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This study aimed to analyze stressors to which medical staff is vulnerable due to the coronavirus disease 2019 (COVID-19) pandemic. It also imposes a hierarchy on complex relations among stressors for excavating underlying structure and builds a model of interrelationships contrasting reality. The design of this study comprises a literature survey, data collection from primary sources, and analysis. Stressors have been explored from within current published/unpublished literature and validated by experts through approval vote. Data were collected from the focus group (panel of experts), and interpretive structural modeling (ISM) was used as the research methodology. Findings of ISM are avowed through “cross-impact matrix multiplication applied to classification” (MICMAC) analysis. As a result of the literature survey, a list of stressors was generated, and a total of 19 stressors qualified as representative of the phenomenon. The results of ISM show that two stressors (i.e., “unavailability of proper personal protective equipment (PPE)” and “lack of proper communication”) emerged as the most critical stressors since they occupy the bottom of the model, whereas, four stressors (i.e., “anxious about isolation/quarantine,” “subject to violent crimes,” “feeling frustrated and powerless,” and “exhausting shifts/hours without clear end”) are relatively less critical since they occupy the top of the model. The rest of the stressors occupy the middle of the model and therefore, have moderate-severe effects on frontline soldiers. The results of MICMAC show that the stressor “subject to violent crimes” is classified in the dependent cluster and the remaining fall in the linkage cluster but no stressor falls in independent and autonomous. Overall results indicate that all stressors are relevant to the phenomenon under this study, but they are currently not settled. This study is invaluable for policymakers, frontline soldiers, researchers, the international community, and society since it provides a lot of new information that is helpful in refining strategies and combating influential stressors.

The study is aimed to analyze the determinants of the effectiveness of SOPs in mass gatherings for containing COVID-19. The overall design of the study involves a literature review, data collection by field survey, structural modeling, and analysis. The study is built on the experts' opinion of a focus group (representing people who recently participated in and are responsible for mass gatherings). The study uses the discussion of the literature review to identify the determinants, interpretive structural modeling (ISM) for developing and analyzing a structural model, and Matrice d'Impacts Croises Multiplication Appliquée a un Classement (MICMAC) for corroboration of results of the ISM/classification of determinants. From the literature review, a list of determinants is generated and verified by a panel of experts. The results of the ISM revealed that the determinants “legal environment of the country,” “practicability of SOPs,” “perceived benefit of adapting SOPs,” and “possibilities of avoiding gathering” occupied the top of the model, therefore, they are less critical determinants, whereas “nature of gathering” occupied the bottom of the model, and is thus the most critical determinant. The remaining determinants form the middle of the model, and are therefore moderately severe. The results of MICMAC show that the determinant “perceived benefit of adapting SOPs” is dependent, “nature of gathering” is independent, and all others are linked. The results of MICMAC implicitly substantiate the findings of the ISM. The overall results of the study show that “nature of gathering” is the key determinant. This research does not require a priori theory since it is a theory-building study that uses an inductive approach. It is based on real data and it is useful for local authorities, organizers, participants (attendees/visitors) of mass gatherings, health officials/regulators, researchers, and the community at large. This study has fundamental importance for planning and preparing for such events while ensuring the minimum risk of COVID-19 transmission.

The purpose of the study is to evaluate county-wide health systems using the data set of the first wave of the COVID-19 pandemic. The overall design of study comprises a literature review, secondary data, and a mathematical analysis. It is a cross-sectional quantitative study following a deductive approach. It uses the data of the first wave of the COVID-19 pandemic taken from the website of Worldometer as of April 8, 2020. The study uses a gray incidence analysis model (commonly known as Gray Relational Analysis, i.e., GRA) as its research methodology. On the basis of the results of GRA, a classification has been made under a predetermined scheme of ensigns: much better, better, somewhat better, fair, poor, somewhat worse, and worse health systems. There are a total 211 countries that have been divided into the seven aforementioned categories. Findings of the study show that Southern Africa Development Community (SADC) countries fall predominantly under the much better ensign, whereas Organization for Economic Co-operation and Development (OECD), Schengen Area (SA), and/or European Union (EU) countries fall under the worse ensign. Pakistan falls under the ensign of poor. It is an original attempt to evaluate the response of health systems based on real data using a scientific methodology. The study provides valuable information about the health systems of the countries for forming an informed opinion about the health systems herein. The study provides useful new information for stakeholders and a new framework for future research.

The aim of the study is to reveal the underlying structure of issues of university students taking online classes during the COVID-19 pandemic period. The overall design of the study includes a review of contemporary literature and field survey for data collection and analysis. Discourse of literature coupled with expert opinion has been employed for identification of issues. Interpretive Structural Modeling (ISM) is used for the determination of intra-issue relationships and analyzing the underlying structure. Cross impact matrix multiplication applied to classification (MICMAC analysis) is used as a technique for classifying issues on the basis of driving–dependence power. Results of the literature show that there are 21 major issues faced by the students taking online classes. ISM shows that lack of institutional guidelines, lack of regulators’ guidelines, stress of pandemic situation, and abrupt (not planned) start of online classes are the most critical issues. MICMAC analysis reveals that there is no autonomous issue, 4 (i.e., connectivity issue, shy to use technology, lack of institutional guidelines, and stress of pandemic situation) are independent, 6 other issues are dependent, and the remaining 11 are linking. This is a valuable study having practical implications for regulators, students, parents, and society to understand the current problem. It is an original attempt that contributes toward literature in the form of a structural model and a diagram of classification of issues.

Cross border and inter country financial recourse is like a civilization hold. It is fundamentally important phenomenon to study. Purpose of this study is to investigate inter country global private financial flows in context of current financial regimes. Design of the study is quantitative based on a secondary data taken from website of World Development Indicators (WDI) 2020. A literature review of relevant studies extracted from renowned research databases is also integral part of the overall design of the study. For the purpose of analysis and investigation the study uses Grey Relational Analysis (GRA). GRA is a mathematical technique capable of handling a multitude of alternatives with plenty of criteria simultaneously. It is a ranking technique that generates the reference series, normalizes the data and compares the weighted average grey coefficients with reference series. GRA is a popular methodology espoused in grey systems theory. It is the study of eighty-three countries on the basis of five different criteria. The countries have been ranked according to Grey relational grades by using rank function of excel and are divided into seven different categories on the basis of intensity of financial flows. The categories have been made on the basis of ordinal scale e.g. exceptionally high level of private global financial flows, excellent, very good, good, fair, poor and very poor. Results show that China, Niger, Brazil, Mozambique, Mongolia, St. Vincent and the Grenadines, Cambodia, Grenada, Thailand, Indonesia, Argentina and Maldives have exceptionally high private financial flows, whereas, countries namely Lesotho, Kazakhstan, Uzbekistan, Botswana, Guatemala, Solomon Islands, Afghanistan, Bolivia, Bhutan, Angola and Russian Federation have poor financial flows. Majorly, Arabian Countries (AC), Organization for Economic Co-operation and Development (OECD) and Union of South American Nations (UNASUR) countries fall under exceptionally high ensign, whereas, member countries of Economic Cooperation Organization (ECO) and Southern Africa Development Community (SADC) countries fall under very poor ensign. This study is useful for political governments, international agencies, researchers and academia (students and teachers of international finance). It also provides new information and deeper insights by way of assigning grey relational grades to countries and classifies them into seven groups. It also extends discussion to enlighten upon bloc level position.

Aim of the study is to evaluate climate selected sixty-six countries of the world. Since climate plays a vital role in economic growth of any country and there is dearth of climate models comparing country-wise position to predict as how climate of country is relatively better than others and how it may change in the future and how will it affect on businesses. Overall design of the study consists of review of literature, data elicitation & analysis. It follows quantitative research philosophy based on secondary data obtained from World Development Indicators (WDI). Grey relational analysis is used method of analysis. A classification is made under a predetermined scheme of ensigns like: much better, better, somewhat better, fair, poor, somewhat worse and worse. Results of the study show that Arabian Countries (AC), mostly member countries of European Union (EE), Organization for Economic Cooperation and Development (OECD) and Union of South American Nations (UNASUR) have much better and conducive climate, whereas, South Asian Association for Regional Cooperation (SAARC) countries have worse and unfavorable climate for business. It is an original and valuable study that uses country level data from a reliable source. Results of the study are useful for international business community, political governments, society at large and researchers.

Corporate social responsibility (CSR) is envisaged to offer several outcomes and while customer-specific consequences are unclear or have not obtained due attention, there is a dearth of literature that focuses on perceptual, attitudinal and behavioral outcomes in tandem. Against this backdrop, this study entails the investigation of perceptual (service quality), attitudinal (affective commitment) and behavioral (customer citizenship behavior) outcomes of CSR through a serial mediation mechanism. A total of 994 responses obtained from fast-food restaurants, highlight the fact that CSR influences service quality, affective commitment, and customer citizenship behavior. Moreover, it is witnessed that CSR influences customer citizenship behavior indirectly as well, as the serial mediation mechanism is also proved. The practical and theoretical usefulness of the study is also provided at the end.