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The primary purpose of this study is to examine the factors leading to shopping cart abandonment and construct a model depicting interrelationship among them using interpretive structural modeling (ISM) and Matriced Impact Croises Multiplication Appliquee an un Classement (MICMAC).

Initially, 20 factors leading to shopping cart abandonment were extracted through a systematic literature review and expert opinions. Fifteen factors were finalized using the importance index and CIMTC method, for which consistency has been checked in SPSS software through a statistical reliability test. Finally, ISM and MICMAC approach is used to develop a model depicting the contextual relationship among finalized factors of shopping cart abandonment.

The ISM model depicts a technical glitch (SC8), cash on delivery not available (SC4), bad checkout interface (SC9), just browsing (SC11), and lack of physical examination (SC12) are drivers or independent factors. Additionally, four quadrants have been formulated in MICMAC analysis based on their dependency and driving power. This facilitates technical managers of e-commerce companies to focus more on factors leading to shopping cart abandonment according to their dependency and driving power.

Taking an expert’s opinion as a base may affect the results of the study due to biases based on subjectivity.

This study’s outcomes would accommodate practitioners, researchers, and multinational or national companies to indulge in e-commerce to anticipate factors restricting the general public from online shopping.

For the successful running of an e-commerce business and to retain the confidence of e-shoppers, every e-commerce company must make a strategy for controlling factors leading to shopping cart abandonment at the initial stage. So, this paper attempts to highlight the main factors leading to shopping cart abandonment and interrelate them using ISM and MICMAC approaches. It provides a clear path to technical heads, researchers, and consultants for handling these shopping cart abandonment factors.

Today’s marketplace has witnessed intense competitive pressures and high levels of uncertainty and disruption. Therefore, supply chains require agility to obtain a sustainable competitive advantage and cope with uncertainties as well as disruptions. Although a wide range of studies exists on supply chain agility (SCA) from the perspective of antecedents or consequences, there is little research on the investigation of enablers of SCA and their relations among them. Furthermore, the literature has investigated proactive and reactive enablers for enhancing SCA, but most studies have not sufficiently framed their analysis of both aspects synchronically. This paper aims to find out the interrelationships among the proactive and reactive enablers for enhancing SCA.

An extensive literature review has been conducted to identify SCA enablers and a Delphi study has been performed to elucidate SCA enablers in the manufacturing industry in Turkey. Interpretive structural modeling (ISM) has been used to identify the contextual relationship among the SCA enablers, and the model has been validated based on Matriced Impact Croises Multiplication Appliquee a un Classement (MICMAC) analysis.

On theoretical and practical levels, the proposed ISM model in this study can help organizations analyze and interpret interrelationships among enablers of SCA. For managers, it can provide better insights and understanding of the facilitators of SCA to enhance the effectiveness of the supply chain and cope with uncertainties and turbulence. According to results, enhancing “supply and demand side competency”, “delivery speed” and “strategic sourcing” are the most significant enablers of SCA.

The study extends the existing literature related to the enablers of SCA by modeling the proactive and reactive enablers of SCA based on the Al Humdan et al. (2020) classification. Arranging the enablers of SCA in a hierarchy and classifying the enablers into different levels with the help of the ISM-MICMAC approach is an exclusive effort to achieve successful management of the supply chain.

ISM technique is one of the tools of interest in soft operations research. The soft nature of this technique has made inevitable use of indeterminacy theories. The present research attempts to develop ISM technique and MICMAC analysis in a neutrosophic space due to the complexity and uncertainty of the decision-making environment.

In this study, single-valued triangular neutrosophic numbers is used to develop Neutrosophic ISM (NISM) and Neutrosophic MICMAC (NMICMAC). First, the general algorithm of NISM and NMICMAC is provided. In the following, the complete description of NISM steps including level value determination, Factor Leveling Algorithm and NISM digraph algorithm are presented. Finally, a description of the NMICMAC steps is described.

An illustrative example – supplier selection problem – is given to verify the effectiveness of the proposed method and in the discussion section; the comparison and analysis of different aspects of the NISM with the previous methods are discussed.

In this study, NISM and NMICMAC are presented for the first time, so that each pairwise comparison judgment is provided as single valued triangular neutrosophic numbers. The development of the model is continued until the final stages of calculations with neutrosophic numbers, and only in the final stage the results are crispy presented. In addition, not only the factors of process are leveled, but at each level the factors are lined up and their importance is determined.

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.

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 success rate of Lean Six Sigma (LSS) in the service industries is dismally poor, and most organisations discontinue LSS initiatives prematurely. This paper aims to identify the LSS barriers (LSSBs) and analyse their interaction via a hierarchical model developed by using interpretive structural modelling (ISM) and Fuzzy Matriced Impacts Croise’s Multiplication Appliqué à un Classement (MICMAC). These allow the LSS execution and implementation to be much more effective and avoid the high cost of implementation.

A structural review of the literature and interviews with experts and professionals from the service industries in the UAE supplied data wherewith to identify LSSBs. Sixteen LSSBs were determined and analysed using ISM and the MICMAC approach to discover the strong drivers and highly dependent barriers. The Fuzzy set was included in the MICMAC analysis to obtain a more precise output and create an effective hierarchical model of the barriers.

The research findings suggest that the top barriers to LSS implementation in service industries are lack of top management commitment, lack of customer focus, resistance to change management and lack of alignment between the LSS and organisational strategy. A deeper analysis using the Fuzzy-MICMAC approach categorises these barriers on the basis of their driving power and dependency.

The relationships between paired LSSBs were obtained through an experts’ interpretations of limited numbers in one country. Conducting a large-scale survey with a more comprehensive demographic or deep focus in one service industry might deepen our understanding of the interactions of LSSBs and models.

The developed ISM that model suggests that the dependencies and relationships among the barriers must be accurately determined so as to remove the collaborative effect of barriers on the implementation process is at the earliest opportunity. This would improve service companies’ competitive advantage and profitability, drive out waste and reduce the cost associated with poor quality. Similarly, academicians may advocate ways in various issues can contribute to improve LSSBs for amended LSS implementation now that business services are booming in the fourth industrial revolution.

The structural model was developed holistically on the basis of the inputs from practitioners and academicians to ensure its practical validity. Though the model has theoretical foundations, its practical applicability is a key factor in its development, so this approach was helpful for practitioner wanted to focus on removing the key dominant barriers and be able to deploy LSS concepts smoothly in service industries. The results support the proposition that top management is a crucial factor for LSS project implementation, whatever the complexity of the research methodology and the nature of the service industries.

The purpose of this paper is to identify the flexible manufacturing system performance variables and analyze the interactions among these variables. Interpretive structural modeling (ISM) has been reported for this but no study has been done regarding the interaction of its variables. Therefore, fuzzy TISM (total ISM) has been applied to deduce the relationship and interactions between the variables and driving and dependence power of these variables are examined by fuzzy MICMAC.

Fuzzy TISM and fuzzy MICMAC analysis have been applied to deduce the relationship and interactions among the variables and driving and dependence power of these variables are examined by fuzzy MICMAC.

In total, 15 variables have been identified from the extensive literature review. The result showed that automation, use of automated material handling, an effect of tool life and rework percentage have high driving power and weak dependence power in the fuzzy TISM model and fuzzy MICMAC analysis. These are also at the lowest level in the hierarchy in the fuzzy TISM model.

Fuzzy TISM model has been suggested for manufacturing industries with fuzzy MICMAC analysis. This proposed approach is a novel attempt to integrate TISM approach with the fuzzy sets. The integration of TISM with fuzzy sets provides flexibility to decision-makers to further understand the level of influences of one criterion over another, which was earlier present only in the form of binary (0, 1) numbers; 0 represents no influence and 1 represents influence.

Lean and green strategies are good options to increase the environmental and operational performance of manufacturing industries. The purpose of this paper is to identify the critical success factors (CSFs) to implement green lean practices (GLPs) in manufacturing industries through the review of the literature and to develop a conceptual model after analysing the fundamental facilitating factors by using ISM-MICMAC approach.

The methodology consists of identifying 12 critical success factors (CSFs) for the green lean implementation by reviewing the relevant available literature. The views of eight experts are valued for inter-relationships of these factors. ISM-MICMAC approach is used for analysing the relations between factors and to develop a conceptual model for green lean implementation.

Twelve CSFs are identified through a review of the literature to adopt GLPs in manufacturing industries. This paper has established the relationships among 12 identified CSFs using the ISM methodology. This paper analyses the dependence power and the driving power of identified CSFs with the help of MICMAC analysis. “Top management commitment” and “Government support” are the most significant CSFs implement GLPs successfully.

The ISM model presented in this study is based on expert opinions. But expert opinions may be biased as these are based on their own judgements. However, the proposed ISM based model needs statistically validations. The ISM model in the present study may be tested in real-world situations of manufacturing industries where results obtained may be different.

This study may provide a useful input for academicians and managers of industries to differentiate between independent and dependent CSFs and their mutual relationships which would help them to focus on those key CSFs that are most significant to implement GLPs.

A conceptual ISM model of identified CSFs shows the different levels of these CSFs. This model may help the manufacturer to implement the green-lean strategies. It may also support policymakers towards adopting GLPs. Arranging CSFs in a hierarchy and to categorise the CSFs into different levels with the help of ISM-MICMAC is an exclusive effort in the area of green lean engagement.

Today the role of industry 4.0 plays a very important role in enhancing any supply chain network, as the industry 4.0 supply chain uses Big Data and advanced analytics to inform the complete visibility. Latest data are available to bring clarity and support real-time decision-making in the entire supply chain that’s why adopting optimization techniques such as lean manufacturing and lean supply chain concept for enhancing the supply chain network of the organizations is a good idea and would benefit them in increasing their cost efficiency and productivity. The purpose of this work is to develop a technique, which may be useful for future researchers and managers to identify and classification of the significant lean supply chain enablers.

In this paper, the authors considered hybrid analytical hierarchy process to find the ranking of the identified lean supply chain enablers by calculating their weightage. Interpretive structural modeling (ISM) is applied to develop the structural interrelationship among various lean supply chain management enablers. Considering the results obtained from ISM the Matrices d'Impacts Croises Multiplication Appliqué a un Classement (MICMAC) analysis is done to identify the driving and dependence power of Lean Supply Chain Management Enablers (LSCMEs).

Further, the best results applying these methodologies could be used to analyze their inter-relationships for successful Lean supply chain management implementation in an organization. The authors developed an integrated model after the identification of 20 key LSCMEs, which is very helpful to identify and classify the important enablers by ISM methodology and explore the direct and indirect effects of each enabler by MICMAC analysis on the LSCM implementation. This will help organizations optimize their supply chain by selective control of lean enablers.

For lean manufacturing practitioners, the result of the study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process, as well as in enhancing the supply chain.

This paper is the first research paper that considered firstly deep literature review of identified lean supply chain enablers and second developed structured modeling of various lean enablers of supply chain with the help of various methodologies.

The purpose of this paper is to identify and develop the relationships among the green supply chain management enablers (GSCMEs), to understand mutual influences of these GSCMEs on green supply chain management (GSCM) implementation, and to find out the driving and the dependence power of GSCMEs.

This paper has identified 35 GSCMEs on the basis of literature review and the opinions of experts from academia and industry. A nationwide questionnaire-based survey has been conducted to rank these identified GSCMEs. The outcomes of the survey and interpretive structural modeling (ISM) methodology have been applied to evolve mutual relationships among GSCMEs, which helps to reveal the direct and indirect effects of each GSCMEs. The results of the ISM are used as an input to the fuzzy Matriced’ Impacts Croisés Multiplication Appliquéeá un Classement (MICMAC) analysis, to identify the driving and the dependence power of GSCMEs.

Out of 35 GSCMEs 29 GSCMEs (mean⩾3.00) have been considered for analysis through a nationwide questionnaire-based survey on Indian automobile organizations. The integrated approach is developed, since the ISM model provides only binary relationship among GSCMEs, while fuzzy MICMAC analysis provides precise analysis related to driving and the dependence power of GSCMEs.

The weightage for ISM model development and fuzzy MICMAC are obtained through the judgment of few industry experts. It is the only subjective judgment and any biasing by the person who is judging might influence the final result.

The study provides important guidelines for both practitioners, as well as the academicians. The practitioners need to focus on these GSCMEs more carefully during GSCM implementation. GSCM managers may strategically plan its long-term growth to meet GSCM action plan. While the academicians may be encouraged to categorize different issues, which are significant in addressing these GSCMEs.

Arrangement of GSCMEs in a hierarchy, the categorization into the driver and dependent categories, and fuzzy MICMAC are an exclusive effort in the area of GSCM implementation.