COVID-19 pandemic and firm performance in the insurance industry in developed and emerging markets

The increasing turmoil in the external organizational setting or business environment has focused attention on capabilities and resources as the primary source of competitive advantage. Obviously, this statement points to the application of the resource-based view (RBV) of organizational management. Nevertheless, what constitutes RBV remains an illusion in many quarters of organizational management, as scholars have managed to put up their personal ideas, and managed to converge on phenomenon-driven theories, in addition to RBV. This paper reviews the concepts of RBV in light of knowledge management to highlight some critical pitfalls that might have eluded the research community on the subject matter of RBV. To this end, this paper’s educational value lies in the fact that it simplifies the concept of RBV to the new researcher in a fashion that is capable of appealing to his or her level. A cross-sectional qualitative research approach was employed in an effort aimed at understanding the role of RBV in creating a sustainable competitive advantage and key competencies. A total of 20 relevant articles were searched from different databases and search engines, including Scopus, EBSCO, ABI Inform, IEEE, PubMed, Science Direct, SABINET, IEEE, Bing, Science Direct, and Google Scholar. The findings indicate that RBV plays an important role and assists organizations not only create, nurture, and maintain competitive advantage, but also understand the collective resources needed to compete favorably in a globalized and highly competitive market. With expert knowledge workers at its core to provide support for knowledge creation, sharing, and utilization, the RBV principles discussed in this paper promise to guarantee a methodological step geared towards the achievement of competitive advantage. It, therefore, makes an incremental contribution to the RBV to attain modest improvement in organizational settings.

This study aims to investigate the role of Fintech in predicting the spread of COVID-19 based on consumers’ Fintech perceptions and behavior before and after the outbreak of COVID-19. The study used a questionnaire-based survey distributed in different countries of the world using the LinkedIn platform for this purpose to reach the targeted population. The snowball sampling technique was used. The study targeted consumers with Fintech experience, especially in digital payments services. 507 samples were retrieved. For the analysis, the Structural Equation Modeling (SEM) was used. The study revealed novel results in predicting COVID-19 spread; these three variables (Fintech Behavior before COVID-19, Fintech Behavior after COVID-19, and Fintech Perception after COVID-19) could predict 52.5% of the variance in the dependent variable (COVID-19 Spread) (R² = 0.525, p < 0.05). The findings show that Higher Fintech perception and behavior among Fintech users will help in reducing the spread of COVID-19 by avoiding the use of contact payment methods. Contactless payment methods are the main tools in Fintech that might help in avoiding the probability of COVID-19 spread. Consumers’ Fintech perceptions and behavior are the most influencing factors that could predict the spread of COVID-19 in this study, where digital payments are the main concern. It is recommended that consumers adopt digital payment methods and tools, especially contactless payment methods, to fulfill their financial services. Other researchers are also encouraged to use the same model to predict the spread of this virus in the Fintech context.

The aim of the article was to find out the optimal capital structure of the companies in relation to their maximum performance. To reach this aim, the data of the companies operating in the field of heat industry of the Slovak Republic were used. As the first method, a correlation matrix was applied. It was found out that there is statistically significant relationship between capital structure indicators and performance of the companies. Due to the lack of data in time series, the authors were not able to apply multiple regression model to assess the impact of these indicators on performance. Therefore, a method of modelling was used to analyze the impact of the change in capital structure on performance. Modelling was based on the principle of a gradual change in the capital structure in favor of debt. By the increase in debt, it was confirmed that there was a change in the values of selected indicators. In the course of analysis, it was confirmed that the value of EVA equity increased with the rising indebtedness by which the proposition I of the modified MM theory was supported. The performance expressed by EVA entity indicator is at its minimum when the capital structure is 90:10 in favor of equity. By increasing the debt, EVA entity rises. Based on these results, it can be stated that the performance of selected companies increases when the share of debt also rises, even when taking into account the rising financial risks.