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Recently, blockchain technology (BT) has resolved healthcare data management challenges. It helps healthcare providers automate medical records and mining to aid in data sharing and making more accurate diagnoses. This paper attempts to identify the critical success factors (CSFs) for successfully implementing BT in healthcare.

The paper is methodologically structured in four phases. The first phase leads to identifying success factors by reviewing the extant literature. In the second phase, expert opinions were solicited to authenticate the critical success factors required to implement BT in the healthcare sector. Decision Making Trial and Evaluation Laboratory (DEMATEL) method was employed to find the cause-and-effect relationship among the third phase’s critical success factors. In phase 4, the authors resort to validating the final results and findings.

Based on the analysis, 21 CSFs were identified and grouped under six dimensions. After applying the DEMATEL technique, nine factors belong to the causal group, and the remaining 12 factors fall under the effect group. The top three influencing factors of blockchain technology implementation in the healthcare ecosystem are data transparency, track and traceability and government support, whereas; implementation cost was the least influential.

This study provides a roadmap and may facilitate healthcare professionals to overcome contemporary challenges with the help of BT.

Significant developments in the service sector have been brought about by Industry 4.0. Automated digital technologies make it possible to upgrade existing services and develop modern industrial services. This study prioritizes critical factors for adopting Industry 4.0 in the Indian service industries.

The author identified four criteria and fifteen significant factors from the relevant literature that have been corroborated by industry experts. Models are then developed by the analytical hierarchy process (AHP) and analytical network process (ANP) approach to ascertain the significant factors for adopting Industry 4.0 in service industries. Further, sensitivity analysis has been conducted to determine the sensitivities of the rank of criteria and sub-factors to corroborate the results.

The outcome reveals the top significant criteria as organizational criteria (0.5019) and innovation criteria (0.3081). This study prioritizes six significant factors information technology (IT) specialization, digital decentralization of all departments, organizational size, smart services through customer data, top management support and Industry 4.0 infrastructure in the transition toward Industry 4.0 in the service industries.

The potential factors identified in this study will assist managers in determining strategies to effectively manage the Industry 4.0 transition by concentrating on top priorities when leveraging Industry 4.0. The significance of organizational and innovation criteria given more weight will lay the groundwork for future Industry 4.0 implementation guidelines in service industries.

Our research is novel since, to our knowledge, no previous study has investigated the potential critical factors from organizational, environmental, innovation and cost dimensions. Thus, the potential critical factors identified are the contributions of this study.

This paper aims to study the impact of economic freedom and some key macroeconomic variables on the foreign direct investment (FDI) inflow in Brazil.

An econometric model is developed that includes FDI inflow as the dependent variable and macroeconomic variables such as the output, current account balance, the real exchange rate, openness and economic freedom as explanatory variables. Annual time series data from 1995 to 2022 is used. Before carrying out the estimation, the time series properties of the data are diagnosed using unit root tests and cointegration tests. Since the data series were found to be stationary in the first difference form and the variables in the model were cointegrated, an error correction model is developed and estimated.

The findings demonstrate that the size of the market (gross domestic product), current account balance and the economic freedom index significantly influence FDI inflow to Brazil. Although the signs of openness and the real exchange rate align with theoretical expectations, they do not attain statistical significance.

To the best of the authors’ knowledge, this is the first formal study on the impact of economic freedom on the FDI inflow in Brazil. The finding of this study adds value to the understanding of FDI dynamics in Brazil, highlighting the critical role of economic freedom and market size in attracting foreign investment.

This paper examines the reaction of the Egyptian stock market to two substantial devaluations of the Egyptian pound (EGP) in 2022 and tests the informational efficiency of the Egyptian market.

The paper uses the event study framework to analyze the significance and direction of abnormal returns of the leading index of the Egyptian stock market (EGX30) on and around the devaluation days. It employs both the constant mean model and the market model to estimate the normal returns of the EGX30. Additionally, the paper uses data on two equity indices, one global and one for emerging markets, as benchmarks for normal returns.

The paper finds that the Egyptian stock market experienced significant positive abnormal returns on the devaluation days of the EGP in March and October of 2022, indicating a positive market reaction to the devaluation. Furthermore, evidence suggests that the Egyptian market may not be informationally efficient as significant positive abnormal returns were observed two weeks before and two weeks after the devaluation day, suggesting news leaks and delayed reactions, respectively.

This study is the first to examine the impact of the recent two devaluations of the EGP in 2022 on the Egyptian stock market. It complements existing literature by analyzing the immediate market reaction to two consecutive devaluations in an African country. Furthermore, the paper evaluates the efficiency of the Egyptian market in processing information related to exchange rates.

The purpose of this study is to unfold the existing information channel in the higher moments of currency futures for different time horizons.

The authors use a quasi-Bayesian local likelihood approach within a time-varying parameter vector autoregression (TVP-VAR) framework and a dynamic connectedness measure to study the volatility, skewness and kurtosis of most traded currency futures.

The authors’ results suggest a time-varying presence of dynamic connectedness within higher moments of currency futures. Most spillovers pertain to shorter time horizons. The authors find that in net terms, CHF, EUR and JPY are the most important contributors to the system, while the authors emphasize that the role of being a transmitter or a receiver varies for pairwise interactions and time windows.

To the best of the authors’ knowledge, this is the first study that looks upon the connectivity vis-á-vis uncertainty, asymmetry and fat tails in currency futures within a dynamic Bayesian paradigm. The authors extend the current literature by proposing new insights into asset distributions.

This article examines if the national productions of West African Economic and Monetary Union (WAEMU) countries can be substituted for the imports by testing MLRC in these countries.

The Mundell–Fleming model (MMF) is the analytical framework adopted in this paper with import demand and export supply functions estimation borrowed to Thirlwall (1979). This study covers four countries in West Africa from 1990 to 2021. The estimation procedure used is an Autoregressive Distributed Lag (ARDL) approach to cointegration.

The findings reveal that there is a strong marginal propensity to import in the WAEMU countries. The hypothesis of a non-significant price effect on imports in the short-term is confirmed for several countries while only Togo satisfies the MLRC in the short and long run.

This study presents several originalities: (1) it evaluates MLRC with a clear analytical framework; (2) unlike other studies, this article quantifies the MLRC from a theoretical, econometric and empirical point of view; (3) this article presents the results country by country in order to reveal heterogeneity between countries; (4) this study adds to the Marshall–Lerner condition for the derivation of Robinson by considering a situation where initially the trade balance is not in equilibrium.

To estimate the volatility of exchange and stock markets and examine its spillover within and across the member countries of BRICS during COVID-19 and the conflict between Russia and Ukraine.

The study utilizes the “dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH)” approach of Gabauer (2020). The volatility of the markets is calculated following the approach of Parkinson (1980). The sample dataset comprises the daily volatility of the stock and exchange markets for 35 months, from November 2019 to September 2022.

The study confirms the existence of contagion effects among member countries. Volatility spillover between exchange and stock markets is low within the country but substantial across borders. Russian contribution increased significantly during the conflict with Ukraine, and other countries also witnessed a surge in the spillover index during the pandemic and war.

It adds to the body of literature by emphasizing the necessity of comprehending the economies' behavior and interdependence. Offers insightful information to decision-makers who must be more watchful regarding the financial crisis and its regional spillover.

The study is the first to explore the contagion of volatility among the BRICS countries during the two biggest crisis periods of the decade.

As one of the world's most valuable traded commodities, the market for coffee beans has grown enormously in recent years. The paper aims on analyzing the nonlinear exchange rate pass-through in Turkish coffee bean imports from two important sources in South America: Brazil and Colombia.

Data collected in this paper through reliable channels include nominal import value, exchange rate, production of total industry, etc. Independent and dependent variables are obtained through conversion. Since the nonlinearly adjusted exchange rate differs significantly from the linearly adjusted one for the export trade of Brazilian coffee beans, this paper develops the autoregressive distributed lag (ARDL) and nonlinear ARDL frameworks and demonstrates their application through asymmetric cointegration and error correction models.

The results of this paper show that imports of Brazilian coffee bean exhibit a more dramatic asymmetry compared to Colombia's coffee bean imports. The results of this study contribute to the import trade of non-oil commodities in developing countries, particularly Brazil, and enrich the existing literature on nonlinear exchange rate adjustments.

The export of Colombian coffee beans is not as old as Brazil, and it was not until much later that Colombia began to export coffee beans to the rest of the world.

The present study is an addition to the literature of agricultural trade. The authors analyze the nonlinear exchange rate pass-through in Turkish coffee bean imports from two important sources in South America: Brazil and Colombia. Different from the current mainstream research on oil commodity trade, this paper focuses on international trade from the perspective of coffee beans, which can enlighten the practice in this field.

This study aims to examine the impact of fiscal deficit, exchange rate and trade openness on current account deficit (CAD). The study tried to empirically investigate the ‘twin deficits hypothesis’ and ‘compensation hypothesis’ in the Indian context.

Autoregressive distributed lagARDL) bound test approach was used by taking annual time series data from 1978 to 2021. The estimates confirm a significant long-run and short-run relationship between dependent variables, i.e. CAD and independent variables such as the fiscal deficit, exchange rate and trade openness.

The results show that positive shocks of all explanatory variables significantly affect the CAD. CAD and fiscal deficit are significantly associated, as the coefficient of fiscal deficit is positive and significant. The study also found that exchange rate and trade openness significantly affect the CAD. The coefficients of exchange rate and trade openness are positive and significant. The findings show that an increase in CADs results from liberal trade policies that help domestic industries grow their trade and expansionary fiscal policy, leading to a higher fiscal deficit. The negative and significant error correction term suggests that short-run disequilibrium converges to long-run equilibrium at a speed of 19.2%. The findings validate the ‘twin deficits hypothesis’ and ‘compensation hypothesis’ in the Indian context.

It can be inferred from the study that liberal policy to promote economic growth and trade openness should be designed and promoted judiciously. An excessive liberalised approach may impact other macroeconomic variables such as current account balances. Integrating the domestic market with global markets poses a big challenge for countries like India that aspire to penetrate global markets. Furthermore, the Indian policy makers should rigorously work and promote the policies such as Fiscal Responsibility and Budget Management (FRBM) as reduction in fiscal deficits, trade imbalances will also be reduced.

This study contributes to the existing literature on ‘twin deficit’ and trade openness by giving new evidence on the trilemma between designing sustainable fiscal policy by spending wisely without imperilling the country's global presence and CAD.

The authors explore the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic.

The authors employ a non-linear autoregressive distributed lag (NARDL) methodology using daily data of the Indonesian economy over the period 2012–2021.

Whilst, over the full sample period, the authors find no cointegration between the exchange rate, the 10-year bond yield and stock market, for the COVID-19 period, evidence of cointegration is present. Furthermore, the results suggest that asymmetric effects are evident both in the short as well as the long run.

To the best of the authors’ knowledge, this is the first time that the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic has been explored in the case of the Indonesian economy.