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Early evidence suggests that coronavirus disease 2019 (COVID-19) caused a sharp deterioration in fiscal accounts worldwide. This paper empirically assesses the fiscal impact of previous pandemics and epidemics.

Using a large sample of 170 countries from 2000 to 2018, this study relies on Jordà's (2005) local projection method to trace pandemics' short- to medium-term dynamic impact on several fiscal aggregates.

This paper shows that (qualitatively) similar responses to those observed more recently with COVID-19 have characterized the effects of previous pandemics. While the fiscal effect has been economically and statistically significant and persistent, it varies; pandemics affect government expenditures more strongly than revenues in advanced economies, while the converse applies to developing countries. The author also finds that asymmetric responses depend on whether a country is characterized as a chronic fiscal surplus or deficit type. Another factor that generates an asymmetric fiscal response is the prevailing phase of the business cycle the economy was in when the pandemic shock hits.

This paper's findings provide a lower bound to what the current COVID-19 pandemic will inflict on countries’ fiscal situation. That said, the set of pandemics and epidemics used in this paper are geographically more concentrated and did not affect all countries in such a systemic and synchronized manner as did COVID-19 more recently.

This is the first paper to explore the fiscal side of this type of health-related shocks, as most of the literature has focused on the more traditional macroeconomic effects.

This research paper aims to discover the elements of good physician leadership as perceived by physicians and to find out how the findings connect to the leadership theory.

The subjects (n = 50) of this qualitative study are physicians from four hierarchical levels (residents/specialising physicians, specialists, heads of departments and chief physicians). Content analysis with a constructivist-interpretative approach by thematisation was the chosen method, and it was also analysed how major leadership theories relate to good physician leadership.

Physician leaders are expected to possess the professional skills of physicians, understand how the work affects physicians’ lives and be competent in applying suitable leadership approaches following different situations and people. Trust, fairness, empathy, social skills, two-way communication skills, regular feedback, collegial respect and emotional intelligence are expected. As medical expertise connects leaders and followers, success in medical leadership comes from credibility in medical expertise, making medical leadership an inseparable part of good physician leadership. Subordinates are physician colleagues, who have their informal leadership roles on their hierarchical levels, making physician leadership a multidimensional leadership setting wherein formal leaders lead informal leaders, which blurs the traditional leader–follower boundary. In summary, good physician leadership is leadership through medical expertise combined with good manners, collegiality and traits from different kinds of leadership theories.

This study discovers elements of good physician leadership in a Finnish health-care context in which no similar prior empirical research has been carried out.

Corporate social responsibility (CSR) is significant in the financial market. Despite plenty of existing research on CSR, few studies have quantified the fine-grained aspects of CSR and examined how diverse CSR aspects are associated with firms' trade credit. Based on the released CSR reports, this paper strives to measure the CSR fulfillment of firms and examine the relationships between CSR and trade credit in terms of textual features presented in these reports.

This research proposes a natural language processing-based framework to extract the overall readability and the sentiment of fine-grained aspects from CSR reports, which can signal the performance of firms' CSR in diverse aspects. Furthermore, this paper explores how the textual features are associated with trade credit through partial dependence plots (PDPs), and PDPs can generate both linear and nonlinear relationships.

The study’s results reveal that the overall readability of the reports is positively associated with trade credit, while the performance of the fine-grained CSR aspects mentioned in the CSR reports matters differently. The performance of the environment has a positive impact on trade credit; the performance of creditors, suppliers and information disclosure, shows a U-shaped influence on trade credit; while the performance of the government and customers is negatively associated with trade credit.

This study expands the scope of research on CSR and trade credit by investigating fine-grained aspects covered in CSR reports. It also offers some managerial implications in the allocation of CSR resources and the presentation of CSR reports.

The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.

Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).

A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.

The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.

This research proposes a framework by which universities can define and implement projects that transform them into entrepreneurial universities. The framework helps decision-makers identify suitable goals and strategies, gather a list of projects to fulfill the goals and strategies and prioritize the projects and form a portfolio.

In the proposed framework, importance–performance matrix, hierarchical strategic planning, Delphi technique, DEMATEL-based ANP and a multi-objective model are used. The mathematical model consists of four objective functions including efficiency, quality and balance maximization and also cost and risk minimization. The proposed framework is applied to Amirkabir University of Technology, Tehran, Iran, and the results are brought in this paper.

The output of the proposed framework is a portfolio of projects that aims to transform a traditional university into a third-generation one. Although the final portfolio must be customized for different universities, the proposed steps of the framework can be helpful for almost all cases.

The suggested framework is unique and uses both qualitative and quantitative techniques for project portfolio selection.