Background: Along with the rapid development of the Internet, the form of destination marketing is becoming increasingly innovative and diverse. Celebrity endorsement via social media, as an effective marketing strategy, has been widely used by various tourism companies to attract and engage with their customers. Previous studies have investigated the various aspects of such endorsement (e.g. the effect of endorser’s attributes) in different contexts (e.g. Hotel, Restaurant, Airline). However, little research has focused on the influence of the tourists’ parasocial relationship with endorsers on destination marketing. Accordingly, the current study aims to explore the role of parasocial relationships on tourists’ destination attitude and visit intention. Method: This study adopted quantitative method and conducted Partial Least Square-Structural Equation Modelling (PLS-SEM) for data analysis. 498 valid questionnaires were collected from Weibo (One of China’s most popuar social media platforms). Results: Results suggested that endorser’s credibility positively influenced follower’s parasocial relationship (p<0.001), which in turn positively influenced destination attitude and visit intention (p<0.001). Additionally, the parasocial relationship significantly mediated the relationship between credibility and destination attitude as well as visit intention (p<0.001). Conclusion: The emerging trend of celerbity endorsement via live-streaming makes tourism destination marketing more diverse and even effective. Through exploring the underlying mechanism of celebrity endorsement, this study can provide destination marketers with insights about how to build and strengthen the tourist’s attitude and visit intention towards destiantion by developing their parasocial relationships with endorsers. This study also adds to the literature of using Partial Least Square-Structural Equation Modelling (PLS-SEM) in tourism and hospiatlity research.

Improving the wildlife tourism experience of travellers is critical to achieving environmental sustainability. Therefore, it’s critical to investigate quality factors to raise experience quality. This study aims to identify the most salient quality factors and their relative importance to the wildlife tourism experience of travellers. In order to take advantage of big data and unprompted textual data, a machine learning method, namely, supervised latent Dirichlet allocation, was used in analysing online reviews of wildlife tourism. A total of 18 quality factors are extracted from data and cross-validated by comparing a prior theoretical framework. Results reveal that travellers’ perception of intensity, authenticity, species popularity, and operators’ rule management are crucial for enhancing traveller’s experience. In addition, the negative influences of wildlife attack and disease highlight the importance of control of encounter. These findings suggest some future research directions and provide practical implications.

Knowing how to improve skiers’ experiences in ski resorts is vital for developing the ski industry. This study aims to provide a holistic understanding of the key attributes of skiers’ experiences and explore them in the context of seasonality. Based on the user-generated content of 14 ski resorts and the topic modelling and sentiment analysis method, a framework of skiing experience attributes was built. Compared with the seasonal data, the dynamic of skiers’ concerns and perceived performance was revealed. The skiers’ concerns in peak seasons and off seasons manifested different orientations. The results show that the relatively important attributes tend to have relatively low performance in the peak seasons. In off seasons, skiers emphasise non-skiing-oriented attributes. This study showcases that skier’s interests and evaluations of various experience attributes vary with seasons. The findings help to understand the skiers’ peak and supporting experiences, which could be used to build ski resorts management and seasonal hedging strategies.