Journal
TOURISM MANAGEMENT
Volume 90, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.tourman.2022.104490
Keywords
Tourism demand forecasting; Social media data; Online review; MIDAS; Hong Kong
Funding
- Early Career Scheme from Research Grants Council of Hong Kong Special Administrative Region, China [25500520]
- National Natural Science Foundation of China [71761001]
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This study aims to forecast international tourist arrivals to Hong Kong from seven English-speaking countries. Incorporating tourist-generated online review data into the destination forecasting system, the study presents a new direction in tourism demand modeling and forecasting. The empirical findings indicate that using tourists' online review data can significantly improve the performance of tourism demand models, particularly when high-frequency online review data is included in traditional time-series models.
This study aims to forecast international tourist arrivals to Hong Kong from seven English-speaking countries. A new direction in tourism demand modeling and forecasting is presented by incorporating tourist-generated online review data related to tourist attractions, hotels, and shopping markets into the destination forecasting system. The main empirical findings indicate that tourism demand forecasting based on tourists' online review data can substantially improve the forecasting performance of tourism demand models; specifically, mixed data sampling (MIDAS) models outperformed competing models when high-frequency online review data were included in traditional time-series models.
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