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With over 3,000 academic journals in the fields of Business and Economics, most academics face a hard time selecting an adequate journal to submit their work to. In today's demanding academic environment and with the presence of different journal ranking lists (JRLs), the selection becomes more difficult when considering employment, promotion and funding. The purpose of this paper is to explore key differences among multiple JRLs pertinent to the latter common objectives. An extensive analysis is conducted to compare the content of journals in the Australian Business Deans Council (ABDC) Journal Quality list, Scopus and Web of Science (WoS) in the fields of Business and Economics. Then, a case of a university with medium research output is considered where scholarly performance evaluation is based on the ABDC Journal Quality List.

After ranking journals in the fields of Business and Economics based on SCImago Journal Rank (SJR) indicator, JCR's Journal Impact Factor (JIF) and JCR's Eigenfactor (EF), a methodology is proposed to categorize journals in the three JRLs into the same categorization adopted by ABDC. The latter establishes a way to compare the four JRLs under consideration and serves as a basis to compare and analyze the content of journals in the ABDC Journal Quality list, Scopus and WoS. As a proxy impact metric, a normalized citation count is associated with each article based on Google Scholar. The publications of the considered university are then evaluated from the perspective of the four JRLs in terms of citation-based impact and quality while considering the exposure to popular world university ranking tables.

For journals classified under fourth tier by ABDC, over 53 and 59% are not indexed by Scopus and WoS, respectively. In this case study, over 42% of the publications appear in journals that are not listed in JCR despite the fact that over 94% of them are listed by the SJR list. Generally, publications that appear in journals listed by JCR achieve, on a yearly average, significantly higher citation rates when compared to those that appear in journals listed in ABDC and SJR Lists.

A four-tier mapping is proposed for consistent comparison among JRLs. Normalized citation count associated with each article based on Google Scholar is employed for evaluation. The findings provide recommendations for scholars, administrators and global universities, including Euro-Med Universities, on which JRL can be more influential for both faculty development and positioning of the university.

The purpose of this paper is to develop a simple deterministic model that quantifies previously adopted preventive measures driven by the trend of the reported number of deaths in both Italy and India. In addition, the authors forecast the spread based on some selected quantified preventive measures. The optimal exiting policy is derived using the inverse dynamics of the model. Furthermore, the model developed by the authors is dependent on the daily number of deaths; as such, it is sensitive to the death rate but remains insensitive to trends in deaths.

In the wake of COVID-19, policymakers and health professionals realized the limitations and shortcomings of current healthcare systems and pandemic response policies. The need to revise global and national pandemic response mechanisms has been thrust into the public spotlight. To this end, the authors devise an approach to identify the most suitable governmental non-pharmaceutical intervention (NPI) policies, previously adopted in a community, country or region that serve as the foundation for most pandemic strategies.

Leveraging Italy, the authors compare the aftermath by considering three scenarios: (a) recently adopted preventive measures, (b) strictest preventive measures previously adopted, and (c) the optimal exiting policy. In comparison to the second scenario, the authors estimate about twice the number of recoveries and deaths within five months under the first scenario and about 80 times more under the optimal scenario. Whereas in India, the authors applied one scenario of recently adopted preventative measures to showcase the rapid turnaround of their model. According to the new timeline, almost 90% of all deaths in India could have been prevented if the policies implemented in April 2021 were put in place three months prior, i.e. in January 2021.

The novelty of the proposed approach is in the use of inverse dynamics of a simple deterministic model that allows capturing the trend of contact rate as a function of adopted NPIs, regardless of pandemic type.