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The COVID-19 pandemic has resulted in significant changes in tourists’ attitudes and behaviors mostly as a result of confinement-related problems. Although various studies have been conducted to analyze customers’ perceptions of service quality and satisfaction using a drop-off/pick-up method, the influence of COVID-19 on customers’ perceptions of service quality and satisfaction has not been examined using online reviews. It is critical to evaluate satisfaction aspects from user-generated content to ascertain their preferences for hotel services during the pandemic. This research aims to explore the viewpoint shared online by hotel tourists, as well as identify which service practice is associated with higher and lower satisfaction during the COVID-19 pandemic.

This paper aims to understand Chinese tourists’ changing shopping experience in Macao. In scrutinizing reviews posted in the pre-COVID and during COVID eras, the study has identified changing patterns in Chinese tourists’ shopping experiences, including increased leisure components while shopping, decreased luxury pursuits and an improved overall leisure and shopping experience because of decreased prices in accommodation and a less crowded retail and leisure environment. An emergent opportunity to provide “retail-tainment” experience is discussed.

The present study aims to synthesize and conceptualize, through a systematic literature review (SLR), the current state of gamification knowledge in the tourism and hospitality (T&H) sector, providing a roadmap for future research recommendations for service research and practice.

The research is based on a systematic literature review and adopts a systematic quantitative approach to summarize existing evidence on gamification usage in the T&H sector, focusing on relevant service literature on gamification. The authors analyze 36 papers published between 2011 and 2019.

The authors synthesize existing knowledge into five themes describing gamification's role in T&H (Edutainment, Sustainable behavior, Engagement factors, Service provider-generated content and User-generated reviews). Then, a cross-analysis of the five themes reveals the pivotal elements (affordances, behavioral and psychological outcomes, and benefits) generated by gamification mechanics in T&H, simultaneously highlighting potential implications and relevant insights for service literature. The review identifies critical issues affecting gamification research and provides a future research agenda, considering opportunities for T&H and service research.

The study provides the first SLR investigating gamification in T&H. The findings present potential implications and relevant insights for T&H contributing to the construction of a more holistic understanding of gamification adoption in service research.

Studying tourist behavior provides the privilege of diverging in the competition between destinations to attract tourists. Previous studies have explored the effects of tourist behavior on brand satisfaction, attachment and loyalty. However, there is a lack of studies that investigated the intuition behind shaping tourist behavior. To address this issue, this study aims to contribute to providing a viewpoint to apply locus of control (LOC) theory in the tourism context. LOC studies how individuals believe in controlling their feelings and judgment toward events.

Tourism may have important positive and negative economic, socio-cultural and environmental impacts. However, cultural and natural resources are also the base to the development of competitive destinations and changes in these resources can have an important impact on tourism development. Despite the considerable literature regarding the impacts of tourism, a limited number of studies examine the impact of the environment on tourism, specifically the impact of air quality (AQ). Therefore, this paper aims to review what is known about the impact of AQ on tourism demand, analysing the different methods and approaches used, as well as the results obtained.

A systematic literature review method was used to examine the state of the art in this topic and identify research gaps and new research directions. Only 26 papers were identified that examine the impact of AQ on tourism demand.

The majority of the studies were carried out in China and investigate the impact of AQ on tourism from the perspective of tourism demand. Both global (tourism demand) and individual (tourist perceptions) approaches have been used to investigate the impact of AQ on tourism.

This is the first systematic literature review on the impact of outdoor AQ on tourism demand. Moreover, this paper analyses the methods and approaches that have been used in the literature to examine the impact of outdoor AQ on tourism demand. The paper ends with a discussion on the identified research gaps concerning the influence of AQ on tourism development.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

The paper examines the role of stakeholders in tourism and hospitality industry to ensure sustainability. This study focuses on investigating how stakeholders view can influence business sustainability in the tourism industry in Ghana and Africa. The study also looks at the future of sustainable tourism activities in Ghana and Africa.

A qualitative approach was adopted and interviewees were purposively selected. Using the semi-structured interview 12 people were interviewed and NVivo used to analyse the data. The study was carried out in Accra the capital city of Ghana. Using the stakeholder based theory and resource based theory (RBT) the study addresses how sustainable competitive advantage can be developed.

Results shows how stakeholders involved such employees, government, community/society, private sector and individual shop owners perceive factors that enhance or limit progress in tourism. Some challenges they encounter include high level of illiteracy, lack of education for upcoming youth, low funding and publicity, lack of the enforcement of laws in the sector. The findings also indicated that to ensure sustainability in the industry the products should be unique, valuable, rare, non-substitutable and non-imitable and has to be advertised.

Researchers encountered difficulties in eliciting answers from stakeholders who are experts and managers in the industry because of their busy schedules. The researcher admits that this research work which is carried out only in Ghana cannot be used to generalise an assumption for the entire industry sectors in Africa and beyond. The sample size could be improved and the study could be conducted in other African countries for the purposes of comparison.

This study reveals how stakeholders view developments in the tourism sector and this can be used as a guide in developing policies and marketing strategies in the tourism industry. Both theoretical and managerial implications are discussed.

Very little literature has been written on stakeholders' view on business sustainability in the tourism industry in Ghana. Moreover the use of stakeholder and resource based view theories application in developing competitive advantage in the developing country perspective is unique. The findings in no small way will somehow benefit the government and industry stakeholders by providing the latest views and perspectives in Ghana.

This study aims to demonstrate the application of computer-aided text analysis (CATA) software in identifying primary associations and impressions of a specified tourist destination.

The Leximancer software is applied on primary information to analyze the concepts evoked by a destination. Because no specific planning has been done for destination image marketing strategies for rural tourism in Andalusia, this study visualizes and determines clusters of the main attributes associated with this destination.

The analysis identifies the main clusters among associations and impressions of the destination that can be useful in developing strategies.

Only a target segment is studied, with a relatively small sample size.

Leximancer can not only be applied to online user-generated content, but primary information can also be mapped to generate a holistic destination image. Furthermore, identification of the relevant attributes and impressions can serve to identify unique assets to help tourism organizations develop a destination.

Several implications concerning destination marketing are outlined.

Although previous studies have applied Leximancer and other CATA software, the present research uses a new approach. Deriving the primary information on destination image using an unstructured methodology, the concepts evoked by a destination are mapped. Because there is a lack of research on rural tourism in Andalusia and its destination image, its associated attributes are studied.

本论文展示如何使用CATA分析软件来确定一个具体旅游目的地的主要关系和印象。

Leximancer软件主要用于分析一个目的地引发的相关概念。因为安达卢西亚至今未有确定的乡村旅游目的地形象营销策略, 本论文视觉化和决定与此目的地相关的主要因素群。

研究结果指出了此安达卢西亚目的地的主要相关群和印象群, 这些对于指定战略计划有很大帮助。

本论文只研究了单一市场群, 分析样本量较小。

Leximancer不仅可以用来分析用户生成内容, 还可以分析主要信息, 以展示整体旅游目的地形象。此外, 分析指出的相关因素和印象群可以用来确立独特的资源组合, 帮助旅游机构开发旅游目的地。

本论文结构指出了多个旅游目的地营销的相关启示

尽管文献中有关Leximancer和其他CATA软件的使用文章, 本论文创立了新的使用方法。基于对旅游目的地形象的主要信息进行非结构性研究, 本论文对于旅游目的地的概念进行展开论述。由于至今未有针对安达卢西亚的乡村旅游研究以及旅游目的地形象研究, 本研究论述了其相关因素。

关键词 安达卢西亚、旅游目的地形象、旅游目的地因素、CATA软件、Leximancer

The purpose of this study is to examine the drivers and barriers of travel behavior associated with tourist behavior during/post-COVID-19 pandemic to provide a knowledge base as well as an agenda for future research.

The authors utilized the Systematic Literature Review (SLR) technique for searching the articles published in Web of Science (WOS) and Scopus, to identify the main drivers and barriers affecting the tourist behavior during/post-COVID-19 pandemic. Eventually, 47 articles were chosen for the final analysis.

The findings are reported in three sections, (1) quantitative research profile, (2) qualitative synthesis and (3) future agenda. This study addresses the nuanced questions regarding the significant change in tourist travel behavior, emotional dynamics and a detailed understanding of mechanisms, such as which drivers and barriers affect tourist behavior in a particular destination. Drivers and barriers to tourist travel behavior were characterized in personal-related, destination-related, and health-related factors. Moreover, this study provides thought-provoking ideas in theory, policy and practice in the field of tourism and hospitality.

This study has three limitations, as follows. First, the authors searched only two databases, Scopus and Web of Science, due to which the authors might be missing some related studies existing on the other databases. Although these databases provide an extensive range of academic literature, further studies could extend the data collection from the other databases (e.g. via Taylor & Francis). However, our systematic literature review (SLR) coverage is quite extensive, since journals are listed on these three main databases. Second, the authors followed a main study search protocol based on the synonyms and related keywords, however, some of the studies that may be related to the tourist behavior towards the destination are missing on account of the lack of our keywords in there, title, author, keyword and abstract. Furthermore, future research could endeavor to add other keywords to expand the results of studies. Third, although the accurate analysis was conducted to reduce subjectivity in identifying themes for drivers and barriers of tourist behavior, future studies on categorization could work to ensure that other sub-themes categorize.

The recent study has some key practical implications. First, this study is valuable for all the stakeholders in a unique way, including destination managers, academicians and policymakers, because it provides insight into barriers and drivers that influence the development of tourist behavior towards the destination. Second, the current study also offers practical implications for people involved in tourism service industries including governments and private businesses. Policymakers and other leaders are increasingly interested in harnessing the economic potential of tourism. Therefore, identifying the barrier which is inhibiting the tourist traffic towards the destination is beneficial to understand and effectively develop strategies to minimize the effect of such factors. Moreover, drivers and barriers of tourist behavior towards the destination in the COVID-19 pandemic situation towards the destination may help to create a framework for the development of destinations according to the current vulnerable situation. Third, current findings suggest that tourism marketers understand the drivers and barrier constructs found in this study and tailor their marketing strategies for attracting existing and new tourists. For instance, marketers should understand the drivers and barriers of tourist behavior for effective strategy development to increase the positive effect of drivers and to reduce the negative effect of barriers.

This is the first systematic literature review on the impact of drivers and barriers of tourist travel behavior. This paper analyses the methods and approaches that have been used in the previous literature to examine the drivers and barriers of tourist travel behavior. The paper ends with the research implication and limitations of the studies.

As the tourism industry becomes more vital for the success of many economies around the world, the importance of technology in tourism grows daily. Alongside increasing tourism importance and popularity, the amount of significant data grows, too. On daily basis, millions of people write their opinions, suggestions and views about accommodation, services, and much more on various websites. Well-processed and filtered data can provide a lot of useful information that can be used for making tourists' experiences much better and help us decide when selecting a hotel or a restaurant. Thus, the purpose of this study is to explore machine and deep learning models for predicting sentiment and rating from tourist reviews.

This paper used machine learning models such as Naïve Bayes, support vector machines (SVM), convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for extracting sentiment and ratings from tourist reviews. These models were trained to classify reviews into positive, negative, or neutral sentiment, and into one to five grades or stars. Data used for training the models were gathered from TripAdvisor, the world's largest travel platform. The models based on multinomial Naïve Bayes (MNB) and SVM were trained using the term frequency-inverse document frequency (TF-IDF) for word representations while deep learning models were trained using global vectors (GloVe) for word representation. The results from testing these models are presented, compared and discussed.

The performance of machine and learning models achieved high accuracy in predicting positive, negative, or neutral sentiments and ratings from tourist reviews. The optimal model architecture for both classification tasks was a deep learning model based on BiLSTM. The study’s results confirmed that deep learning models are more efficient and accurate than machine learning algorithms.

The proposed models allow for forecasting the number of tourist arrivals and expenditure, gaining insights into the tourists' profiles, improving overall customer experience, and upgrading marketing strategies. Different service sectors can use the implemented models to get insights into customer satisfaction with the products and services as well as to predict the opinions given a particular context.

This study developed and compared different machine learning models for classifying customer reviews as positive, negative, or neutral, as well as predicting ratings with one to five stars based on a TripAdvisor hotel reviews dataset that contains 20,491 unique hotel reviews.