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This paper is to produce different scenarios in forecasts for international tourism demand, in light of the COVID-19 pandemic. By implementing two distinct methodologies (the Long Short Term Memory neural network and the Generalized Additive Model), based on recent crises, we are able to calculate the expected drop in the international tourist arrivals for the next 12 months. We use a rolling-window testing strategy to calculate accuracy metrics and show that even though all models have comparable accuracy, the forecasts produced vary significantly according to the training data set, a finding that should be alarming to researchers. Our results indicate that the drop in tourist arrivals can range between 30.8% and 76.3% and will persist at least until June 2021.The novel coronavirus (COVID-19) exposed individuals to a great uncertainty about its health and economic ramifications, especially in the early days and weeks of the outbreak. This study documents oil and gasoline market implications of individuals' behavior upon such uncertainty by analyzing the relationship between Google search queries related to COVID-19-information search that reflects one's level of concern about the subject (risk perception)-and the performance of oil and gasoline markets during the pandemic. The empirical analysis based on daily data and a structural vector autoregressive model reveals that a unit increase in the popularity of COVID-19 related global search queries, after controlling for COVID-19 cases, results in 0.083% and 0.104% of a cumulative decline in Dow Jones US Oil & Gas Total index and New York Harbor Conventional Gasoline Regular spot price, respectively, after one day, 0.189% and 0.234% of a cumulative decline after one week, and 0.191% and 0.237% of a cumulative decline after two weeks. The reaction of Brent and West Texas Intermediate crude oil prices to the spike in COVID-19 related online searches is found to be statistically insignificant, which can be explained by oil price pass-through into gasoline spot price.Based on the supply of stock market returns hypothesis, we argue that the unprecedented adverse shock of COVID-19 on the countries' economic growth translates into a negative shock to the stock markets. According to the institutional theory, we also argue that the impact of COVID-19 in emerging countries is different from developed countries. Based on the overreaction hypothesis, we expect that the market reaction during the stabilizing period of COVID-19 spread is different from the market reaction during the infection period. Using high-frequency daily data across 53 emerging and 23 developed countries from January 14 to August 20, 2020, we find that COVID-19 cases and deaths adversely affect stock returns and increase volatility and trading volume. Cases and deaths affected stock returns and volatility in the emerging markets, while only cases of COVID-19 affected stock returns, volatility, and trading volume in the developed markets. COVID-19 cases and deaths are related to returns, volatility, and trading volume for emerging countries during the rising infection of COVID-19 (pre-April 2020), while cases and mortality rates are related to returns, volatility, and trading volume in developed countries during the stabilizing spread (post-April 2020). Therefore, the emerging markets' investors seem to react to COVID-19 cases and mortality rates differently from those in the developed markets across two different periods of COVID-19 infection.The ongoing COVID-19 outbreak has revealed vulnerabilities in global healthcare responses. Research in epidemiology has focused on understanding the effects of countries' responses on COVID-19 spread. While a growing body of research has focused on understanding the role of macro-level factors on responses to COVID-19, we have a limited understanding of what drives countries' responses to COVID-19. We lean on organizational learning theory and the extant literature on rare events to propose that governance structure, investment in healthcare infrastructure, and learning from past pandemics influence a country's response regarding reactive and proactive strategies. With data collected from various sources and using an empirical methodology, we find that centralized governance positively affects reactive strategies, while healthcare infrastructure and learning from past pandemics positively influence proactive and reactive strategies. This research contributes to the literature on learning, pandemics, and rare events.

This study sought to validate a real-world speech task designed to assess attention and interpretation bias in an integrated and ecologically valid manner.

Thirty adolescent girls gave a speech in front of an emotionally ambiguous judge and a positive judge while wearing mobile eye tracking glasses to assess how long they looked at each judge (i.e., attention bias). They also reported their interpretations of the ambiguous judge and distress associated with the task (i.e., interpretation bias).

These task-based measures correlated with self-report of interpretation bias and mother-report of attentional control, demonstrating convergent validity. They did not correlate with frustration or high intensity pleasure, indicating discriminant validity. Task-based measures of interpretation bias also showed predictive and incremental validity in relation to child distress during the speech.

This proof-of-concept study demonstrates the initial validity of a novel task designed to assess attention and interpretation bias as they manifest in real-world social interactions.

This proof-of-concept study demonstrates the initial validity of a novel task designed to assess attention and interpretation bias as they manifest in real-world social interactions.

Despite increasing interest in positive psychological states, we know little about how regulatory responses to positive (savoring) compared to negative events (e.g. acceptance, cognitive reappraisal) influence emotional functioning. Savoring may be particularly helpful for athletes who are often trained to attend more to negative (e.g. rectifying weaknesses) compared to positive stimuli (e.g. enjoying progress).

Sixty-seven college athletes completed a two-week daily diary study. Using multi-level modeling, we first explored whether various regulatory responses to daily negative events predicted unique variance in daily emotions (i.e. happy, content, grateful, sad, angry, annoyed). selleck products Next, we tested whether savoring positive events strengthened the association between event intensity and positive daily emotions. Finally, we tested whether regulatory responses to positive compared to negative events had stronger moderating (buffering) effects on the association between daily negative event intensity and daily emotions.