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The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.

The authors examine the impact of analysts’ prior epidemic experience on forecast accuracy by comparing the changes from the pre-COVID-19 period (calendar year 2019) to the post-COVID period extending up to March 2023 across HRE versus non-HRE analysts. The authors consider a full sample (194,980) and a sub-sample (136,836) approach to distinguish “Recent” forecasts from “All” forecasts (including revisions).

The study's findings reveal that forecast accuracy for HRE analysts is significantly higher than that for non-HRE analysts during COVID-19. Specifically, forecast errors significantly decrease by 0.6% and 0.15% for the “Recent” and “All” forecast samples, respectively. This finding suggests that analysts’ prior epidemic experience leads to an enhanced ability to assess the uncertainty around the epidemic, thereby translating to higher forecast accuracy.

The finding that the expertise developed through an experience of following high-risk firms in the past enhances analysts’ performance during the pandemic sheds light on a key differentiator that partially explains the systematic difference in performance across analysts. The authors also show that industry experience alone is not useful in improving forecast accuracy during a pandemic – prior experience of tracking firms during epidemics adds incremental accuracy to analysts’ forecasts during pandemics such as COVID-19.

The study findings should prompt macroeconomic policymakers at the national level, such as the central banks of countries, to include past epidemic experiences as a key determinant when forecasting the economic outlook and making policy-related decisions. Moreover, practitioners and advisory firms can improve the earning prediction models by placing more weight on pandemic-adjusted forecasts made by analysts with past epidemic experience.

The uncertainty induced by the COVID-19 pandemic increases uncertainty in global financial markets. Under such circumstances, the importance of analysts’ role as information intermediaries gains even more importance. This raises the question of what determines analysts’ forecast accuracy during the COVID-19 pandemic. Building upon prior literature on the role of analyst experience in shaping analysts’ forecasts, the authors examine whether experience in tracking firms exposed to prior epidemics allows analysts to forecast more accurately during COVID-19. The authors find that analysts who have experience in forecasting for firms with high exposure to epidemics (H1N1, Zika, Ebola, and SARS) exhibit higher accuracy than analysts who lack such experience. Further, this effect of experience on forecast accuracy is more pronounced while forecasting for firms with higher exposure to the risk of COVID-19 and for firms with a poor ex-ante informational environment.

The crude oil supply chain (COSC) is one of the most complex and largest supply chains in the world. It is easily vulnerable to extreme events. Recently, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (often known as COVID-19) pandemic created a massive imbalance between supply and demand which caused significant price fluctuations. The purpose of this study is to explore the influential factors affecting the international COSC in terms of consumption, production and price. Furthermore, it develops a model to predict the international crude oil price during disease outbreaks using Random Forest (RF) regression.

This study uses both qualitative and quantitative approaches. A qualitative study is conducted using a literature review to explore the influential factors on COSC. All the data are extracted from Web sources. In addition to COVID-19, four other diseases are considered to optimize the accuracy of predictive results. A principal component analysis is deployed to reduce the number of variables. A forecasting model is developed using RF regression.

The findings of the qualitative analysis characterize the factors that influence international COSC. The findings of quantitative analysis emphasize that production and consumption have a higher contribution to the variance of the data set. Also, this study found that the impact caused to crude oil price varies with the region. Most importantly, the model introduced using the RF technique provides a high predictive ability in short horizons such as infectious diseases. This study delivers future directions and insights to researchers and practitioners to expand the study further.

This is one of the few available pieces of research which uses the RF method in the context of crude oil price forecasting. Additionally, this study examines international COSC in the events of emergencies, specifically disease outbreaks using machine learning techniques.

Due to the frenetic and dynamic working conditions ascribed to architecture, engineering and construction (AEC) project organizations, enormous research has addressed the poor mental health propensity of project management practitioners (PMPs). However, research has not considered the distant factors related to organizational design causing poor mental health. Therefore, this study addresses the problem by integrating institutional theory, agency theory and resource-based theory (RBT) to explore the relationship between organizational design elements: project governance, knowledge management, integrated project delivery, project management skills and mental health management indicators. Examples of mental health management indicators include social relationships, work-life balance and project leadership.

Purposive sampling method was adopted to collect survey data from 90 PMPs in 60 AEC firms in Australia. Structural equation modelling (SEM) was utilized to test the relationship between the variables.

The research found that project governance, knowledge management and integrated project delivery are positively correlated to mental health management indicators. However, the research finding suggests that project management skills have a negative impact on mental health management indicators.

The findings offer guidelines to AEC firms on achieving positive mental health management outcomes through concentration on project governance, knowledge management and integrated project delivery. It further calls for a reconsideration of existing project management skills causing poor mental health management outcomes.

The purpose of the study is to analyse the impact of COVID-19 on the hospitality industry during the rise of worldwide pandemic crises using Twitter analysis. The study is based on 57,794 English-language tweets mined from Twitter from 1 April 2020 to 15 October 2020. Based on thematic and sentiment analysis, the study found that overall sentiments expressed on Twitter were negative. This chapter contributes to existing knowledge about the COVID-19 crisis and broadens the respondents’ understanding of the potential impacts of the crisis on the most vulnerable tourism and hospitality industry. This research emphasises the sustainable revival of the hospitality industry.

The authors believe the COVID-19 pandemic has an impact on supply and demand. The potential decline in real sector performance leads to lower expectations of securities performance. The uncertainty of future performance can change investor behaviour. This study tried to gain insight into stock investor behaviour during the COVID-19 pandemic. The results showed that the majority of the investor realized and believed the pandemic would affect the stock market performance. Hence, they did not show herding behaviour and were very confident during the COVID-19 pandemic. The survey also indicates that investors tend to avoid risk rather than take the opportunity to buy at a lower price. Moreover, investors believe that the COVID-19 vaccine will soon be found, and the economy will return to normal. Government and self-regulated organizations (SRO) are responsible for making effective policies to convince the investors about the future prospect.

Joint research is pointed out by the literature as a potentially virtuous cooperation scheme to generate learning in the public sphere and beneficial effects in society. The purpose of this study, based on the Argentine experience in the COVID-19 pandemic, is to analyze the network of capacities, relationships and effects generated, over time, by a series of projects financed by the State in 2010, to clarify the link between learning effects and social effects.

A qualitative methodology focused on the multiple case study method was used. Each case covers joint R&D projects financed 10 years ago by the state that subsequently led to different solutions for COVID-19.

The work identifies a public learning process that integrates both industry’s contributions and the intellectual dimension of economic benefits and their translation into specific capabilities; conceptualizes the capacities accumulation process as a multiplier of social effects (direct and indirect) that emerge as knowledge is reused; identifies the articulation between different schemes as a condition for learning effects and social effects to manifest over time.

An aspect not studied in the literature is addressed, the relationship between the learning process induced by joint research, in terms of capabilities, and the social effects specifically generated over time. This is taking place in a context, such as the COVID-19 pandemic, where calls from the scientific and academic community to promote science–industry cooperation are multiplying.

This chapter seeks to understand the intersection between eGovernment, social media, and digital inequalities by examining the disparate flow of information during the COVID-19 pandemic. Developed economies are increasingly transitioning to digital interfaces for information dissemination and provision of services. The authors explore the potential of, and challenges facing eGovernment by looking at the use of social media during the COVID-19 pandemic. This chapter employs a case study approach to probe the dynamics of government-initiated efforts at information dissemination through the Center for Disease Control and Prevention’s (CDC) website and social media account on Twitter. The analysis in this chapter uses NodeXL to examine communication roles played by government and non-governmental actors within this slice of the Twittersphere centered around CDC@gov. As the findings demonstrate, non-governmental actors played key roles in the dissemination of public health messaging. The authors analyze these data with an eye to the potential of social media for public health communication and extrapolate that understanding to the use of digital access and social media for the provision of accurate, official information in other circumstances. While the COVID-19 pandemic was a global health crisis, individuals and households face individual or local crises every day. This angle of vision allows the chapter to conclude with recommendations pertaining to government-led information dissemination for the public good during crisis and non-crisis situations alike. In the concluding section, the authors probe the degree to which eGovernment can also address digital inequalities including connectivity, device, and literacy gaps. The authors offer solutions needed for eGovernment initiatives in light of challenges posed by digital inequalities to ensure that digital information sharing and services are accessible to all.

This study analyzes the evolution of topics related to COVID-19 on Chinese social media platforms with the aim of identifying changes in netizens' concerns during the different stages of the pandemic.

In total, 793,947 posts were collected from Zhihu, a Chinese Question and Answer website, and Dingxiangyuan, a Chinese online healthcare community, from 31 December, 2019, to 4 August, 2021. Topics were extracted during the prodromal and outbreak stages, and in the abatement–resurgence cycle.

Netizens' concerns varied in different stages. During the prodromal and outbreak stages, netizens showed greater concern about COVID-19 news, the impact of COVID-19 and the prevention and control of COVID-19. During the first round of the abatement and resurgence stage, netizens remained concerned about COVID-19 news and the prevention and control of the pandemic, however, less attention was paid to the impact of COVID-19. During later stages, popularity grew in topics concerning the impact of COVID-19, while netizens engaged more in discussions about international events and the raising of spirits to fight the global pandemic.

This study contributes to the practice by providing a way for the government and policy makers to retrospect the pandemic and thereby make a good preparation to take proper measures to communicate with citizens and address their demands in similar situations in the future.

This study contributes to the literature by applying an adapted version of Fink's (1986) crisis life cycle to create a five-stage evolution model to understand the repeated resurgence of COVID-19 in Mainland China.

This study aims to investigate the mechanism of the misinformation spread based on the elaboration likelihood model and the effects of four factors – emotion, topic, authority and richness – on the depth, breadth and structural virality of misinformation spread.

The authors collected 2,514 misinformation microblogs and 142,006 reposts from Weibo, used deep learning methods to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network using the network analysis method.

Results show that misinformation has a smaller spread size and breadth than true news but has a similar spread depth and structural virality. The differential influence of emotions on the structural characteristics of misinformation propagation was found: sadness can promote the breadth of misinformation spread, anger can promote depth and disgust can promote depth and structural virality. In addition, the international topic, the number of followers, images and videos can significantly and positively influence the misinformation's spread size, depth, breadth and structural virality.

The influencing factors of the structural characteristics of misinformation propagation are clarified, which is helpful for the detection and management of misinformation.