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This paper examines the effects of online campaigns celebrating frontline workers on COVID-19 outcomes regarding new cases, deaths, and vaccinations, using the United Kingdom as a case study. We implement text and sentiment analysis on Twitter data and feed the result into random regression forests and cointegration analysis. Our combined machine learning and econometric approach shows very weak effects of both the volume and the sentiment of Twitter discussions on new cases, deaths, and vaccinations. On the other hand, established relationships (such as between stringency measures and cases/deaths and between vaccinations and deaths) are confirmed. On the contrary, we find adverse lagged effects from negative sentiment to vaccinations and from new cases to negative sentiment posts. As we assess the knowledge acquired from the COVID-19 crisis, our findings can be used by policy makers, particularly in public health, and prepare for the next pandemic.
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This guest editorial presents the papers in the Special Issue Section of the European Journal of Tourism Research from the 4th International Scientific Conference "TOURMAN 2021" (www.tourman.gr), 21-23.05.2021, entitled "Restarting tourism, travel and hospitality: The day after"
ABSTRACT
Research background: The global financial crisis from 2007 to 2012, the COVID-19 pandemic, and the current war in Ukraine have dramatically increased the risk of consumer bankruptcies worldwide. All three crises negatively impact the financial situation of households due to increased interest rates, inflation rates, volatile exchange rates, and other significant macroeconomic factors. Financial difficulties may arise when the private person is unable to maintain a habitual standard of living. This means that anyone can become financially vulnerable regardless of wealth or education level. Therefore, forecasting consumer bankruptcy risk has received increasing scientific and public attention. Purpose of the article: This study proposes artificial intelligence solutions to address the increased importance of the personal bankruptcy phenomenon and the growing need for reliable forecasting models. The objective of this paper is to develop six models for forecasting personal bankruptcies in Poland and Taiwan with the use of three soft-computing techniques. Methods: Six models were developed to forecast the risk of insolvency: three for Polish households and three for Taiwanese consumers, using fuzzy sets, genetic algorithms, and artificial neural networks. This research relied on four samples. Two were learning samples (one for each country), and two were testing samples, also one for each country separately. Both testing samples contain 500 bankrupt and 500 nonbankrupt households, while each learning sample consists of 100 insolvent and 100 solvent natural persons. Findings & value added: This study presents a solution for effective bankruptcy risk forecasting by implementing both highly effective and usable methods and proposes a new type of ratios that combine the evaluated consumers' financial and demographic characteristics. The usage of such ratios also improves the versatility of the presented models, as they are not denominated in monetary value or strictly in demographic units. This would be limited to use in only one country but can be widely used in other regions of the world.
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Purpose: The current COVID-19 pandemic has created an extremely dynamic and uncertain environment in which businesses find it very difficult to operate, particularly those in the hospitality industry. It is therefore very important to understand which actions hospitality businesses think the private and public sectors should adopt in order to cope with the pandemic and its impact. To facilitate this, this research adopted chaos theory to investigate Italian small and medium enterprises (SMEs) in the hospitality sector. Methods: A mixed method approach, based on a convergent parallel design data validation variant, was adopted. A survey with open and closed questions was developed and sent to a sample of businesses. 1,040 completed questionnaires were collected and analysed through descriptive statistics;in addition to these usable surveys, 361 open-ended answers were analysed thematically. Results: The results showed that Italian entrepreneurs and managers were over-relying on interventions from the public sector and that there was a lack of business actions being made, thus evidencing a deficit in terms of long-term strategic thinking and the innovation required during such turbulent times. Implications: Although these results cannot be generalised to the whole of the hospitality industry, they shed light on important elements that industry associations should take into account.
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[...]applying a segmentation study to a sample of 88 corporate museums in Italy, they identified three different clusters of corporate museums based on their strategic orientation and their actual willingness to cooperate with other local stakeholders to enhance industrial tourism in the area (namely: traditionalist, strategist and individualist) with the biggest group (i.e. the 'strategists') being represented by those museums that are strategically committed to support the company goals, while being open to any networking activity with external tourism stakeholders to further boost the attractiveness of the whole tourism destination where they operate. Luisa Errichiello and Roberto Micera, in their study "A process-based perspective of smart tourism destination governance", combine the relatively recent smart approach with destination governance theory to propose a governance process framework for smart tourism destinations where smartness principles, tools and methods can be applied to increase the sustainable competitiveness of destinations beyond the mere technology dimension, making the role of collaborative structures, user-driven services, social innovation and local community involvement, explicit. [...]not least, we are deeply grateful to the Editor-in-Chief of the European Journal of Tourism Research (Professor Stanislav Ivanov) for giving us the possibility to undertake such a wonderful 'journey'.
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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.