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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 paper is to develop an integrated model, in order to identify and classify, key criteria, and to study their role in the selection process of third party logistics (3PLs) services providers for shippers' logistics need.

In this paper, an integrated model using interpretive structural modeling (ISM) and FMICMAC analysis has been developed to identify and classify the key selection criteria of 3PL services providers, typically identified by many researchers and practiced by the shippers for effective supply chain management. The key criteria are also modeled to find their role and mutual influence in the selection of 3PL services providers.

The key finding of this modeling helps to identify and classify the criteria, which may be further used, to identify the potential 3PL services provider. Integrated model reveals, criteria such as information technology capability; size and quality of fixed assets and quality of management as independent criteria, whereas criteria such as compatibility, long‐term relation and reputation as dependent criteria. Criterion namely flexibility in operation and delivery is found to be an autonomous criterion. The important criteria like quality of service, information sharing and trust, geographical spread and range of services, delivery performance, operational performance, financial stability, optimum cost, and surge capacity are found as the linkage criteria. Integrated model also establishes the direct and indirect relationship among various criteria, which plays a significant role in the selection process.

The research provide an integrated model using ISM and FMICMAC to identify and classify various key criteria required for the selection of 3PL services providers. The various key criteria have been grouped under four broad classification, namely, dependent criteria, independent criteria, autonomous criteria and linkage criteria based on their driving and dependence power, deduced from fuzzy reachability value. The model helps in the identification, classification and selection of key criteria along with their behavior, thus this research will help logistics managers to select right criteria for the selection of potential 3PL services providers for their logistics need.

The objective of the paper is to identify and model the relevant enablers related to the issue of adoption and implementation of project-based learning (PjBL) in higher educational institutions.

The present study has developed an integrated model using interpretive structural modeling (ISM) and the Fuzzy Matrice d' Impacts Croises Multiplication Appliqué an Classement (FMICMAC) approach, which helps to identify and classify the important enablers and reveal the direct and indirect effects of each enabler on the PjBL implementation in higher educational institutions.

The paper has identified the key enablers and presented an integrated model using ISM and FMICMAC. The result shows that there exists a group of enablers having a high driving power and low dependence requiring maximum attention and strategic importance, while another group consists of those enablers that have high dependence and are the resultant actions.

The study proposes a scientific way to model the relevant enablers to implementation of PjBL. This would help higher educational institutions to prioritize the enablers as these are hierarchically structured. The model is based on the experts' opinions, which may be biased, influencing the final output of the structural model.

Enablers are building blocks for the adoption of PjBL. The study presents an integrated model using ISM and FMICMAC to identify and categorize various key enablers of PjBL adoption in higher education institutions. The results will help higher educational institutions to focus on the right enablers for the successful implementation of PjBL in their programs.

The purpose of this paper is to identify factors related to rural healthcare services and establish a hierarchical model for the effective rural healthcare management in India.

A questionnaire survey identified and correlated numerous factors related to the Uttarakhand rural healthcare systems. Experts opinion were translated into a reachability matrix and an interpretive structural model. A fuzzy matriced impacts croises-multiplication applique and classment (FMICMAC) analysis arranged the factors as hierarchical stages using their driving power.

The interpretive structural and FMICMAC hierarchical models suggest four key driving factors: diseases, climatic conditions, population growth and political pressure.

Despite numerous issues, rural healthcare services can be improved by considering key driving factors that could be used as a prediction tool for policy makers.

Results demonstrate that population control, coordinating services with local bodies and rural health center annual maintenance can be game changers toward better healthcare services.

The aim of this paper is, first, the desire to present the issue of retail sector competitiveness with the simultaneous determination of factors having an impact on competitiveness and their development. The main aim is to identify the factors and relationships among those factors to strengthen the competitive positioning of apparel retail stores.

The literature review and experts’ opinion helped to identify the key factors. The relationships among the factors were obtained by using interpretive structural modelling (ISM). Experts’ opinions were collected again for the fuzzy direct relationship matrix. Factors were further classified by driver and dependence power using the fuzzy matrix of cross-impact multiplications applied to classification (FMICMAC) analysis.

Total nine strengthening factors (SFs) identified here, and developed an integrated model using ISM and classified it into four clusters with the help of driver and dependence power. The model hierarchy shows the interrelationships among these SFs. The retail environment, Information and Communication Technology, technology adoption and human resource management were found to be the most significant factors needing some spotlight by the top-level authority.

The study will help managers to understand the variables and their relationships and to select the right factors to achieve a potential competitive position. Relationships among the factors were obtained through the opinions of experts and academicians. Expert opinion is a subjective judgement, and biasing in judgement might affect the result.

The research presents the first kind of an integrated model using ISM and FMICMAC to identify nine factors and classify them by their driving and dependence power. The developed model helps in the identification, classification and selection of factors as per requirement. This study will assist managers to understand the variables and their relationships and to select right factors to achieve a potential competitive position.

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 global research evidences indicate that the technology adoption in case of agribusiness has a potential to enhance the performance and bring operational efficiency. India is the world’s largest producer as well as consumer of milk but struggles with yield per cattle, overall productivity, low rate of technology acceptance and adoption, health detection of milching units, animal data recording and presence of dairy products in the global market. The purpose of this study is to focus on identifying the challenges of technology adoption in dairy farms and constructing a hierarchical model using soft systems methodology.

This study uses nominal group technique-based discussion with domain experts and personal interviews with dairy farm owners/managers for the identification of challenges, fuzzy interpretative structural modeling as well as FMICMAC to develop a hierarchical model of challenging elements and to divide the identified elements into four categories based on the dominance of driving-dependence power.

This research has developed a list of 12 challenges affecting the technology adoption in a dairy farm business unit, identified through the personal interviews with 60 dairy farms across three highest milk-producing states of India in terms of annual milk output – Haryana, Punjab and Uttar Pradesh. Lack of government support followed by lack of educational opportunities in dairy-based education were found as the most crucial and high driving challenges, whereas high cost, huge investment and low acceptance of decision-maker were found as the most dependent challenges of technology adoption.

This research is one step ahead of interpretive structural modeling that considers the fuzzy-based dominance in the model to showcase the degree of relationship along with its existence, but it lacks to statistically validate the findings using techniques like SEM.

This paper has developed a list of challenges in adoption of technology along with their inter-relationships to highlight the required focus challenge that drives or is dependent on the other challenges. The goal is to bring performance improvement and assist Indian dairy farm business stakeholders or decision-makers in formulating strategic and action plans and help policy planners to make favorable policies based on the understanding of contextual relationship between challenges.

In Indian context, dairy is an important part of agriculture sector, and milk is an essential item that facilitates income generation to small and rural households and a source item for several other businesses and activities. The results of this research suggested the policy planners and government to ensure subsidized and insured technologies, training support and facilities, educational opportunities and efforts for promotion of technology adoption among dairy farmers. The suggestions are purely on the basis of the relevance of challenges in the hierarchy and can play a significant role in improving the level of technology adoption and can ultimately uplift the social and economic well-being from micro-level of farmers to macro-stage concerning economic development of India.

To the best of the authors’ knowledge, this study is purely original and outcome of the research conducted by authors.

The purpose of this paper is to identify the factors that influence hospitals’ selection by health-care insurers in India and to establish a hierarchical model representing the relationship among different factors and their influence on the entire scenario.

A survey with a set of questionnaires was conducted with different health-care insurer executives of reputed health insurance companies. The data has been gathered by using a five-point Likert scale. Their opinions were converted into a reachability matrix and an interpretive structural modeling was constructed. The final results obtained were verified by using fuzzy Matriced Impacts Croises-Multiplication Applique and Classement analysis.

The results suggested three key driving factors, National Accreditation Board for Hospitals & Healthcare Providers accreditation of the hospital, purchasing power of people in the region and national and international recognition of the hospital among the eleven factors selected for the study.

The research mainly focuses on the health insurance benefits provided by privately owned insurance companies and do not comment on any government’s mass health insurance scheme.

With a small proportion of people under the umbrella of health insurance in India, these factors will assist and expedite insurer’s effort to penetrate deep into rural and urban areas enhancing availability and escalating affordability.

This paper presents key factors responsible for better coordination between health-care systems and insurance companies.

The study aims to explore and classify the key enablers of the Association to Advance Collegiate Schools of Business (AACSB) implementation in business schools.

By applying the Interpretive Structural Model (ISM) approach, it builds a hierarchical model of the identified enablers of AACSB implementation. Additionally, the Fuzzy Matriced Impacts Croises-Multiplication Applique and Classement (FMICMAC) technique is used to classify and determine the influence of these enablers on the implementation of AACSB accreditation.

The paper presents an ISM model of the identified enablers and draws managerial insight from it. Categorization of the key enablers into four groups helps in understanding the relative influence of each group of enablers on the AACSB implementation in business schools.

The proposed model of the key enablers will help business schools that are pursuing AACSB accreditation prioritize the enablers. As the study has considered experts' opinions to establish the model, some amount of bias cannot be discounted.

The development of the ISM model of the key enablers of AACSB implementation in business schools is a unique attempt. The findings will help business schools focus on the key enablers that influence implementation of AACSB standards.

The purpose of this paper is to ascertain and understand the relationship dynamics among the quality-enabled factors (QEFs) affecting the initiation stage of production system life cycle (PSLC). This study presents an approach for refining the decision making in the initiation stage of the production system.

In this paper, ten QEFs have been identified for the initiation stage of PSLC. An interpretive structural modelling (ISM) approach has been utilized to cultivate an organizational association among these identified QEFs. The results of ISM approach are used as an input to fuzzy Matriced’ Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis, to identify the driving and dependence power of QEFs.

The key consequences of this paper are to prioritize the strategic QEFs in reducing the risks linked with initiation stage of production system. The integrated model obtained by ISM-fuzzy MICMAC illustrates that there exists two clusters of QEFs, one is having high driving power and low dependency power which requires extreme consideration and of strategic importance (such as honesty and sincerity in collecting and analyzing field data) and other is having high dependence power and low driving power and are resultant effects (such as strategic decision-making ability).

The integrated ISM-fuzzy MICMAC model developed is not statistically corroborated; consequently structural equation modelling (SEM) approach which is also known as linear structural relationship approach could be utilized to examine the validity of developed hypothetical model.

This is first study to identify ten QEFs in initiation stage of production system and further, to deploy integrated ISM-fuzzy MICMAC approach to recognize and categorize the QEFs influencing the initiation stage of production system.