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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.

This paper appraises the effects of monetary policy on trade in value-added (TiVA) using a panel of 38 developing countries spanning the period 1990 to 2019. Specifically, the authors subsequently summon the theory of trade in intermediate products within the New Keynesian framework for open economies that comprises price rigidity to verify this relationship and thereon control for robustness by correcting for endogeneity and unbalanced panel effect.

The authors mobilize the within estimator corrected for cross sectional dependence as well as the two-stage-least squares fixed effect estimator which corrects for endogeneity. For robustness, the authors also use the Hausman–Taylor estimator to control for endogeneity and random effects in annualized data and the least squares dummy variable corrected estimator.

Results suggest that the monetary policy instruments such as inflationary gaps and anticipatory inflationary outcomes significantly affect TiVA in developing countries only in the short term with no long-term effect. In addition to contributing to the scanty empirical literature, the authors provide relevant insights on monetary policy tools that can be mobilized in fashioning a global value chain penetration and upgrading strategies.

The authors convoke the theory of trade in intermediate products casted into the New Keynesian framework comprising price rigidity to verify the relationship between TiVA and monetary policy (b) verify for robustness by correcting for endogeneity and unbalanced panel effect.

The stock market has shown fluctuating degrees of volatility because of the recent COVID-19 pandemic in India. The present research aims to investigate the effect of the COVID-19 on the stock market volatility, and whether the economic package can control the market volatility or not, measured by a set of certain sector-level economic features and factors such as resilience variables.

We examine the correlation matrix, basic volatility model and robustness tests to determine the sector-level economic features and macroeconomic factors helpful in diminishing the volatility rising because of the COVID-19.

The outcomes of this study are significant as policymakers and financial analysts can apply these economic factors to set policy replies to handle the unexpected fluctuation in the stock market in sequence to circumvent any thinkable future financial crisis.

The originality of the paper is to measure the variables affecting the stock market volatility due to COVID-19, and understand the impact of capital market macroeconomic variables and dummy variables to theoretically explain the COVID-19 impact on stock market volatility.

Studies of unicorns and gazelles can offer detailed information about the process of enterprise development but are unrepresentative as examples of entrepreneurial success. In presenting a novel method for outlier analysis, this article combines insights from case studies of unusual organizations with explanatory frameworks that management scholars have applied to broader samples of firms, irrespective of their survival.

The authors illustrate the approach to outlier analysis using a prominent case from economic history: the House of Rothschild, founded during the 18th century, which became the most famous investment bank in Europe. Following the iterative refinement of mechanisms using comparison data on Jewish enclave firms, this analysis sheds light on the sources of dissimilarity in outcomes between Rothschild and the comparison group.

The study results suggest that the House of Rothschild's longevity can be explained via the mechanisms of risk sequencing, intergenerational transfers and spatial brokerage. The authors show that these mechanisms are not idiosyncratic to one enterprise but instead generalize to other family firms.

Outlier analysis encourages a rapprochement between case study and large-N research. The high failure rate of new organizations means that those yielding a large amount of information to researchers tend to be exceptional. By obtaining data on a comparison group of startups founded by similar entrepreneurs, analysts can probe the mechanisms of success identified for unicorns or gazelles.

This study aims to elucidate institutional analysis as an effective approach to investigating and designing the multilevel policymaking system of online social networks (OSN) for achieving a participatory model.

The institutional mapping approach has been used to analyze Iran’s OSN multilevel policymaking system. A combination of two matrices, including institutions-institutions and institutions-functions, was used to perform the institutional mapping. Two main steps were taken to draw the mentioned matrices. First, a review of related studies in Iran’s OSN policymaking system was conducted and the policy functions mentioned in these studies were identified and categorized using the meta-synthesis. Second, based on analyzing two policy documents of Iran’s OSN, institutions and their interactions were identified and policy functions were allocated to institutions.

Based on the results, the most important policy functions in the current OSN policymaking system in Iran are support, regulatory, monitoring and evaluation, business environment development, culture building and promotion, organizing licenses and permissions, policymaking and legislation. Also, the results show that there are shortcomings in this system, some of the most important of which are lack of transparency in regulatory, little work in culture building and promotion, neglect of the training of specialized human resources and research and development, slow development of the business environment and neglecting the role of nongovernmental organizations in policymaking.

By examining and analyzing how different institutions operate within a multilevel policymaking system, the policymaking process and its overall effectiveness can be enhanced. This analysis helps identify any inconsistencies, overlaps or conflicts in the roles and policies of these institutions, leading to a better understanding of how a multilevel policymaking system is organized.

Airline scheduling is an extremely complex process. Moreover, disruption in a single flight may damage the entire schedule tremendously. Using an efficient recovery scheduling strategy is vital for a commercial airline. The purpose of this paper is to present an integrated aircraft and crew recovery plans to reduce delay and prevent delay propagation on airline schedule with the minimum cost.

A mixed-integer linear programming model is proposed to formulate an integrated aircraft and crew recovery problem. The main contribution of the model is that recovery model is formulated based on individual flight legs instead of strings. This leads to a more accurate schedule and better solution. Also, some important issues such as crew swapping, reassignment of aircraft to other flights as well as ground and sit time requirements are considered in the model. Benders’ decomposition approach is used to solve the proposed model.

The model performance is also tested by a case including 227 flights, 64 crew, 56 aircraft and 40 different airports from American Airlines data for a 24-h horizon. The solution achieved the minimum cost value in 35 min. The results show that the model has a great performance to recover the entire schedule when disruption happens for random flights and propagation delay is successfully limited.

The authors confirm that this is an original paper and has not been published or under consideration in any other journal.