Telework quality and employee well-being: Lessons learned from the COVID-19 pandemic in Italy

The coronavirus disease 2019 (COVID-19) forced organisations to implement intensive telework for many of their workers overnight. This scenario was completely new, and the emergency caused by COVID-19 created the possibility of experimenting with new ways of working with an unknown impact on employee well-being. Drawing on previous literature, we defined a model of telework quality consisting of the following four core domains: agile offices within organisations, functional remote workstations, flex-time and engaging management. We identified two high-quality and low-quality telework profiles using latent profile analysis on a data sample of 2295 insurance and financial sector employees. Demographic, occupational and procedural characteristics were associated with the probability of being in the positive or negative profiles. Our results showed that employees' emotional exhaustion and work engagement levels were related to telework quality. This study suggests that organisations need to consider the quality of telework to effectively adopt new ways of working that foster employee well-being.     

Identifying emergence process of group panic buying behavior under the COVID-19 pandemic

The sudden COVID-19 caused frequent incidents of large-scale material panic buying, resulting in imbalance in supply and demand of goods and threatening social stability. It is of great significance to analyze the formation of group panic buying and help alleviate such action. This paper takes the panic buying phenomenon as the research target, quantifies the internal and external factors affecting individual buying behavior, restores the selection process of individual buying behavior, and constructs the emergence model of group panic buying behavior by using the idea of cluster dynamics. Through simulations, we find that: (1) The epidemic information intensity has a significant impact on the emergence of group rush buying behavior. (2) Government intervention plays a significant role in reducing the scale of group rush buying. Besides, the effects of intervention reach the best before people who do not participate in rush buying disappear. In addition, we also discuss the impact, limitations and future research directions.     

Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach

Macroeconomic data are an important piece of information in decision making for both the public and private sectors in Thailand. However, the release of key macroeconomic data, usually in a lower frequency such as quarterly, is not always in a timely manner. Using the higher frequency data such as monthly and daily to analyze or forecast the lower frequency data can mitigate the release timing effect. This study applies the mixed-frequency data approach to analyze and forecast Thai key macroeconomic data. The mixed data sampling regressions with various specifications are employed and implemented through some macroeconomic data such as gross domestic product and inflation. The results show that in most cases the mixed-frequency models outperform the autoregressive integrated moving average model, which we used as the benchmark model, even during the COVID-19 period. Some policy implications can also be drawn from the analysis.     

Mass gatherings contributed to early COVID-19 mortality: Evidence from US sports

Social distancing is important to slow the community spread of infectious disease, but it creates enormous economic and social cost. Thus, it is important to quantify the benefits of different measures. We study the ban of mass gatherings, an intervention with comparably low cost. We exploit exogenous variation in the number of National Basketball Association and National Hockey League games, which arises due to the leagues' predetermined schedules, and the sudden suspension of the 2019–2020 seasons. We find that, among clusters of counties that are adjacent to sports venues, each additional mass gathering increased the cumulative number of COVID-19 deaths by 10.3%.     

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policymakers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision-makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.     

Supply Chains in Times of Crisis: Evidence from Kenya’s Production Network

Trading relationships between suppliers and buyers play a key role in transmitting both local and international shocks. We use transaction-level data from Kenya to study the relevance of a firm’s domestic network position and links to international supply chains in determining its trajectory during the COVID-19 crisis. We document that firms with high exposure to import and export markets tend to be larger, older, and employ more workers. The specialisation of direct importers, often intermediaries, on international markets made them very vulnerable to the initial COVID-19 shock. Exporters, one-third of whom operate in primary sectors, experienced a less severe decline in sales. We find that both importers and exporters adjust their domestic supply chains in response to international trade shocks — before and during the crisis alike. Sourcing from international markets does not crowd out domestic purchases, while sales abroad and at home can act as substitutes. Diversified domestic supply chains helped firms to mitigate the impact of the COVID-19 crisis and recover more strongly.     

Economic uncertainty and national bitcoin trading activity

This study analyzes the dynamics between bitcoin trading, price activities, and economic surprise shocks from a broad and novel perspective on a national level. We start by estimating the response of bitcoin trading in terms of volume and volatility to economic surprises. Following this, we extend our framework by applying a Generalized AutoRegressive Conditional Heteroskedasticity model to get an indication of the volatility reaction of national bitcoin activities to economic surprise shocks. Our results show that local and global shocks affect local bitcoin activities and trading volatilities, confirming that economic events affect bitcoin markets. We argue that increased trading activity, coupled with a price reaction, indicates that bitcoin might be considered a hedge or safe haven asset against economic uncertainty. We find evidence that bitcoin is treated as a speculative asset against negative economic policy uncertainty shocks in Canada pre-Covid-19. These results change during the Covid-19 pandemic, leading to a significant structural break. Here, we find indications that bitcoin might be treated as a safe-haven asset in New Zealand and Australia. This shows that bitcoin behaves differently depending on the studied country, underlining the importance of country-level studies. It also shows that bitcoin is a new asset that is evolving rapidly and that the period in which it is studied is important.     

疫情影响下全球邮轮网络格局与重构

新冠疫情对全球邮轮旅游业产生了严重的影响,改变了邮轮网络的地理空间格局。本文主要分析了疫情前期、疫情初期和疫情后期3个阶段的邮轮网络格局和空间特征,研究发现：（1）疫情前期,邮轮网络呈现出全球性的线状分布格局;（2）疫情初期,邮轮网络呈点状分布,在区域层面、国家层面和港口层面具有显著的集聚性特征;在企业层面,因企业船队规模的大小差异,邮轮网络分别呈点状散布和集聚分布;中国-东亚区域邮轮网络呈现出空白化特征,主要源于出入境管理和港口布局的差异。（3）疫情后期,全球邮轮经历了尝试性复航到部分复航,邮轮网络由单一区域到多区域的渐进变化。最后,基于疫情发展和邮轮网络演进特征,从未复航区域和已复航区域两个方面,重点讨论了东亚、澳新等区域邮轮网络可能的重构格局和已复航区域邮轮网络未来的扩展变化,以期为后疫情时代邮轮运营商的网络组织、全球邮轮网络的演进提供参考和借鉴。     

When does the manufacturing reshoring strategy create value?

Reshoring has gained a lot of attention recently by academics and practitioners alike, and is promising to become even more relevant in the aftermath of the COVID-19 pandemic. Building on earlier research on the effects of reshoring announcements on the short-term market value of the firm, this work employs an event-study methodology and aims to understand under which circumstances the market perceives reshoring as potentially more (or less) value-creating. The analysis of a sample of 64 reshoring instances from 2005 to 2019, announced by 54 firms from eight developed economies, suggests that investors are more confident in the firm's future cash-flow potential when: a) it invests in productive activities at home, instead of overseas, i.e. ‘kept-from-offshoring’ (as opposed to actual relocations of activities, i.e. ‘back-reshoring’); b) the reshoring instance is communicated as a ‘plan’ (rather than a fixed ‘decision’); c) no state- or government-induced financial incentives are involved; d) the motivations are primarily ‘cost-efficiency seeking’ (rather than ‘customer perceived value seeking’).