How Digitalization, Work-Family Balance, and Work Efficiency Can Influence Employees' Preferences for Teleworking in the Future

During Covid-19 pandemic many employees found themselves in the new position of teleworkers. Proof of the last years, teleworking remains an alternative to the classic system, reason why in this paper we aim to examine, based on an online questionnaire, the perceptions of Romanian employees about the very complex aspects that teleworking involves: the new ICT tools and technologies adoption, impact on work efficiency, work-family balance, and employees physical and mental health. The paper is based on an empirical analysis of data, and the authors have used as research methods: testing the independence of groups (Mann-Whitney U and Kruskal Wallis) and the correlation between variables, categorical principal components analysis (CATPCA) and logistic regression model. Our findings show that several factors, such as flexible working hours, family time, and autonomy have a positive influence on the decision to telework, while IT security risks, interruptions and virtual meetings were not perceived by respondents as negatively influencing their decision to telework. However, the consequences for mental health, such as mental stress, lack of socialisation and the difficulty of separating working time from family time, lead respondents to prefer another working system. The implications of the research are relevant for policy makers, employers, and employees as they reveal which categories of employees are more likely to telework and why.

Healthcare under pressure: modelling COVID-19 fatalities with multiscale geographically weighted regressions

Purpose: The paper aims to disentangle the factors behind territorial disparities in the coronavirus disease 2019 (COVID-19) case fatality ratio, focusing on the pressure put by the pandemic on healthcare services and adopting a spatial perspective. Design/methodology/approach: Multiscale geographically weighted regression (MGWR) models have been used for uncovering the spatial variability in the impact of healthcare services on COVID-19 case fatality ratio, allowing authors to better capture the real spatial patterns at local level. The authors proved that this approach yields better results, and the MGWR model outperforms traditional regression methods. The selected case studies are two of the biggest UE countries, among the first affected by a high incidence of COVID-19 cases, namely Italy and Germany. Findings: The authors found sizeable regional differences in COVID-19 mortality rates within each of the analysed countries, and the stress borne by local healthcare systems seems to be the most powerful factor in explaining them. In line with other studies, the authors found additional factors of influence, such as age distribution, gender ratio, population density and regional development. Originality/value: This research clearly indicated that COVID-19 related deaths are strongly associated with the degree of resilience of the local healthcare systems. The authors supply localized results on the factors of influence, useful for assisting the decision-makers in prioritizing limited healthcare resources. The authors provide a scientific argument in favour of the decentralization of the pandemic management towards local authorities not neglecting, however, the necessary regional or national coordination. © 2021, Emerald Publishing Limited.

Healthcare under pressure: modelling COVID-19 fatalities with multiscale geographically weighted regressions

Purpose: The paper aims to disentangle the factors behind territorial disparities in the coronavirus disease 2019 (COVID-19) case fatality ratio, focusing on the pressure put by the pandemic on healthcare services and adopting a spatial perspective. Design/methodology/approach: Multiscale geographically weighted regression (MGWR) models have been used for uncovering the spatial variability in the impact of healthcare services on COVID-19 case fatality ratio, allowing authors to better capture the real spatial patterns at local level. The authors proved that this approach yields better results, and the MGWR model outperforms traditional regression methods. The selected case studies are two of the biggest UE countries, among the first affected by a high incidence of COVID-19 cases, namely Italy and Germany. Findings: The authors found sizeable regional differences in COVID-19 mortality rates within each of the analysed countries, and the stress borne by local healthcare systems seems to be the most powerful factor in explaining them. In line with other studies, the authors found additional factors of influence, such as age distribution, gender ratio, population density and regional development. Originality/value: This research clearly indicated that COVID-19 related deaths are strongly associated with the degree of resilience of the local healthcare systems. The authors supply localized results on the factors of influence, useful for assisting the decision-makers in prioritizing limited healthcare resources. The authors provide a scientific argument in favour of the decentralization of the pandemic management towards local authorities not neglecting, however, the necessary regional or national coordination. © 2021, Emerald Publishing Limited.

Vulnerability and resilience in health crises. Evidence from european countries

The purpose of this paper is to draw the first lessons from the on-going coronavirus crisis and to identify viable solutions for what should become the goal of any country: transforming their own economies into sensitive and responsive economies regarding public health problems. The originality of our approach is given by its objective as well as the strategy employed for verifying research hypotheses. The objective is twofold: detecting the indicators that may constitute signals for the vulnerability of countries in times of health crisis and highlighting the underlying factors of the resilience capacity. Many indicators have been considered: six indicators concerning Covid-19 pandemic and 27 socio-economic indicators. Three main hypotheses have been formulated and tested using various statistical methods. Our findings provide deep insights for understanding how Covid-19 crisis is correlated to specific economic (urbanization, sectorial employment, health system) and demographic factors (aging, mortality). The study has succeeded in identifying the pattern of a country with greater resilience and better ability to cope with a health crisis. Our results could be useful when forecasting the spread of another pandemic wave, its impact on people’s health and estimating how some markets will be reshaped. © 2021 The Author(s). Published by Vilnius Gediminas Technical University.