Tourism destination images in terms of the gaps between the projected and perceived images are of great significance in the development of destinations. Additionally, the use of big-data in tourism studies remains under-utilized despite the boom in big-data applications and the increasing number of electronic User Generated Contents (UGC). Aiming to take advantage of tourism UGC to fully understand the destination image gap between official promotion materials and tourist perception of Sanya City in China, this study innovatively employed a big-data analysis technique, Tourism Sentiment Evaluation (TSE) model and proposed a new analysis framework integrating the “cognitive-affective” model with the gap analysis of projected and perceived destination image to explore the destination image gap of Sanya. It is found that Sanya’s perceptive destination image is overall consistent with its official positioning; however, there also exist image gaps between the two groups in terms of the impact of festival events and tourists’ attitude towards core scenic spots amongst others. This study’s findings are discussed in light of their methodological, theoretical, and practical implications for destination positioning, marketing, and management.
Meng, Lingkun; Liu, Yi; Wang, Yuanlei; and Li, Xiaojuan
"A big-data approach for investigating destination image gap in Sanya City: When will the online and the offline goes parted?,"
Regional Sustainability: Vol. 2
, Article 9.
Available at: https://egijournals.researchcommons.org/regional-sustainability/vol2/iss1/9