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The purpose of this study is to investigate the mediating effect of perceived destination image attributes on the inter-relationship between online user-generated content’s information qualities (UGC) and tourists behavior by extending the Mehrabian and Russel’s stimulus-organism-response (SOR) model.

Based on the Malaysian tourism setting, a total of 255 valid responses from foreign inbound tourists were collected. Partial-least square-structural equation modeling (PLS-SEM) was used to test study hypotheses.

The PLS-SEM indirect path analysis confirms that cognitive image mediates the relationship between intrinsic information quality and behavioral intentions. Meanwhile, affective image significantly mediates the contextual information quality influence on behavioral intentions. Interestingly, however, this study found affective image mediated oppositely on the relationship between social information quality and behavioral intentions.

The study provides a better understanding of how destination image impacts the way tourists perceive different information qualities on behavioral intentions, which suggests the ongoing complex nature of these human-technology relationships within the tourism realm.

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).

Lake tourism is a growing academic sub-field of tourism studies with an emerging body of literature. However, little research attention has been given to lake-destinations’ projected or perceived tourism images. Specifically, there has been a scarcity of literature investigating the variables involved in the formation of a lake-destination image. Therefore, this study aims to explore the main attributes that might potentially influence this type of destination, and simultaneously, contribute to conceptualizing and defining lake tourism as recent research area. An explorative study was then conducted in order to generate a set of image variables through the use of textual and photographic data. The results will contribute to characterize potential lake-destinations and to develop a final list of variables specifically related to this type of destination.

The data sample used in this study is composed of 2,638 Chinese tourists who have travel experiences to the South Pacific region. This study examines the effects of memorable tourism experiences, destination cognitive and affective images, and satisfaction on revisit intention and their mechanisms from a cognitive–affective perspective. Results show that destination cognitive image, destination affective image, and satisfaction, respectively, play a mediating effect on the relationship between memorable tourism experiences and revisit intention. Memorable tourism experience is the most important predictor of revisit intention, and it mainly affects the cognitive image of a destination. In line with previous studies, this research has shown that memorable tourism experiences have significant impact on the destination image and tourists' revisit intention, which can provide significant implications for tourism practitioners and destination managers in the South Pacific islands.

This study utilised Mehrabian and Russel's stimulus-organism-response (SOR) model to investigate the inter-relationship between perceived social media information qualities (IQ) of online UGC, destination image, perceived travel risk and behavioural intentions.

A total of 255 responses from international tourists were collected. The data collection via an online survey was performed from October 2020 to February 2021. The study model and hypotheses were examined using partial least squares-structural equation modelling (PLS-SEM).

The result indicated that social media intrinsic, representational, and social IQ significantly influenced tourists perceived cognitive image, while only contextual and social IQ significantly predicted the perceived affective image. This study also confirms the significant effect of cognitive image on the affective image. In addition, the authors found that both destination image components, cognitive and affective, significantly affect tourists' behavioural intentions. However, surprisingly, tourists' perceived travel risk did not moderate the effect of destination image components on behavioural intentions.

This study contributes to the body of knowledge by extending Mehrabian and Russel's SOR model in the tourism behaviour context.

From a practical perspective, this study proves that UGC significantly affects destination image and plays an integral part in luring tourists to visit a destination.

Previous research in this area is limited, making this study particularly novel. This study represents one of the initial attempts to evaluate the dimensions of information quality in UGC on social media and online review platforms, particularly within the field of tourism. Treating online travel UGC seriously could assist organisations in leveraging tourist behaviour and enhancing destination image.

The purpose of this study is to investigate the influence of tourists’ emotional experiences on predicting behavioral intentions via cognitive, affective and overall image in yoga tourism.

This study was conducted using data collected from 398 tourists visiting a yoga tourism destination in India. Confirmatory factor analysis and structural equation modeling were used in analyzing the collected data.

The study confirmed that specific tourists’ emotions act as a predictor of cognitive, affective and overall image. This in turn influenced the behavioral intentions of tourists. The effect of specific emotions on affective image was stronger than on cognitive image in yoga tourism.

The marketing campaign of yoga tourism should highlight the special benefits of yoga to activate, stimulate and influence tourists toward yoga tourism, thereby improving the flow of future tourists. It would also help in better positioning and promoting yoga tourism as a unique and distinct niche tourism market.

This study contributed to the literature by understanding the predictive power of specific emotions on behavioral intentions via, cognitive, affective and overall image in yoga tourism. As far as the authors’ knowledge is concerned, this study is first known attempt to investigate such relationships in tourism literature.

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.

Reviews of scientific publications in the area of tourism continuously find commonalities and repeatedly applied criteria in conceptualizing destination image. Much emphasis has been placed on investigating the image components (dimensions) and potential impacts as perceived by the consumer. Publications on the image formation and change, however, do not disclose many details on the process and impacts of change agents. Hence, this study aims to look into the initial stages of destination image planning and how these plans are implemented through projecting onto the official destination websites.

The text-based content analysis builds on a random sample of one-third of the tourism development plans and the respective official tourism websites of sub-provincial prefectures in China. Terms (originally phrased in Chinese language only) were extracted that could be classified as image components considering the context where they appeared.

Results exhibit a sparse application of varied and imaginative image elements in both the tourism development plans and the official websites. Deviations between intended and projected destinations are substantial. An overwhelming majority of prefectural destination management organizations (DMOs) appears to be distant from a professional implementation of an image positioning strategy.

Due to resource limitations, only a random sample of one-third of the 365 sub-provincial prefectures in China could have been screened. To draw a complete, though structurally most probably not very different, picture on the scope and variety of image elements, a complete investigation would be necessary. The projection of image items on the official websites represents a restricted view on possible image formation agents. For a more comprehensive understanding, other information channels (e.g. printed advertising material, travel catalogues and guide books) would complement the perspective on induced image agents.

From a managerial perspective, it appears to be an easy job to position a destination along one or two main appeal characteristics, particularly when the majority of these are factual aspects of geography, landscape, history or culture. Whether such a positioning strategy materializes in view of an increasing competition among destinations is questionable. The condensed image profiles identified in this study can act as blueprints for developing more pronounced positioning profiles. The variation across groups of destination image profiles and the composition of prefectures for each group reflects the potential competitive pressure that prefectures may excel unless the DMO representatives decide to go for an adapted target position.

This study is a rare attempt to analyse the intended/planned and projected image elements of a multitude of tourism destinations simultaneously. Such a comparison is usually done on a case-by-case basis only. Thus, the insights of this study go beyond the limits of an individual destination enabling structural comparisons across neighbouring and nation-wide regions.

The aim of the paper is to develop a conceptual model of the process by means of which the induced tourism image of a destination is created. The model focuses on the role tourism agents' relational networks play in this process and particularly on the effects of the links with external actors – i.e. tour‐operators – on the destination's induced image.

Based on Gartner's definition of image formation agents, it is assumed that there are tourism agents that use the tourism image as a pull factor to influence the buying behaviour of potential visitors. Basically, these agents are: internal actors, located within a particular tourism destination, and external agents – i.e. tour‐operators – which are not normally associated with any particular destination, but have stakes in the travel decision process of potential visitors. In parallel, it is assumed that the tourism destination is a web of relational networks where the agents are connected by means of collaborative links that facilitate the supply of a tourist product or experience to the visitors.

In this paper two potential gaps in the induced tourism destination image formation process are found, and that the position of relevant actors in the network and the structure of the network are two determinant factors of the emergence – or inhibition – of these gaps. It is also suggested that these gaps and the lack of collaborative links among internal and external actors would affect the coherence of the supplied tourist products and the satisfaction with the tourist experience.

The relevance of the paper lies in a new approach to the induced tourism image formation process focusing on the destination's relational network and, in particular, the network of relations with external agents (i.e. tour‐operators).

The purpose of this paper is to evaluate tourist destinations’ image based on the attributes obtained from the extant literature using correspondence analysis.

This study is aimed at measuring and illustrating destination image of tourism destinations perceived by domestic tourists. Five tourism destinations – Ooty, Shimla, Manali, Mussoorie and Mount Abu were selected as the subject of the study. Based on a representative sample of 800 respondents from the surveys across destinations, correspondence analysis was employed to illustrate an attribute based comparative analysis of the destination image of the tourist destinations.

Findings reveal that the attribute natural attraction was positively perceived by respondents across all destinations and the attribute infrastructure emerged as an area in need of dire attention. Further, the most dominant attributes which marked destinations’ positioning were – local cuisine and food outlets, hotels and restaurants, famous handicraft and parking facilities.

The findings of this study need to be integrated with qualitative studies to explore the underlying reasons for the perceived destination image.

These results provide direction to policy makers and practitioners to visualize their destinations’ competitive standing relative to their competitors’ strengths and weaknesses. This tourist-derived intelligence presents an opportunity to take advantage of its current position, or if necessary, optimally reposition itself.

This paper documents research that was the first to systematically capture and comparatively illustrate the destination image of Indian tourist destinations.