ABSTRACT
When people make travel decisions, they consult their imagination, considering how they would feel in the respective travel situation. Both, researchers who examine this phenomenon and practitioners executing it, commonly hold the vague assumption of an evaluative cognitive process that enables tourists to factor such information into their decision-making process. The nature and functioning of such a process is largely unknown. The authors suggest that travelers, often subconsciously, mentally simulate future hotel stays and predict future feelings to inform their decision-making, a process referred to as affective forecasting. Executing an experimental design, the authors show that actively engaging in episodic future thinking to trigger affective forecasting increases travelers' intentions toward holiday accommodations. This effect is mediated by hotel trust and risk perception, demonstrating that affective forecasting is an effective way for regaining tourists' trust and reducing their perceived risk during a pandemic. Contributions to theory and practical implications are discussed.
ABSTRACT
How does the suffering of a whole industry influence people's attitudes toward that industry? This research is the first, across disciplines, to examine this question. The authors provide the first conceptual study and empirical test for the phenomenon called tourism solidarity. Based on seminal social psychology research, tourism solidarity is conceptualized and defined as an individual's compassion with and support of an industry, resulting from an observation of suffering. The authors use a covariance-based structural equation model as well as a novel Bayesian estimation approach (i.e., non-parametric) to develop a reliable and easy-to-apply tourism solidarity scale and assess its role of solidarity in two consecutive empirical studies. By doing so, the authors are able to empirically demonstrate the importance of tourism solidarity for tourist behavior, and provide both tourism researchers and practitioners with a conceptual model and measurement tool to assess, quantify and actively manage solidarity toward the tourism industry. © The Author(s) 2023.
ABSTRACT
In this paper, we study the long memory behavior of the hourly cryptocurrency returns during the COVID-19 pandemic period. Initially, we apply different tests against the spurious long memory, with the results indicating the presence of true long memory for most cryptocurrencies. Yet, using the multivariate test, the series are found to be contaminated by level shifts or smooth trends. Then, we adopt the wavelet-based multivariate long memory approach suggested by Achard and Gannaz (2016) to model their long memory connectivity. The findings indicate a change in persistence for all series during the sample period. The fractal connectivity clustering indicates a similarity among Ethereum (ETH) and Litecoin (LTC), Monero (XMR), Bitcoin (BTC), and EOC token (EOS), while Stellar (XLM) is clustered away from the remaining series, indicating the absence of any interdependence with other crypto returns. Overall, shocks arising from COVID-19 crisis have led to changes in long-run correlation structure. © 2022 Elsevier B.V.
ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic forced many countries into lockdowns to limit the spread of infection. Israel's containment measures included school closures, mobility restrictions, and workforce reductions. Our study evaluated the effect of COVID-19 on the occurrence and patterns of burn injuries. The study data was obtained via retrospective chart review of burn patients treated between March 15, 2020 and April 30, 2020, namely the period of strict national lockdown. This data was compared against data from paralleling periods between 2017-2019. A total of 686 patients were treated for burn injuries in the two study periods. Age group analysis revealed an increased ratio of pediatric patients aged 0-3 years during the lockdown (55.91% vs 40.79%, p=0.002). In contrast, there were fewer patients presenting with burn injuries in the 7-16 and 17-29 age groups (9.66% vs 3.15%, p=0.017; 16.46% vs 7.09%, p=0.007, respectively). During both study periods, scald injuries were the most common burn etiology and burn injuries occurred most often at home. This predominance was further pronounced during the lockdown (71.65% vs 58.68%, p=0.007; 90.55% vs 74.60%, p=0.0001, respectively). The lockdown period underlined the danger faced by pediatric patients in their household environment. This danger was possibly compounded by an improper level of adult supervision as parents transitioned to remote work. These findings can educate us about factors that render burn injuries more likely not only during lockdowns, but also during regular times, thus shaping the development of burn prevention practices.
ABSTRACT
In this paper, we study the long memory behavior of Bitcoin, Litecoin, Ethereum, Ripple, Monero, and Dash with a focus on the COVID-19 period. Initially, we apply a time-varying Lifting method to estimate the Hurst exponent for each cryptocurrency. Then we test for a change in persistence over time. To model the multivariate con-nectivity, the wavelet-based multivariate long memory approach proposed by Achard and Gannaz (2016) is implemented. Our results indicate a change in the long-range dependence for the majority of cryptocurrencies, with a noticeable downward trend in persistence after the 2017 bubble and then a dramatic drop after the outbreak of COVID-19. The drop in persistence after COVID-19 is further illustrated by the Fractal connectivity matrix obtained from the Wavelet long-memory model. Our findings provide important implications regarding the evolution of market efficiency in the cryptocurrency market and the associated fractal structure and dy-namics of the crypto prices over time
ABSTRACT
Emicizumab (Hemlibra™) is approved for prophylaxis of hemophilia A (HA) patients. The HAVEN studies addressed bleeding reduction in emicizumab-treated patients, but real-world data on bleeding patterns during emicizumab therapy are lacking. We aimed to compare the occurrence of breakthrough bleeding at different time points, starting from emicizumab initiation. This longitudinal prospective observational cohort study included HA patients (n = 70, aged 1 month to 74.9 years) that completed at least 18 months of follow-up in our center. We analyzed the number of spontaneous and traumatic bleeds during selected time points of the study ("bleeding periods"). The percentage of traumatic and spontaneous bleeding episodes was not significantly different among "bleeding periods" (P = 0.053 and P = 0.092, respectively). Most trauma-related treated bleeds resulted from either hemarthrosis (53%) or head trauma (33%). Spontaneous bleeding episodes were mostly hemarthroses (80%). Potential associations of the patients' age, annualized bleeding rate before emicizumab treatment, and the presence of inhibitors with spontaneous bleed occurrence were analyzed with binomial logistic regression. The odds of bleeding while on emicizumab increased by a factor of 1.029 (P = 0.034) for every one year of age. Conclusions: Our real-world data revealed that the risk of bleeding persists, especially in older patients, despite therapy with emicizumab. These data may help clinicians in counselling their patients and in planning their management.
ABSTRACT
The paper examines the impact of Economic Policy Uncertainty (EPU) on the dynamic connectedness among the precious metals before and over the COVID-19 pandemic period, using the Quantile-VAR method. This approach allows us to capture the left and right tails of the distributions of the precious metals returns corresponding to spillover effects under different market conditions: the bear, normal, and bull market states among these assets. We find that the total spillover index (TCI) varies across quantiles and increases widely during extreme market conditions, with a noticeable influence of the recent COVID-19 pandemic. Then, studying the impact of the economic uncertainty on the connectedness among the four precious metals, we find that gold still plays a dominant “safe-haven” asset in hedging market uncertainty, with other precious metals showing heterogeneous responses to the presence of the COVID-19 pandemic. Moreover, we argue that the COVID-19 pandemic significantly affects the dynamic connectedness among precious metals and the relationship between economic policy uncertainty and dynamic connectedness. © 2021 Elsevier Ltd
ABSTRACT
With the COVID-19 pandemic reaching a more mature, yet still threatening, stage, the time is ripe to look forward in order to identify the topics and trends that will shape future tourism research and practice. This note sets out to develop an agenda for tourism research post COVID-19. We surveyed several industry and academic experts seeking their opinion on three important questions: What potential future topics are needed to address the impact of COVID-19? What existing research areas/topics will become more relevant? What changes are recommended for data collection? Interpreting and synthesizing the answers yields six focal research avenues that researchers should devote more attention and effort to. For each topic, we present various important research questions. By doing so, this note paves the way and serves as a signpost for countless intriguing future research endeavors that are of high relevance and demanded by the industry.
ABSTRACT
Studies across the social sciences are making increasing use of an evolutionary perspective. Yet, despite its potential, the application of evolutionary psychology in tourism research is scant. Evolutionary psychology is arguably one of the most useful approaches to understanding the effects of the Coronavirus pandemic on the tourist's psyche. This research highlights, explains, and empirically demonstrates the vast untapped potential of this perspective for post-COVID-19 tourism research. The authors develop an Evolutionary Tourism Paradigm, which is based on biological epistemology and theory to address questions in post-COVID-19 tourism research. This paradigm is brought to life through a developed ocean and islands model, and its utility for future research endeavors on the Coronavirus pandemic is empirically demonstrated in two studies.