies (CEs) and their contribution to entrepreneurship in developing countries.

In this study, the authors first conducted a comprehensive quantitative literature review based on LangChain to identify the critical CE drivers from the social, technological and organisational perspectives. Based on the input from the expert panel of Iranian academic and industry professionals, the authors applied an integrated fuzzy interpretive structural modelling and cross-impact matrix multiplication approach to classification (Fuzzy-ISM-MICMAC) to investigate the chronology of entrepreneurial drivers.

Level-based model results reveal entrepreneurial drivers in developing nations and their interrelationships, specifically underlining the importance of supply chain factors and stakeholder preferences. Thus, the differences between the perception of the main drivers in developed and developing economies can be identified, with the former paying particular attention to legislative and financial factors. The study's findings contribute to conserving resources, reducing waste and adopting more sustainable corporate practices, thereby assisting developing countries in achieving development goals.

This study employs an innovative quantitative systematic literature review approach that relies on a large language model to identify the drivers of the CE. Furthermore, it adopts a systematic approach to examine the enablers of the CE rather than a narrow and individual perspective of the entrepreneurial drivers. The study employs the fuzzy ISM MICMAC technique to showcase the prioritisation of entrepreneurial prospects in emerging economies.

The purpose of this study is to analyse various risks associated with third-party logistics (3PL) in the coffee supply chain and to present a framework that computes the influence of these risks on the critical success factors of the coffee supply chain.

The risks have been identified through a comprehensive literature review and validation by industry experts. The paper utilises an interpretive structural modelling (ISM) methodology for developing a hierarchical relationship among the CSFs. Furthermore, fuzzy MICMAC analysis is carried out to categorise these CSFs based on their driving power and dependence value. The fuzzy technique for order preferences by the similarity of an ideal solution (fuzzy-TOPSIS) approach has been applied to prioritise the risks associated with 3PL based on their ability to influence the CSFs of the coffee SC. Furthermore, we performed a sensitivity analysis to analyse the stability of the results obtained in this study.

This study illustrates ten risks associated with 3PL and five CSFs in the coffee supply chain. The analysis revealed that coffee enterprises need to develop a balanced pricing strategy to ensure a sustainable competitive advantage, whereas the lack of direct customer communication is the most dominant 3PL risk affecting the CSFs.

This research provides coffee enterprises with a generalised framework with set parameters that can be used to attain a successful coffee supply chain in any developing nation.

The study contributes to the literature by being the first kind of study, which has used fuzzy ISM-MICMAC to analyse the CSFs of the coffee supply chain and fuzzy-TOPSIS for analysing the impact of various risks associated with the 3PL in the coffee supply chain. Thus, this work can be considered a benchmark for future research and advancement in the coffee business field.

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.

The purpose of this paper is to develop an interpretive structural modeling (ISM) model to investigate the critical success factors (CSF) and the extent of CSF's influence in the implementation of lean industry 4.0 in manufacturing supply chain.

The study has been carried out with the help of the latest literature followed by brainstorming sessions with experts. The experts were the managers from the industries, assistant professors, and research scholars from academia working in this domain. Finally, a structured model is formed using ISM methodology for the analysis of the CSFs followed by matrice d'impacts croisés multiplication appliquée á un classment (MIAMAC) Analysis for the validation of the model.

The study identifies robotics, virtual and augmented reality and cloud computing as the main CSFs which are responsible to drive all the identified CSFs. However the CSF professional training and development (PTD) has been identified as the weakest driver but having the highest dependent power.

The study has included nine CSFs and the contextual relationships between the CSFs are based on the knowledge and experience of the experts, which may be biased. Moreover, the paper has covered the ISM approach, and the same thing can be validated using the fuzzy-ISM and other multi-criteria decision-making (MCDM) techniques.

This investigation of the CSFs in the lean industry 4.0 is original and the identified CSFs are the result of the literature reviews and an extensive discussion from the experts. The paper uses the complete experience of the respective experts to make this work more effective and original.

The purpose of this paper is to investigate the variables for the non-existence of transparency information in red meat supply chains operating in Gulf cooperation council (GCC) countries. The study provides a synthesis and proposes a hierarchy-based model among the identified inhibitors of transparency with their respective importance.

The synthesis and prioritization of inhibitors are done on the basis of an extensive literature review as well as consultation with academicians and meat supply chain professionals. Using semi-structured interviews and Fuzzy-Interpretive Structure Modeling (F-ISM) approach, the research presents a structured model of the identified inhibitors.

The research has two major contributions, first, it presents variables that can be considered as inhibitors for transparency in a supply chain. Second, utilizing the F-ISM model it shows that there exists a group of inhibitors having a high control power and low reliance and are of strategic importance. These variables require utmost attention to make implementation of transparency initiatives to be successful.

At a time when management of red meat supply chain is crucial for the food security in the GCC region, these findings will be immensely helpful for the government and industry professionals in developing suitable policies to regulate the red meat supply chains. The study would be particularly relevant when the upstream side is in a developing nation and the need is to avoid any health hazards resulting from the consumption of contagious meat and jeopardize the whole supply chain.

Cases of discarding plausible contagious meat into sea or abandoning it in open to create environment hazards can be minimized if transparency is implemented in meat supply chain by considering the variables and their relationships discussed in the research.

Key issues related to transparency in a food supply chain are discussed and interconnectedness of critical factors for the red meat supply chain are delineated.

In Gulf Cooperation Council (GCC) countries, entrepreneurship is not a very popular option among women. The low rate of entrepreneurship as a career choice among females is attributed to factors that are specific to females in GCC region. The purpose of this paper is to understand these variables and their interrelationships that lead to low rate of adoption of entrepreneurship as a career choice among females in GCC region.

The research followed a two-stage process; first, a comprehensive literature review was conducted to identify barriers to female entrepreneurship in GCC region. In the second stage, the identified barriers were modeled using fuzzy interpretive structural modeling technique to construct a hierarchical structure and the impact matrix cross-reference multiplication applied to a classification approach to analyze the control power and dependence among these barriers.

The research shows that there exists a group of barriers having a high driving power and low dependence requiring maximum attention and of strategic importance, while another group consists of those variables that have high dependence and are the resultant actions.

This research will help policy makers to focus on variables that are most important and requires immediate attention for the development and empowerment of female entrepreneurship in the GCC region. The paper suggests future course of action to provide an impetus to efforts aimed at improving female entrepreneurship in the GCC region.

The policy makers in the GCC region have accorded high priority to entrepreneurship to reduce the dependence of population on government jobs. This research is one of the few studies on female entrepreneurship conducted in GCC region to understand the impediments that limit the adoption of entrepreneurship as a career choice by women.