Abstract
AbstractIn extraordinary situations, like the Covid-19 pandemic, irregular demand fluctuations can hardly be predicted by traditional forecasting approaches. Even the current extent of decline of demand is typically unknown since tourism statistics are only available with a time delay. This study presents an approach to benefit from user generated content (UGC) in form of online reviews from TripAdvisor as input to estimate current tourism demand in near real-time. The approach builds on an additive time series component model and linear regression to estimate tourist arrivals. Results indicate that the proposed approach outperforms a traditional seasonal naïve forecasting approach when applied to a period of extraordinary demand fluctuations caused by a crisis, like Covid-19. The approach further enables a real-time monitoring of tourism demand and the benchmarking of tourism business in times of extraordinary demand fluctuations.
Publisher
Springer Nature Switzerland
Reference30 articles.
1. Athanasopoulos, G., Song, H., Sun, J.A.: Bagging in tourism demand modelling and forecasting. J. Travel Res. 57(1), 52–68 (2018)
2. Box, G.E., Jenkins, G.M.: Time Series Analysis, Forecasting and Control. Holden Day, San Francisco, CA (1970)
3. Chen, J.L., Gang, L., Wu, D., Shen, S.: Forecasting seasonal tourism demand using a multi series structural time series method. J. Travel Res. 58, 92–103 (2019)
4. Cho, V.: A comparison of three different approaches of tourist arrival forecasting. Tour. Manage. 24, 323–330 (2003)
5. Dedeoğlu, B.B., Taheri, B., Okumus, F., Gannon, M.: Understanding the importance that consumers attach to social media sharing: Scale development and validation. Tour. Manage. 76, 103954 (2020)
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献