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The purpose of this paper is to develop a comprehensive framework to identify and classify key medical tourism enablers (MTEs) and to study the direct and indirect effects of each enabler on the growth of medical tourism in India.

In this paper, an integrated approach using interpretive structural modeling (ISM) and Fuzzy Matrice d'Impacts Croisés Multiplication Appliquée á un Classement (FMICMAC) analysis has been developed to identify and classify the key MTEs, typically identified by a comprehensive review of literature and expert opinion. The key enablers are also modeled to find their role and mutual influence.

The key finding of this modeling helps to identify and classify the enablers which may be useful for medical tourism decision makers to employ this model for formulating strategies in order to overcome challenges and to become a preferred medical tourism destination. Integrated model reveals enablers such as medicine insurance coverage, international healthcare collaboration, and efficient information system as dependent enablers. No enabler is found to be autonomous enablers. The important enablers like healthcare infrastructure facilities and global competition are found as the linkage enablers. Research in medicine and pharmaceutical science, medical tourism market, transplantation law, top management commitment, national healthcare policy, competent medical and para-medical staffs are found as the independent enablers. Integrated model also establishes the direct and indirect relationship among various enablers.

The research provides an integrated model using ISM and FMICMAC to identify and classify various key enablers of medical tourism in India. In conventional cross-impact matrix multiplication applied to classification analysis, binary relationship of various enablers is considered. FMICMAC analysis helps to establish possibility of relationship among various enablers so that low-key hidden factors can be identified. The low-key hidden factors may initially exhibit marginal influence but they may show significant influence later on during analysis. The uncertainty and fuzziness of relationship among various enablers can be conveniently handled by FMICMAC and expert opinions can easily be captured. This research will help medical tourism decision makers to select right enablers for the growth of medical tourism in India.

The purpose of this study is to analyze the interaction between the enablers and barriers to successful project control system (PCS) implementation in petroleum and chemical projects and to provide a knowledge-based foundation for the evaluation of the dominant factors of PCS success.

An integrated approach of interpretive structural modeling (ISM) and cross-impact matrix multiplication applied to classification (Matrice d’Impacts Croisés Multiplication Appliquée à un Classement) were used. An expert group of industry professionals and academics was consulted at the initial stage as an input for the ISM methodology to explore the contextual relationships among the critical factors to PCS implementation in petroleum and chemical projects.

This study identifies three dominants enablers of PCS success: the technical skills of the project controller; explicitly defined roles of project team members; and an accurate work breakdown structure. Additionally, six dominant barriers to successful PCS implementation have been identified: lack of standard processes, vague contract deliverables, unclear project goals, unclear project milestones, disparate control system between owner and contractor and lack of information communication. Organizations in the petroleum and chemical sectors should put more effort into the development of these factors to optimize the implementation of PCS.

A limitation of this current study is its focus on the petroleum and chemical sectors in Saudi Arabia. It is expected that the findings will have wider applicability, but further studies in different sectors and geographic regions should be undertaken to validate this assumption. In this study, a second validation expert group was used to ensure the reliability of the results as much as was possible. However, further studies could be completed to evaluate alternate classification schemes and to test the robustness of the expert consensus and techniques reported, in this study, including an increased number of experts.

The present study makes some practical contributions to the project delivery process. It provides different project stakeholders with a better understanding of the factors that improve PCS outcomes and helps project owners and contractors to evaluate the PCS used by both parties to understand how they might be aligned for successful project execution.

The successful implementation of PCS is a key contributor to the total project delivery process. Although there is growing attention on PCS as a critical project management tool in petroleum and chemical projects, the literature in project management shows that PCS implementation has not been taken up as much as originally anticipated. This paper will address this gap by focusing on improving the take-up of strategical PCS implementation through the identification of six dominant enablers and nine dominant barriers as driver factors of success.

The concept of “Smart Cities” is gaining prominence across the world as a solution to effectively address the issues or impediments faced by cities due to rapid urbanization. The purpose of this paper is to identify the key factors which form the primary basis for the implementation of “Smart Cities”. Particularly, this paper aims to analyse the contextual relationship and driving/dependence power of these key factors and model these using the total interpretive structural modelling (“TISM”) framework.

The key factors which form the basis for the implementation of Smart Cities were identified through an evaluation of the literature on “Smart Cities” and expert opinions. Thereon, the contextual relationship between these key factors was examined with the help of experts. Thereafter, these key factors were modelled using the total interpretive structured modelling (“TISM”) framework. Cross-impact matrix multiplication applied to classification (MICMAC) analysis was further applied to classify the factors. It is pertinent to note that the driving power and dependence of these key factors were also reviewed.

This paper establishes a TISM of the key factors for the implementation of “Smart Cities” which will aid in examining the interrelationship among the factors and will also identify the hierarchy among these factors. On extensive examination of the literature and expert opinions on “Smart Cities”, it can be asserted through TISM that quality of life (F1), e-services adoption (F5) and economic growth (F8) are the leading factors in establishing “Smart Cities”. Furthermore, it must be noted that the MICMAC analysis and driving-dependence graph helps in classifying the key factors as autonomous factors, drivers, linkages and outcomes, which assists in comprehending which factors possess driver power and which are exhibiting dependency.

The contribution lies in the authentic manner in which this paper attempts to use the TISM approach combined with MICMAC analysis to model key factors for the implementation of “Smart Cities”; which would aid and assist policymakers and practitioners to construct a structural framework for the implementation of “Smart Cities” through identification of drivers, linkages and outcomes.

This paper aims to discover and analyze the challenges hampering blockchain technology’s (BT’s) implementation in the Indian health-care sector. A total of 18 challenges have been prioritized and modeled based on an extensive literature search and professional views.

An integrated multi-criteria decision-making approach has been used in two phases. Best worst method (BWM) is used in the first phase to prioritize the challenges with sensitivity analysis to validate the findings and eliminate a few challenges. In the second phase, interpretive structural modeling is applied to the remaining 15 challenges to obtain relative relationships among them with cross-impact matrix multiplication applied to classification analysis for their categorization.

The study’s results reveal that limited knowledge and expertise, cost and risk involved, technical issues, lack of clear regulations, resistance to change and lack of top management support are the top-ranked or high-intensity challenges according to the BWM. Interpretive structural modelling findings suggest that the lack of government initiatives has been driving other challenges with the highest driving power.

This work has been conducted in the Indian context, so careful generalization of the results is needed.

This work will give health-care stakeholders a better perspective regarding blockchain’s adoption. It will help health-care stakeholders, service providers, researchers and policymakers get a glimpse of the strategies for eradicating mentioned challenges. The analysis will help reduce the challenges’ impact on blockchain’s adoption in the Indian health-care sector.

The adoption of BT is a novel concept, especially in developing countries such as India. This is one of the few works addressing the challenges to BT adoption in the Indian health-care sector.

The purpose of this paper is to contribute to a growing body of research on the applicability of resource-based theory (RBT) to the information systems (IS) area. In particular, the paper provides an understanding of the IS value creation process, and strategies for managing it by demonstrating the application of cross-impact analysis.

RBT and systems theory are adopted as a theoretical framework in this study. Cross-impact analysis is used as a method for investigating interactions among elements of an IS value creation system. These elements were identified through 22 semi-structured interviews with organisational stakeholders, and assessed in terms of direction and strength of their interactions, and depicted in a coordinate system.

The result of the analysis is a meaningful classification of elements in an IS value creation system as: levers, indicators, identities, buffers or trends, based on their position in the system. The results provide additional clarity and insights into the relationships between IS and organisational resources and their effect on IS value.

The research findings have important implications for researchers and managers in terms of understanding the impact of interactions among IS and organisational resources on formulating successful strategies for managing the IS value creation system.

This study explores interactions among IS/information technology resources and organisational resources by using cross-impact analysis. It shows that interactions among the identified resources do have a major influence on the overall IS value creation system.

The purpose of this paper is to investigate the interactions among the key factors of supply chain (SC) in the Indian automotive industry. These key factors are helpful to measure supply chain performance (SCP) and to improve the firm’s effectiveness.

In this paper, an interpretive structural modeling with a fuzzy cross-impact matrix multiplication applied to classification-based approach is used to examine the interactions among the key factors of SCP measurement.

The authors have identified the most dominant key factors used for measuring the performance in automotive SC. The results exhibit that the order lead-time and order entry method are the most significant key factors. These key factors have high driving power to measure SCP whereas the post-transaction measure of customer service and customer query time are highly dependent on other factors. Such relationships among the key factors can help a firm’s top management to make essential judgments in order to solve the overall SC problems and provide a better approach to proactively deal with problems.

In this paper, the authors have explored the interactions among the key factors of the SCP in the Indian automotive industry.

Agile new product development (ANPD) attracts researchers and practitioners by its ability to rapidly reconfigure products and related processes to meet the needs of emerging markets. To increase ANPD adoption, this study aims to identify ANPD enablers (ANPDEs) and create a structural framework that practitioners can use as a quick reference.

Initially, a comprehensive literature review is conducted to identify ANPDEs, and a structural framework is developed in consultation with an expert panel using a hybrid robust best–worst method interpretive structural modeling (ISM). During the ISM process, the interactions between the ANPDEs are investigated. The ISM result is used as input for fuzzy Matrice d’Impacts croises-multiplication appliqúean classment means cross-impact matrix multiplication applied to classification (MICMAC) analysis to investigate enablers that are both strong drivers and highly dependent.

The study’s findings show that four ANPDEs are in the low-intensity cluster and thus are excluded during the structural frame development. ISM output shows that “Strong commitment to NPD/top management support,” “Availability of resources,” “Supplier commitment/capability” and “Systematic project planning” are the important ANPDEs. Based on their driving and dependence power, the clusters formed during the fuzzy MICMAC approach show that 16 ANPDEs appear in the dependent zone, one ANPDE in the linkage zone and 14 ANPDEs in the driving zone.

This research has intense functional consequences for researchers and practitioners within the industry. Industry professionals require a conservative focus on the established ANPDEs during ANPD adoption. Management has to carefully prepare a course of action to avoid any flop during ANPD adoption.

The framework established is a one-of-a-kind study that provides an integrated impression of important ANPDEs. The authors hope that the suggested structural framework will serve as a blueprint for scholars working in the ANPD domain and will aid in its adoption.

Seaports are vital in facilitating sustainable development, and environmental, social and governance (ESG) factors significantly impact an organization’s performance. Therefore, this study aims to identify and evaluate barriers and strategies of green investments to promote ESG practices within the seaport sector.

To fulfill this aim, a systematic literature review, interpretive structural modeling and the matrix of cross-impact multiplications were applied to classification analysis.

12 barriers were prioritized and categorized by experts in a focus group to optimize efforts and define the materiality of these barriers in implementing ESG strategies within seaport companies.

The implications of this study provide an alternative approach for ESG management in the context of seaports that can be applied in different regions by experts' opinion assessment.

No prior studies assessed the barriers and strategies for green investments in ESG from the port sector perspective.

The purpose of this paper is to identify barriers to implement green supply chain and to understand their mutual relationship. Green supply chain management (GSCM) barriers are identified using available GSCM literature and on consultations with experts from industry and academician. Interpretive structural model (ISM) was developed to identify the contextual relationship among these barriers.

A group of experts from industries and academics was consulted and ISM is used to develop the contextual relationship among various GSCMBs for each dimension of GSCM implementation. The results of ISM are used as an input to fuzzy matrix of cross-impact multiplications applied to classification (MICMAC) analysis, to identify the driving and dependence power of GSCMBs.

This paper has identified 14 key GSCMBs and developed an integrated model using ISM and the fuzzy MICMAC approach, which helps to identify and classify the important GSCMBs and reveal the direct and indirect effects of each GSCMB on the GSCM implementation. ISM model provides only binary relationship among GSCMBs, while fuzzy MICMAC analysis provides precise analysis related to driving and dependence power of GSCMB, to overcome this limitation, integrated approach is developed.

ISM model development and fuzzy MICMAC analysis were obtained through the judgment of academicians and industry experts. It is the only subjective judgment and any biasing by the person who is judging the GSCMBs might influence the final result.

This is first kind of study to identify GSCMBs and further, to deploy ISM and fuzzy MICMAC to identify and classify the key GSCMEs that influence GSCM implementation in the organization. The results will be useful for business managers to understand the GSCMBs and overcome these GSCMBs during GSCM implementation in an organization.

The purpose of this paper is to identify and build a hierarchy of the factors influencing competitiveness of electronics manufacturing industry (EMI) at the industry level and apply the interpretive structural modeling, fuzzy Matriced’ Impacts Croisés Multiplication Appliquée á UN Classement (i.e. the cross-impact matrix multiplication applied to classification; MICMAC) and analytic hierarchy process (AHP) approaches. These factors have been explained with respect to managerial and government policymakers’ standpoint in Indian context.

This study presents a hierarchy and weight-based model that demonstrates mutual relationships among the significant factors of competitiveness of the Indian EMI.

This study covers a wide variety of factors that form the bedrock of the competitiveness of the EMI. Interpretive structural modeling and fuzzy MICMAC are used to cluster the influential factors of competitiveness considering the driving and dependence power. AHP is used to rank the factors on the basis of weights. Results show that the “government role” and “foreign exchange market” have a significantly high driving power. On the other hand, the “capital resource availability” and “productivity measures” come at the top of the interpretive structural modeling hierarchy, implying high dependence power.

The study has strong practical implications for both the manufacturers and the policymakers. The manufacturers need to focus on the factors of competitiveness to improve performance, and at the same time, the government should come forward to build a suitable environment for business in light of the huge demand and frame suitable policies.

The lackluster performance of the industry is because of the existing electronics policies and environmental conditions. The proposed interpretive structural modeling and fuzzy MICMAC and AHP frameworks suggest a better understanding of the key factors and their mutual relationship to analyze competitiveness of the electronics manufacturing industry in view of the Indian Government’s “Make in India” initiatives.

This paper contributes to the industry level competitiveness and dynamics of multi-factors approach and utilize the ISM–fuzzy MICMAC and AHP management decision tool in the identification and ranking of factors that influence the competitiveness of the EMI in the country.