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This paper aims to examine the relationship between Indian rupee-US dollar exchange rate and the macroeconomic fundamentals for the post-economic reform period.

The authors have used an empirical model which includes a range of important macroeconomic variables based on the basic monetary theories of exchange rate determination. At the first stage of the analysis, they have tested structural break in the data. Subsequently, they have employed the fully modified ordinary least square, Wald’s coefficient restriction and impulse response functions (IRF) to estimate the monetary model in the long- and short-run horizons.

Results of analyses indicate that the macroeconomic fundamentals determine exchange rate in a significant way, but their effect varies sizably across the periods. The IRF illustrate the importance of interest rate in controlling exchange rate volatility.

The analysis of the behavior of inter-relationship among macroeconomic variables will help policymakers in a deep-rooted understanding of this complex and time-varying relationship.

Most of the existing studies have tested the impact of a single or a few macroeconomic fundamentals on exchange rate. But in the present study, we have tested the impact of a range of important variables, i.e. money supply, real income or output, price level and trade balance. Further, considering the importance of structural breaks in data, they authors have employed standard tests of structural break and incorporated the issue in the cointegration analysis.

The recent pandemic of COVID-19 has posed challenges for delivering essential and desirable health-care services for the masses. Digital health-care services initiated by several hospitals and health practitioners promise efficient and safe health care in the new normal post-COVID era but need a supportive enabling ecosystem. Therefore, this study aims toward identifying and modeling the key enabling factors for digital health-care services.

A total of nine factors were identified from the literature review and verified by the domain experts which can enable the wider acceptance of digital health-care services. The identified factors were then modeled with the help of the total interpretive structural modeling (TISM) approach and fuzzy Matrices d’Impacts Croises Multiplication Appliquée à un Classement (MICMAC) and a meaningful contextual relationship were developed for the factors.

This study reflects that the trust of patients is required for the acceptance of digital health care. Quality of patient care and affordability cum accessibility of online services will define mass engagement. Hospital staff resilience, hospital care service capacity, strategic partnerships and collaborations supported by technology and regulatory structure are the major factors defining the enabling ecosystem.

This study has its uniqueness in the way the TISM approach and fuzzy MICMAC are used for modeling the enabling factors toward growth and acceptance of digital health-care services in the days to come in developing nations. The focus of this study can be considered as relevant for the study interested in investigating the role of cognitive dimensions in influencing actors’ behaviors and decisions.