Chinese inbound tourism growth peaked in 2012 and in following years, arrivals have exhibited a downward trend. Over the same time Chinese outbound tourism has increased significantly and by 2016 the number of Chinese outbound tourists (52.7 million) was nearly twice that of international arrivals to China (28.1 million) (CTA, 2018). The aim of this paper is to identify the determinants of international tourists visiting China based on destination attributes. For the purposes of this research, Australia was selected as a study site on the grounds that China has been a popular destination for Australian residents. This study examines a range of behavioral factors that may affect intentions to travel to China including: past travel experience to China; perceptions of overseas destination attributes; beliefs in China’s ability to satisfy the needs and constraints that appear to prevent Australian residents from traveling to China; and tourists’ intentions to visit or revisit. Data collected from Australian residents on aspects of travel to China included perceptions, beliefs, constraints, information sources, and past experience. The research shows that past experience was positively associated with intention to visit or revisit. Five constraint factors were identified. Based on these findings, the study discusses practical implications for management and government officials needed to boost Chinese inbound tourism.
Rural tourism has become an important force in implementing the rural revitalisation strategy and accelerating rural economic development. The hectic pace of life has made more and more city dwellers yearn for rural life, and travelling in the countryside has become their weekend choice. However, the current level of rural tourism informationization is low, the publicity is insufficient, the tourists’ awareness is low, and the source of customers is seriously insufficient. To this end, this paper proposes a relatively novel multidata source fusion tourism recommendation algorithm, which adopts the idea of tensor orthogonal decomposition and fuses multisource data models to predict the target domain’s for rating. The integrated consideration of multiple data sources under the do-it-yourself approach assists the target domain to discover the target user neighbourhood users more quickly and to discover the user’s interest degree more accurately. It is worth pointing out that the recommendation algorithm proposed in this paper under the fusion of multiple data sources is not necessarily applicable to data sources with weak correlation, such as travel data sources and music data sources, which are relatively weakly correlated, and the algorithm is slightly weak in making predictions of user preferences.
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