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Destinations to be able to compete with each other need to equip themselves with as many competitive advantages as possible. Tourists' loyalty to a destination is considered as a prominent competitive tool for destinations. Tourists' loyalty manifests itself in recommendation of the destination to others, repeat visit of the destination and willingness to revisit the destination. Although a plethora of studies have tried to define models to show the relation between loyalty and the antecedent factors leading up to it, few of them have tried to integrate these models with mathematical approaches for better understanding of loyalty behavior. The purpose of this paper is to integrate a tourist destination model with Bayesian Network in order to predict the behaviour of destination loyalty and its antecedent factors.

This paper has developed a probability model by the integration of a destination loyalty model with a Bayesian network (BN) which enables to predict and analyze the behavior of loyalty and its influential factors. To demonstrate the application of this framework, Tehran, the capital of Iran, was chosen as a destination case study.

The outcome of this research will assist in identifying the weak key points in the tourist destination area for giving insights to the marketers, businesses and policy makers for making better decisions related to destination loyalty. In the analysis process, the most influential factors were recognized as the travel environment image, natural/historical attractions and, with a lower degree, infrastructure image which help the decision maker to detect and reinforce the weak factors and put more effort in focusing on improving the necessary parts rather than the irrelevant parts.

The research identified all critical factors that have the most influence on destination loyalty while driving the associate uncertainty which is significant for the tourism industry. This resulted in better decision-making which is used to identify the impact of tourism destination loyalty.

In the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.

The study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.

In the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.

Previous research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

This study initiated an investigation of how the Macau–Zhuhai tourism cooperated and discussed how Macau and Zhuhai could join hands to develop tourism in the region. The study demonstrated an approach for destination marketing organizations to explore online tourist-generated content and to understand tourists' perceptions of the destination image (DI).

A total of 1,291,057 reviews (535,317 for Macau and 755,740 for Zhuhai) were collected, analyzed and examined to determine how the DI s of Macau and Zhuhai changed during the period of 2015–2019 based on tourist-generated content on travel websites (TripAdvisor, Ctrip.com and Qyer.com) through a text-mining approach.

The result revealed that Macau and Zhuhai were in a hybrid of competition and collaboration on tourism DI s. First, Macau and Zhuhai competed in hotel and catering industry. Macau was appealing to international tourists and provided high-end and prestigious offerings; while Zhuhai was impressed by cost-effective accommodation and food. Second, Macau diversified industrial structure with diverse “Tourism, Leisure and Recreation” and “Culture, History and Art” more than Zhuhai did. Meanwhile, Macau should balance the different demands of international and Chinese tourists. Third, complementary potentials were found in natural resources, urbanization technology and tourism innovation and related projects.

The research provides valuable insights for policymakers and industrial managers on their endeavors to develop DIs. Policymakers should be able to develop supportive mechanisms and tourism facilitators to promote industrial collaboration and mutual DIs. Managers could refer to the components in the changing DIs and identify the developmental gaps and cooperation potentials in their targeted areas.

The research fulfills the gap in regional tourism studies on Macau, in which the evaluation on synergetic influence and neighbor effect from Zhuhai has been underexplored. Facilitated by up-to-date data mining techniques, the study contributes to both DI and coopetition literature in tourism marketing; and this should inspire further studies on the antecedences of DI changes, resolutions to the competing interests and DIs of different stakeholders in different forms of strategic cooperation in regional tourism. The employment of DIs is an explicit demonstration of tourists' immersion and values attached to the destination, providing effective cues on the status of coopetition.

The purpose of this paper is to propose an improved optimization method for image matching problem, which is based on multi-scale Gaussian mutation pigeon-inspired optimization (MGMPIO) algorithm, with the objective of accomplishing the complicated image matching quickly.

The hybrid model of multi-scale Gaussian mutation (MGM) mechanism and pigeon-inspired optimization (PIO) algorithm is established for image matching problem. The MGM mechanism is a nonlinear model, which can adjust the position of pigeons by mutation operation. In addition, the variable parameter (VP) mechanism is exploited to adjust the map and compass factor of the original PIO. Low-cost quadrotor, a type of electric multiple rotorcraft, is used as a carrier of binocular camera to obtain the images.

This work improved the PIO algorithm by modifying the search strategy and adding some limits, so that it can have better performance when applied to the image matching problem. Experimental results show that the proposed method demonstrates satisfying performance in convergence speed, robustness and stability.

The proposed MGMPIO algorithm can be easily applied to solve practical problems and accelerate convergence speed of the original PIO, and thus enhancing the speed of matching process, which will considerably increase the effectiveness of algorithm.

A hybrid model of the MGM mechanism and PIO algorithm is proposed for image matching problem. The VP mechanism and low-cost quadrotor is also utilized in image matching problem.

Against the background of the popularity of social media and heritage tourism, this study aims to focus on world heritage sites, proposing a method to examine and compare the digital spatial footprints left by tourists using geographic information systems.

By analyzing user-generated content from social media, this research explores how digital data shapes the destination image of WHS and the spatial relationships between the components of this destination image. Drawing on the cognitive-affective model (CAM), it investigates through an analysis of integrated data with more than 20,000 reviews and 2,000 photos.

The creativity of this research lies in the creation of a comprehensive method that combines text and image analytics with machine learning and GIS to examine spatial relationships within the CAM framework in a visual manner.

The results reveal tourists' perceptions, emotions, and attitudes towards George Town and Malacca in Malaysia, highlighting several key cognitive impressions, such as history, museums, churches, sea, and food, as well as the primary emotions expressed. Their distributions and relationships are also illustrated on maps.

Tourism practitioners, government officials, and residents can gain valuable insights from this study. The proposed methodology provides a valuable reference for future tourism studies and help to achieve a sustainable competitive advantage for other heritage destinations.

This study aims to explore the differences in the tourists’ perceived destination image on travel e-commerce platforms (e.g. Ctrip and Fliggy) and social media platforms (e.g. Xiaohongshu and Weibo).

This paper aims to systematically visualize the structure and trends from 2005 to 2021, which will help scholars gain a deeper appreciation for existing studies and grasp future research possibilities and directions.

The approach is bibliometric, using VOSviewer and CiteSpace to analyze 765 journal articles and reviews from the Web of Science (WoS) and Scopus databases over the past 16 years.

There is considerable interest in urban tourism destination image (U-TDI), partly because of the role of image in promoting the economic development of urban tourism and the associated benefits to stakeholders. Most research output concerns China, the USA, Spain and the United Kingdom (UK); research in the USA context has had a particularly wide range of influence. Highly cited journals play a crucial role, while subject structure, key articles and high-frequency keywords indicate popular topics, sub-themes and development trends. Drawing on these findings, the authors identify four topics that deserve further study.

This systematic review will enhance understanding of U-TDI research and inform future research directions as well as highlighting the need to explore destination image in greater depth, it guides policymakers in the tourism industry seeking to develop city image.

This paper aims to generate knowledge of the impact of different virtual reality (VR) set-ups in tourism promotion regarding destination image, place attachment and behavioural intention.

The paper presents a comparative study of the impact of different visualisation technologies (video, immersive VR and multisensory immersive VR) to promote tourism destinations. The study’s dependent variables are destination image, place attachment and behaviour intention.

Results show that VR content impacts these variables. Multisensory immersive VR is the preferred content type for destination promotion. It is also evidenced that female participants scored each variable higher than male participants. Males reported higher scores on the video set-up for destination image and place attachment. Behavioural intention reported higher values in the video when compared to immersive VR in both sexes.

This paper concludes that there is a preference towards multisensory set-ups, which suggests that incorporating audiovisual and sensory elements can significantly enhance the effectiveness of VR experiences in attracting and engaging potential tourists.

The paper contributes to the scarce body of knowledge regarding the impact of different VR factors on tourism promotion, including the multisensory VR component.

本文旨在探讨不同虚拟现实（VR）设备在旅游推广中对目的地形象、地方依恋和行为意图的影响, 并增加对此方面的认识。

本文对不同可视化技术（视频、沉浸式VR和多感官沉浸式VR）在旅游目的地推广中的影响进行了比较研究。研究的依赖变量包括目的地形象、地方依恋和行为意图。

研究结果表明, VR内容对这些变量有影响。多感官沉浸式VR是目的地推广的首选内容类型。研究还证明, 女性参与者在每个变量上的评分高于男性参与者。男性在视频设备上对目的地形象和地方依恋评分较高。无论是在男性还是女性中, 视频在行为意图方面的得分都高于沉浸式VR。

我们得出结论, 人们更偏好多感官设备, 这表明将视听和感官元素纳入虚拟现实体验可以显著提高吸引和吸引潜在游客的效果。

本文对不同虚拟现实因素对旅游推广的影响, 包括多感官VR组成部分, 做出了有益的贡献。

This study aims to uncover marketing strategies to restore a positive image in times of pandemic crisis to bring tourists back to a popularly affected tourism city in China, Wuhan in Hubei Province. The paper argues that the process of restoring city image requires understanding the perceptions of Generation Z, a segment of the population who have a high propensity to travel after COVID-19.

This study employed a constructivist grounded theory approach to explore the perspectives of China’s Generation Z consumers of Wuhan’s future image to restart tourism. The data is collected through semi-structured interview of 29 respondents and analyzed with the initial, focused and theoretical coding process.

Start by narrowing the gap between projected and perceived image, the findings suggest that Generation Z could be pulled to visit Wuhan city through four core products such as dark tourism activities, special-interest leisure, heroism and storytelling.

The destination image restoration framework after health-related crisis is creatively proposed. It combines the analysis of crisis and urban characteristics from the perspective of the target audience (Generation Z) and provides specific strategies to restore the tourism city image from cognitive, affective and conative dimensions. Significantly, two novel characteristics of Generation Z (i.e. lighthearted and patriotic) emerge in addition to the typical features of this generational cohort. This study also found a high preference for a technologically oriented type of attraction that reverses the morbid COVID-19 memories into an entertainment tool.