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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.

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.

Mobile-based payment is increasing exponentially but in the developing country like India, consumers’ perception is highly positive in daily cash transaction. The purpose of this research is to identify and examine the important challenges for mobile wallet (m-wallet) implementation in India. In the wake of COVID-19, one of the transmission mechanisms of this virus has been the coins and paper money passed between a buyer and a seller. As such m-wallet considered as a convenience of payment has become a necessity in light of the pandemic.

The authors explored 19 unique sets of challenges selected from the literature and collected data from 14 experts from private sector, multinational corporations and mixed private and public partnership who have significant knowledge and experience of mobile payment implementation and use in their respective organisations. Also, the authors have used Interpretive Structural Modelling (ISM) methodology in developing a hierarchal model for the identified challenges. The authors implemented Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis to classify the identified challenges.

The ISM-based framework is divided into nine different hierarchical levels. “Lack of strong regulatory compliance (Ch6)” has been recognised as the most important challenge, which inhibited the mobile wallet implementation, whereas “Perception of customers about the value of using mobile wallets (Ch11)” is the most dependent critical challenge. There are seven hierarchical layers in between the top and the bottom level with the varied number of challenges based on their driving and dependence power.

This is the first research to the best of our knowledge that has not only comprehensively reviewed the m-wallet literature but also employed a unique ISM-MICMAC-based approach to develop a framework of challenges for the m-wallet implementation.

Under the background that engineering, procurement and construction (EPC) contracting model is introduced to adapt to the highly fragmented characteristics of prefabricated construction, the schedule management of general contractor is faced with the challenge of dynamic transmission and interaction of construction scheduling-related risk. The purpose of this paper is to develop the hierarchy of prefabricated construction scheduling-related risks from the perspective of the general contractor, and to analyze the transmission mechanism between risks. The paper also aims to further distinguish the difference of the impact degree of scheduling-related risks, and provide reference for formulating the strategy to alleviate the construction delay.

Based on a review of the literature on prefabricated buildings, this paper identifies 22 scheduling-related risks in construction from the perspective of the general contractor. Semi-structured interviews were then conducted to obtain experts' views on the interrelationships among these risks. Following this, their overall structure was determined by using a hierarchical structure established by using interpretive structural modeling (ISM), and Matrice d'Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) technique was applied to classify them into four groups according to their driving and dependence powers.

The results indicate that the 22 scheduling-related risks in construction followed the inherent path of step-by-step transmission, and all of them could cause different degrees of delays in prefabricated construction. Among them, general experience in contracting projects, the use of emerging technologies and the completeness of the relevant standards and specifications were strong drivers of scheduling delays in construction, and should be prioritized by the general contractor in schedule management. The transitive link between scheduling risks can guide them in developing prevention strategies.

Data quality and reliability risks are the major drawbacks of semi-structured interviews. These were minimized by engaging experts with rich theoretical and hands-on experience in prefabricated construction projects. The hierarchical model only reflects static influence relationships, and so dynamic interactions among scheduling-related risks should be studied in future.

The primary value of this study is in its development of a hierarchical model by using the integrated ISM–MICMAC approach that reflects the interaction between scheduling risks in the construction of prefabricated buildings. The hierarchy of these risks and the results of a “driving-dependence power” analysis can guide the general contractor in taking targeted preventive measures to avoid scheduling delays in the construction of prefabricated buildings.

Implementing blockchain in sustainable development goals (SDGs) and environmental, social and governance (ESG)-aligned infrastructure development involves intricate strategic factors. Despite technological advancements, a significant research gap persists, particularly in emerging economies. This study aims to address the challenges related to SDGs and ESG objectives during infrastructure delivery remain problematic, identifying and evaluating critical strategic factors for successful blockchain implementation.

This study employs a three-stage methodology. Initially, 13 strategic factors are identified through a literature review and validated by conducting semi-structured interviews with six experts. In the second stage, the data were collected from nine additional experts. In the final stage, the collected data undergoes analysis using interpretive structural modeling (ISM)–cross-impact matrix multiplication applied to classification (MICMAC), aiming to identify and evaluate the independent and dependent powers of strategic factors driving blockchain implementation in infrastructure development for SDGs and ESG objectives.

The study’s findings highlight three significant independent factors crucial for successfully integrating blockchain technology (BT) into infrastructure development for SDGs and ESG goals: data security (F4), identity management (F8) and supply chain management (F7). The study unravels these factors, hierarchical relationships and dependencies by applying the MICMAC and ISM techniques, emphasizing their interconnectedness.

This study highlights critical strategic factors for successful blockchain integration in SDG and ESG-aligned infrastructure development, offering insights for policymakers and practitioners while emphasizing the importance of training and infrastructure support in advancing sustainable practices.

This paper aims to identify factors that affect the sports tourism destination's competitiveness on a small island. Hence, this study looks at and evaluates these factors. The study then comes up with a model that clarifies the interrelationships between these factors.

The authors broke down the data analysis process into three steps. The first step was to conduct a literature review and use industry and academia experts' help to determine the essential aspects (fuzzy Delphi method). Then, a hierarchical model was developed, and the factors were categorised using the interpretive structural modelling (ISM) approach. Factors' driving and dependency power were also determined using MICMAC analysis.

This work has identified 13 key factors related to the sports tourism destination's competitiveness on a small island. For a small island like Kish Island, the two independent variables (government support and destination political stability) that define the institutional framework for the destination are most important. Building corresponding competitive and support strategies to address these two independent variables is thus beneficial.

The research's results provide decision-makers, practitioners, and researchers with new insights into the hierarchical model of determinants. The study will fill the existing gap between theory and practice.

Sports tourism destination managers on small islands may benefit from the proposed model since the model will enable them to organise the managers' priorities better to enhance the managers' destinations' competitiveness and provide tourists with a more accurate depiction of the destination.

According to the authors' knowledge, the research design presented in this article has provided the first attempt to hierarchical analyse these factors and develop a model for sports tourism destination competitiveness on small islands and destinations with less-developed economies. This study fills the gap in the destination competitiveness and sports tourism literature by not only identifying the key influencing factors but also examining the interactions between these factors and providing empirical evidence supporting their relationships.

Suppliers and marketers have started planning toward postpandemic scenarios where logistics and retail will happen in a new way with the help of technological advances. This shift means new challenges for manufacturers, suppliers and retailers, and there is a need for strategic sourcing decisions for a robust supply chain system, logistics and on-time delivery system, as consumers have shown a positive change in online buying behavior. Furthermore, with digital transformation, customers are expected to not return to traditional buying. Hence, it becomes essential to identify the factors acting as enablers of online purchase behavior for sustainable digital business. This study aims to analyze the positive shifts in online purchasing by consumers, identify and model the enablers of positive transformations in online purchasing by consumers.

The interpretative structural modeling (ISM) technique is used to draw the interrelationships among the variables and their impact on online buying. A context-oriented relationship among the factors has been set up through the expert opinion technique. A total of 40 specialists have been approached for this. ISM with Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis was used to prioritize these drivers, identify the most critical factors and establish a driver-dependence relationship among these drivers.

Several significant categories of enablers like health, trust, convenience, work from home, referral buying, panic purchase and overstocking possess a strong influence on the shift to online due to the pandemic. The results will help the policymakers, suppliers, retailers, managers and practitioners with insights to plan, prepare for challenges and make decisions toward preparation and shifting to the emergent digital world. In addition, the study provides academicians scope for further research in the related area.

Consumer behavior significantly impacts retail and supply chain business, as it is an interface with the customer and links between a manufacturer and a customer. This study provides an insight into the shift in purchase behavior which can help suppliers in this transition phase to be better prepared for tomorrow to achieve sustainable competitive advantage.

This study assists practitioners and researchers in understanding the interrelationships among the factors using ISM-MICMAC analysis in a realistic way rather than daydreaming with overambitious goals.

Supply chain digitalization (SCD) facilitates a competitive advantage and is equally challenging. A literature analysis reveals that research related to SCD is available, but analysis of key barriers and their interrelations for effective SCD is limited. This paper aims to identify and find out interrelationships among the key barriers affecting SCD in the electronic supply chain (SC).

Interpretive structural modeling (ISM) is used to identify the contextual relationship among the SCD barriers. The model is validated using MICMAC analysis.

The findings of this study have implications at both theoretical and practical levels. The proposed ISM model can help organizations analyze and interpret interrelationships among barriers to SCD. It can lead to better insights and understanding for minimizing the dominant barriers to effective SCD.

This study tries to bridge the gap in identifying and finding the interrelationships among the key barriers to effective SCD in an electronic SC. The result assists practicing managers and academicians in effective decision-making on the SCD process.

Green lean six sigma (GLSS) is a sustainable development approach that leads to improved patient care with improved safety and quality of service to patients. This study aims to identify, study, model and analyze GLSS success factors for the Indian health-care facility.

Interpretive structural modeling (ISM) and Impact Matrix Cross-Reference Multiplication Applied to a Classification analyses have been used to understand the hierarchical structure among the GLSS success factors. This enabled the development of dependency relationships between success factors, in particular, which factors support the development of other factors.

Specifically, this study found that the success factors “commitment of management” and “financial availability” are the most critical to GLSS implementation success, as they support the development of all other success factors. Meanwhile “embedding sustainable measures at each stage of the service”; “the capability and effectiveness of real-time data collection”; and “feedback and corrective actions” most directly support the GLSS implementation in the health-care facility and serve as the final indicators of implementation progress.

The major implication of this research work lies in suggesting a direction for practitioners to execute the GLSS approach through a systematic understanding of classification and structural relationships among different enablers. This study also facilitates health-care managers to explore different GL wastes in hospitals and challenges to sustainability pursuits in health-care that assist in an organization’s efforts toward sustainable development.

This research work is the first of its kind that deals with the identification and analysis of the prominent factors that foster the inclusive implementation of GLSS within the health-care facility.

Blockchain technology (BCT) can be used for a wide variety of applications across domains and can bring many benefits. BCT-based applications can be beneficial for the government as well as businesses. Despite the many promises, BCT implementation lags behind. The purpose of this research is to identify a roadmap of critical implementation challenges that influence BCT implementation by governments.

The study develops an ISM-based model spread across seven levels to analyze the inter-relationship among the selected BCT challenges. The MICMAC analysis further helps in evaluating the variables based on their driving power and dependencies.

The findings show that all challenges have a strong impact on implementing BCT. The foundation for implementation BCT is to define standards and develop appropriate regulations. Next, the findings show the need for a shared infrastructure meeting the basic technical and societal requirements and developing viable business models to advance BCT implementation. Many challenges hinder the development of blockchain applications meeting the technical and ethical requirements.

Existing research has analyzed the relationship among challenges. To the best of the authors' knowledge this is the first paper to collate these implementation challenges and incorporate them to develop a hierarchical model using interpretive structural modeling technique. The results can be used to prioritize the tackling of the challenges.