Search results

The purpose of this chapter is to evaluate and discuss the factors that underpin tourists' perceptions of anti-COVID-19 practices in tourist accommodations and destinations, as well as in tourists' experience, satisfaction and behaviour, in order to make recommendations for managers at hotels and tourist destinations.

A survey at tourist accommodations in the Canary Islands was conducted to collect data, and 431 questionnaires were part of the final analysis. The data were analysed with the SPSS27 statistical processing software. Various multivariate techniques such as correlation analysis and factor analysis were used to analyse the data.

The results reveal that anti-COVID-19 practices at the accommodation are the most important for tourists to feel safe in their visit to the destination. The most important factors in the tourists' experience during the pandemic are those related to communication and behaviour. Satisfaction after COVID-19 is perceived higher if places or items for cleaning and disinfestation are provided to tourists and rooms are naturally ventilated, while intentions to visit after COVID-19 are highest to visit local destinations.

The results of the research will support scholars and practitioners to recognise the importance of factors that influence anti-COVID-19 practices, tourists' experience, satisfaction and behaviour during the pandemic. In addition, the research offers policy recommendations for maximum benefits for the tourism industry in the Canary Islands and others tourist destinations.

The chapter was an original primary and exploratory study that surveyed tourists to determine their perceptions of anti-COVID-19 practices, experience, satisfaction and behaviour during the pandemic. The results reveal the most important factors in the construction of the analysed variables, and may be used as a starting point for further hospitality marketing research.

Purpose: This chapter analyzes how the COVID-19 pandemic has been managed and strategies put in place to rejuvenate the tourism industry in Uganda.

Methodology: This chapter adopts an integrated approach involving questionnaire survey and rapid situational analysis of documents to synthesize information on management of past disease outbreaks, COVID-19 and their impacts on tourism.

Findings: Uganda's tourism industry is vulnerable and has been exposed to past disease outbreaks such as Ebola and Marburg with varying effects on its image and performance. With the outbreak of COVID-19, the industry has drawn lessons from the previous responses and management experiences to cope with the effects of the pandemic.

Research Limitations: The main limitation in this chapter is the low response rate due to stringent lockdown conditions and limited access to respondents and official documents.

Practical Implications: This chapter recommends the need to mainstream strategies for crisis management into the tourism policy and development planning frameworks.

Originality/Value: This chapter provides a robust approach to analyze tourism industry's response, recovery and sustainability after disease outbreaks, pandemics and related crises in future.

The concept of risk perception in tourism has long been studied and debated, being commonly approached as a multidimensional construct. Physical risk is one of the most common dimensions of perceived risk, covering numerous tourists' concerns. Given the extent of the items included in the physical risk dimension, the study aims to overview this topic, analysing different aggregated risks, presenting a schematic representation based on an extensive literature review that a posteriori was categorized into three groups: human-made, mixed and natural. The aim was to introduce a broader and more precise understanding of the physical risks in tourism.

Businesses have a vast interaction and communication network within economic, sectoral, legal, and cyber systems. This business network shapes their relationships with other businesses which are their stakeholders. Advances and innovations in the digital world are utilized as a tool of conflict for excessive and unfair competition, market penetration, internationalization, sustainability, or having a strategic edge over rivals. Concepts popularized by the pandemic such as virtualization, virtual markets, social media, virtual advertising, and other cyber/digital factors have accelerated and intensified competition between businesses. Within this process, businesses experience intense conflicts stemming from such competition. This would impact businesses' strategies aimed at conflict in the postpandemic period. After the pandemic, businesses can implement strategies of cooperation, show of force, compromise, avoidance, and problem-solving in conflicts. Businesses can also adopt increasing performance and productivity, resolving problems as quickly as possible, developing mutual relationships with other businesses and establishing an environment of trust as principles in a conflict.

Though some disaster risk reduction and management (DRRM) abstractions and core concepts may appear transparently obvious to some readers, others might not easily grasp the complexities embedded in them. This chapter focusses on the main arguments connected to DRRM. It unravels some of the complexities that abound in the framing of key disaster risk reduction concepts in literature. This chapter is divided into three parts. The first part focusses on understanding the dynamics of disasters. This part revisits definitions of disasters in literature, how they have been conceptualised in academia and what makes them different from other related concepts such as hazards, crisis, vulnerabilities and emergencies. Furthermore, considering that some impacts of disasters are more obvious than others, it examines some of the less conspicuous relationships between disasters and other phenomena. The second part examines the concept of DRRM in existing literature, highlighting the importance of resilience in DRRM and revisiting key methodological approaches in building resilience among communities. The third part places the concept of DRRM within the African context. It demonstrates the delicate aspects embedded in successful DRRM in Africa amid institutional development and policy issues. This part concludes with the identification of key knowledge gaps in DRRM in Africa. These knowledge gaps identified in the wider literature are used to justify why the chapters in this book and the context covered (sub-Saharan Africa) are of utmost importance in DRRM.

This research aims to explore the potential of big data technology for sustainable management and investigate its impact on tourism. Its goal is to obtain meaningful results related to sustainable tourism to understand better how big data technology plays a role in decision-making by looking at it through the lens of various studies.

A systematic review, which is a qualitative method, was used in this study. The analysis was conducted using secondary data from the Web of Science Core Collections databases.

Big data technology has many economic benefits for businesses, but it also has managerial benefits such as forecasting, decision-making and tracking human and machine behaviour. Furthermore, big data technology offers sustainability benefits such as resource efficiency, preventive quality systems, carbon reduction and environmentally friendly production.

Big data's capabilities enable businesses to make more informed business decisions, improve overall business performance and contribute to achieving various SDGs. Big data, which aids in developing smart and sustainable tourism in the tourism sector, assists tourism managers in making economically, socially and environmentally sound decisions.

With the rising of “religious fever” in China rural area, the authors inquire the reason why it happened. First, the authors explore the group characteristics which could affect both happiness and the religion belief of Chinese rural elderly. The authors analyze the micro-data of “thousand village surveys” data of Shanghai University of Finance and Economics by using Order Logit and Propensity Score Matching (PSM) method. These results show that when the elderly people have the following features related to health such as: feeling psychological loneliness, not obtaining the good management of chronic disease in the village, and not being participated in new rural cooperative medical system are more likely to believe in religious in the rural areas. And the authors also find these Chinese rural elderlies who believe in religion are less happy than atheism elderly actually (by PSM). Believing in religion is not the solution and maybe the way these elderly resorts to when they encounter health problem.

Preventing and tackling bullying effectively are important agenda for schools to safeguard all children’s well-being, engagement and sense of belongingness. Children perceived to be different from their peers tend to have a higher risk of being bullied at school, in particular, children with disabilities. It can be challenging for teachers to stop bullying that targets children with disabilities. This chapter considers bullying as a barrier to ensuring inclusive and quality education for everyone. It draws on findings from an ethnographic study concerning the status of inclusion of children identified as having learning difficulties in mainstream schools in China, by listening to what children and teachers have to say (Wang, 2016). The study found that the child participants were subject to forms of bullying. They found it useful to gain support from others when bullying happened, and they showed empathy towards peers’ well-being. The teacher participants reflected on the dilemmas and challenges of dealing with bullying and were keen to share experiences about what they found helpful in addressing the issue. The chapter discusses how insights about bullying learned from children and teachers can be used to inform the enactment of inclusive pedagogy. It is concluded that an inclusive pedagogical response that recognizes every child’s voice is necessary for tackling bullying and co-creating an inclusive environment.

This chapter investigates the behavior of Reddit’s news subreddit users and the relationship between their sentiment on exchange rates. Using graphical models and natural language processing, hidden online communities among Reddit users are discovered. The data set used in this project is a mixture of text and categorical data from Reddit’s news subreddit. These data include the titles of the news pages, as well as a few user characteristics, in addition to users’ comments. This data set is an excellent resource to study user reaction to news since their comments are directly linked to the webpage contents. The model considered in this chapter is a hierarchical mixture model which is a generative model that detects overlapping networks using the sentiment from the user generated content. The advantage of this model is that the communities (or groups) are assumed to follow a Chinese restaurant process, and therefore it can automatically detect and cluster the communities. The hidden variables and the hyperparameters for this model are obtained using Gibbs sampling.

With the rapid development of China’s urbanisation and market economy, municipal solid waste (MSW) generation is increasing dramatically. In response to the threat of environmental pollution and the potential value of converting waste into energy, both the government and the public are now paying more attention to MSW treatment and disposal methods. In 2014, 178.6 million tonnes of MSW was collected at a safe treatment rate of 84.8%. However, the treatment methods and the composition of MSW are influenced by the collection area, its gross domestic product, population, rainfall and living conditions. This chapter analysed the MSW composition properties of Lhasa, Tibet, compared with other cities, such as Beijing, Guangzhou and so forth. The research showed that the moisture content of MSW in Lhasa approaches 31%, which is much lower than the other cities mentioned previously. The proportion of paper and plastics (rubbers) collected was 25.67% and 19.1%, respectively. This was 1.00–3.17 times and 0.75–2.44 times more than those found in Beijing and Guangzhou, respectively. Non-combustibles can reach up to 22.5%, which was 4.03–9.11 times that of Beijing and Guangzhou, respectively. The net heating values could reach up to 6,616 kilojoule/kilogram. The food residue was only half the proportion found in other cities. Moreover, the disposal method applied in each city has also been studied and compared.