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The purpose of the research is to analyze the impact of world uncertainty and pandemic uncertainty on Islamic financial markets. For representing Islamic financial markets four different Islamic indices (DJ Islamic index, DJ Islamic Asia–Pacific index, DJ Islamic-Europe index and DJ Islamic-US) are taken.

The study employs quantile-on-quantile regression approach to see the overall dependence structure of variables based on quarterly data ranging from 1996Q1 to 2020Q4. This technique considers how quantiles of world uncertainty and pandemic uncertainty asymmetrically affect the quantiles of Islamic stocks by giving an appropriate framework to apprehend the overall dependence structure.

The findings of the study confirm a strong negative impact of world uncertainty and world pandemic uncertainty on regional Islamic stock indices but the strength of the relationship varies according to economic conditions and across the regions. However, the world pandemic effect remains the same and does not change. Conversely, pandemic uncertainty has a larger effect on Islamic indices as compared to world uncertainty.

Our findings have significant implications for investors and policymakers to take proper steps before any uncertainty arise. A coalition of the central bank, government officials and investment bank regulators would be needed to tackle this challenge of uncertainty.

To the best of the authors' knowledge, none of the current works has considered the asymmetric impact of world and pandemic uncertainties on Islamic stock markets at both the bottom and upper quantiles of the distribution of data.

This study aims to examine the characteristics of financial analysts’ earnings forecasts after COVID-19 outbroke in the USA. Specifically, the authors examine how financial analysts tradeoff between accuracy and responsiveness under investors’ heightened information demand when there is market-wide uncertainty. In addition, the authors investigate how COVID-19 may affect analysts’ cognitive bias.

The research uses a sample of US-listed firms from March 2019 to February 2021, the period surrounding the COVID-19 outbreak in the USA.

The empirical analyses reveal that analysts issue timelier, more frequent, but less accurate forecasts after the COVID-19 outbreak, indicating that analysts become more responsive to investors’ intensified demand for information during the pandemic. Yet, the high uncertainty caused by COVID-19 increases forecasting difficulty. There is no systematic difference regarding the forecast accuracy between high- and low-ability analysts. Meanwhile, high-quality audit can improve forecast accuracy. Contrary to prior findings that analysts tend to underreact to bad news, the empirical evidence suggests that analysts, shaped by the salience bias, overestimate the negative impact of the pandemic. Analysts first issue pessimistic forecasts at the start of the outbreak and then revise forecasts upward steadily as the fiscal year-end approaches.

The study contributes to the literature by adding novel evidence on how COVID-19-induced uncertainty affects analyst forecast characteristics. It also provides additional evidence on how high-quality audit is associated with improved analyst forecast accuracy even under heightened uncertainty of COVID-19.

The purpose of this paper is to examine the response of Travel & Leisure (T&L) stocks of some advanced economies (the USA and United Kingdom) as well as Europe to uncertainty due to pandemics and epidemics. The motivation for the study is derived from the expectation that pandemics and epidemics which are infectious would limit activities and events that require physical interactions such as those associated with T&L, and therefore, returns on related investments may decline during this period.

The authors formulate a model in line with Westerlund and Narayan (2012, 2015) where uncertainty due to infectious diseases is included as a predictor in the valuation of T&L stocks while also controlling for endogeneity bias (for omitted variables bias), conditional heteroscedasticity effect (typical of high frequency data) and persistence (typical of most financial and economic time series).

The authors’ results suggest that contrary to the negative impact of previous cases of pandemics and epidemics on the T&L stocks, the behavior of these stocks during COVID-19 pandemic is modest owing to the positive nexus between equity market volatility due to infectious diseases (EMV-ID) (our proxy for pandemics and epidemics) and the T&L returns during the COVID-19 period. The authors maintain that investors in this market need not panic as the market tends to be resilient to pandemics over time albeit with a lower resilience during daily trading. The results leading to this conclusion are robust to alternative measures of the COVID-19 pandemic.

The peculiarity of this paper on T&L stocks is premised on the introduction of the new datasets for infectious diseases, and the need to include the COVID-19 pandemic given its peculiarity. Essentially, we utilize the Baker et al. (2020) dataset which captures all the pandemics including COVID-19 and a complementary dataset on the COVID-19 pandemic using an alternative approach.

This paper aims to study individuals’ information service satisfaction during the COVID-19 pandemic lockdown in China’s urban communities.

The researchers analyse people’s uncertainties during the pandemic and argue that uncertainties caused by the lockdown can negatively affect people. By reducing people’s uncertainty during the pandemic, community staff members can improve individuals’ information service satisfaction and social order. This study constructs a conceptual model that includes key transparency and self-disclosure constructs and their relationships that can contribute to the trust and satisfaction of the community information service phenomenon. The researchers collected 489 responses to test their hypothesis from an online survey of Chinese residents in areas where the strict lockdown policy was implemented.

The empirical results show that policy and goods information transparency significantly affect information service satisfaction in a positive way, with goods information transparency having the highest impact. Second, self-disclosure of community staff members is also an effective way to increase information service satisfaction. Finally, trust plays a mediating role in the influence of information transparency and self-disclosure on information service satisfaction.

This paper innovatively uses uncertainty reduction theory to examine the effects of information transparency and self-disclosure on satisfaction with community information services. It expands the research in the field of information service satisfaction and extends the scope of the research subjects of self-disclosure.

In this paper, the author examines the role of uncertainty due to pandemic on the predictability of sectoral stock returns in South Africa. This is motivated by the ongoing global pandemic, COVID-19, in predicting sector stock returns.

The study considers estimation of dynamic panel data with dynamic common correlated effects estimator and two pair-wise forecast measures, namely Campbell and Thompson (2008) and Clark and West (2007) tests in dealing with the nested predictive models.

The results show that pandemic uncertainty has a negative and statistically significant effect on the different sector returns, implying that sector stock returns decline as the pandemic outbreak becomes more pronounced. While the single predictor model consistently outperforms the historical average model both for in-sample and out-of-sample, controlling for other macroeconomic variables effect improves the forecast accuracy of infectious diseases uncertainty. These results are consistently robust to both the in-sample and out-of-sample forecast periods, outliers and heterogeneity. These results have implications for portfolio diversification strategies, which we set aside for future research.

The empirical literature is satiated with studies on how news can predict economic and financial variables, however, the role of uncertainty due to infectious diseases in the stock return predictability especially at the sectoral level is less understudied, this is the main contribution of the study.

This study aims to examine uncertainties created due to the pandemic that multinational enterprises (MNEs) had to confront. It also assesses MNEs’ response to these uncertainties through their dynamic capabilities (DCs). It relied on theories of DCs and organizational learning.

MNEs listed in Fortune Global 500 served as the population of the study, while data were retrieved from their respective corporate websites. The final phase generated 704 documents systematically analyzed for dialogic communication. Content analysis was used to make inferences.

This study found six distinct uncertainties created by COVID-19. Furthermore, it was found that irrespective of industry-type or headquarters location, organizations could transform their internal processes and remain resilient by strategically sensing and responding to exogenous shocks through DCs.

The use of dialogic communication through website analysis could be prone to misrepresentations and data exaggeration from organizations. However, this limitation was mitigated by focusing on Fortune Global 500 MNEs, which are reputable global corporations.

Dealing with and coping with the uncertainties created by COVID-19 presents MNEs with valuable capabilities and experience in handling future global viral diseases when they inevitably occur.

Unlike previous shocks, COVID-19 had an immeasurable global disruption to MNEs’ business operations. Evidence was found that MNEs could remain resilient by using DCs in response to uncertainties amid an exogenous shock. It makes a theoretical contribution by extending what was previously known about DCs, uncertainties and exogenous shocks.

The outbreak and the spreading of the COVID-19 pandemic have impacted the global financial sector, including the alternative clean and renewable energy sector. This paper aims to assess the impact of the pandemic, COVID-19 on the stock market indices of the clean energy sector using quantile regression methods.

This study utilized daily data sets on the four major categories of stocks: (1) Morgan Stanley Capital International Global Alternative Energy Index, (2) WilderHill Clean Energy Index, (3) Renewable Energy Industrial Index (RENIXX) and (4) the S&P 500 Global Clean Index. The study adopts a multifactor capital asset pricing model.

Clean and alternative energy stocks are powerful instruments for diversification. However, the impact of the volatility index induced by infectious disease is negative and significant across quantiles.

For investors and policymakers, considering how the uncertainty caused by COVID-19 and the geopolitical index influences renewable energy markets is of great practical importance. For investors, it throws insights into portfolio diversification. For policy makers, it helps to devise strategies to reboot the economy along the lines of the deployment of renewables. This study sheds light on a global green-energy transition and has practical implications for renewable energy resilience in post-pandemic times.

This paper can be considered as a pioneer that explores the nexus between oil prices, interest rates, volatility index, and geopolitical risk upon the stock indices of clean and alternative sources of (renewable) energy in the COVID-19 pandemic situation. The results have important insights into the area of energy and policy decision-making. Additionally, the paper's novelty lies in using the explanatory variables associated with the Covid 19 pandemic.

This conceptual paper aims to provide a further understanding of the impact of economic policy uncertainty (EPU), news framing and information overload on panic buying behavior during the COVID-19 pandemic.

Drawing on earlier research and news releases about the COVID-19 outbreak, this paper advances testable propositions based on the protection motivation theory and information processing theory.

This paper infers that the major shift in consumer decision-making towards panic buying is a result of high EPU. International reports have contributed to deepening this uncertainty, and the consequences of this EPU are expected to affect the economic recovery through 2022. Furthermore, the adoption of particular frames of the pandemic has played a key role in the dissemination of misinformation and fake news during the public health crisis and affected purchasing decisions. The study also infers that the perceived threat among consumers is driven by information overload as a source of mistrust towards economic and health information sources.

This paper addresses two theoretical gaps associated with consumer buying behaviour. First, it highlights the impact of EPU, as a macroeconomic indicator, on consumer buying behaviour. Second, this paper is an attempt to integrate theories from different disciplines to foster an adequate understanding of buying behavior during the COVID-19 outbreak period.

This study is to reconfigure a hierarchical supply chain model utilizing databases and text files to understand future pathways due to COVID-19 pandemic has had a bullwhip effect, disrupting the global supply chain, and a mechanism is needed to address this disruptive event under pandemic uncertainties.

To address this mechanism, this study employs bibliometric analysis and text mining to reconfigure a hierarchical supply chain model under pandemic conditions and associates it with social media to conduct an intuitive visual analysis.

The current academic concerns are related to an overconcentration on risk management and a data-driven approach, generating an enormous gap between the concerns of academics and those of the public. The evidence shows that for both countries with outstanding performance and those that need improvement, the efficiency in terms of preventing the spread of the pandemic should be promoted.

This study contributes to (1) reconfiguring a hierarchical supply chain model under pandemic uncertainties and (2) bridging theory and practice by offering comparable interrelated attributes to guide post-COVID-19 strategies in the supply chain. The findings are that the supply management approach and big data are attributes that involve the concerns of world public and academics under pandemic uncertainties.

Although the use of management control systems (MCS) in crisis management has received extensive attention, limited knowledge exists regarding the benefits of the broad scope, timeliness, integration and aggregation dimensions. This study aims at examining the performance implications of the context-structure combinations of pandemic management strategy (PMS), MCS use and pandemic-induced uncertainty of public health institutions (PHIs) in Ghana.

Data were collected using online survey questionnaire where 246 public health managers qualified for the study. Data were analyzed using covariance-based structural equations modeling (version 23).

PMS was found to have a significant and positive impact on three (broad scope, timeliness and aggregation) of the four dimensions. The integrated dimension was statistically insignificant. In addition, the three dimensions had a significant impact on top managers’ satisfaction with MCS use, which in turn impact on cost containment and quality of care. Finally, COVID-19 uncertainty moderated the relationship between MCS use and operational performance.

The three dimensions of broad scope, timeliness and aggregation are critical for PHIs when it comes to crisis management. Moreover, the presence of pandemics strengthens the relationship between top manager use of MCS and performance in health care. More sophisticated MCS information is required when managing pandemic-related crisis by PHIs.

This study presents a theoretical framework that integrates PMS, MCS use and performance of public health care from a contingency perspective. It extends the benefits of contingency theory to include the three dimensions of MCS with respect to crisis management.