The SARS-CoV-2 virus that causes the COVID-19 disease is highly infectious and contagious. The long-term consequences for individuals are as yet unknown, while the long-term effects on the international community will be dramatic. COVID-19 has changed the world forever in every imaginable respect and has impacted heavily on the international travel, tourism demand, and hospitality industry, which is one of the world’s largest employers and is highly sensitive to significant shocks like the COVID-19 pandemic. It is essential to investigate how the industry will recover after COVID-19 and how the industry can be made sustainable in a dramatically changed world. This paper presents a charter for tourism, travel, and hospitality after COVID-19 as a contribution to the industry.

This study applied a shared heterogeneity duration model to tourists’ length of stay at different locations of multidestination trips. This analysis helps to understand tourists’ behaviors and to predict their length of stay according to relevant variables. Such information can be applied to the development of efficient marketing strategies aiming to push the average length of stay to the desired direction, and to develop “on the fly” service provision and revenue management strategies. The focus on multiple destination trips offers an innovative analytical perspective. A large data set of 309,000 visits to Brazilian destinations was analyzed. Several empirical findings regarding determinants of tourists’ length of stay were obtained. Positively skewed distributions for duration and hazard functions were found to best fit observed data. Shared heterogeneity was found to statistically improve the explanatory capacity of duration models when multidestination tourism trips data are analyzed.

Purpose: This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution; Second, this paper elaborates on those estimates’ usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons.Design/methodology/approach: This study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models.Findings: The results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naïve I model).Practical implications: A discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors’ experience and tourism stakeholders’ decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures.Originality/value: High-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week.Plain Language Summary: This research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors’ experience and public and private decision-making.

This paper analyses Chinese tourist arrivals in Thailand during the last 25 years using various quantitative techniques. The number of Chinese tourists in Thailand has been increasing for two decades but since 2009 the trend has been particularly dramatic, with a 56.84% annual growth rate. The study found an increase of seasonality, but arrivals showed the same yearly pattern. Income elasticity indicates that Thailand will continue to benefit from China's economic growth. In addition, Chinese tourists respond less to a change in Thailand's prices than to changes in competitors' prices. In 2017, Chinese arrivals are forecast to reach 7.9 million with an annual growth rate of 19.98% since 2013. Therefore, Thailand should focus on capacity preparation for this market growth while monitoring the policies of its major competitors in the China outbound market.