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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.

Success of software projects depends on identification of project risks and managing the risks in a proactive manner. Risk management requires thorough insights into interrelationship of various risk factors for proposing strategies to minimize failure rate. The purpose of this paper is to develop a comprehensive structural model to interrelate important risk factors affecting the success of software projects.

Specifically, this study reveals how interpretive structural modelling helps the risk managers in identifying and understanding the interrelationship among various risk factors. A total of 23 risk factors (or risk sources) have been identified through an extensive literature review.

Necessary modelling information has been gathered from expert through a structured questionnaire survey. Matrice d’Impacts croises-multipication appliqué an classment analysis has been employed to classify the risk factors into four clusters such as autonomous, dependent, linkage and independent based on their driving and dependence power. Risk factors with strong dependence and weak driving power need urgent attention from managerial perspective.

The proposed model is useful for software managers/practitioners to address risk factors associated with complicated projects.

The purpose of this paper is to identify the dimensions of service quality as well as of service loyalty in the context of medical tourism. It seeks to demonstrate the conceptualization of medical tourism service loyalty (MTSL) construct. This research also attempts to examine the effect of service quality dimensions on service loyalty dimensions of medical tourism.

The dimensions of service quality as well as of service loyalty are identified using an exploratory factor analysis. Next, the reliability and validity of the quality factors and loyalty factors are established through confirmatory factor analysis using AMOS 18.0 version. The related hypotheses are tested using structural equation modeling (SEM).

The paper identifies eight-factor construct for medical tourism service quality and three-factor construct for MTSL. It is found that the treatment satisfaction dimension of service quality has positive and significant impact on MTSL. It is also observed that, overall, medical tourism service quality has positive impact on MTSL.

These dimensions of service quality should be viewed as the levers of improving perceived service quality with respect to medical tourism. Examining the service quality dimensions’ impact on customer loyalty for medical tourism sector can offer the industry valuable insights regarding which aspects of the service to focus on in order to improve medical tourist’s satisfaction and loyalty toward the firms.

This paper introduces the concept of service quality and service loyalty in medical tourism sector. In conceptualizing MTSL, the authors propose an integration of behavioral measures, attitudinal measures and cognitive measures. The interrelationship between the service quality construct and medical loyalty construct was established using SEM. This is useful for the healthcare manager to measure the medical tourist’s perceptions of service quality on these dimensions as related to medical tourism performance.