Suche nach „[D.] [Buhalis]“ hat 2 Publikationen gefunden
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    MobilEuropan Campus Rottal-Inn


    Katerina Volchek, A. Liu, H. Song, D. Buhalis

    Forecasting tourist arrivals at attractions: Search engine empowered methodologies

    Tourism Economics, vol. 25, no. 3, pp. 425-447


    DOI: 10.1177/1354816618811558

    Abstract anzeigen

    Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro level. The number of visits to five London museums is forecast and the predictive powers of Naïve I, seasonal Naïve, seasonal autoregressive moving average, seasonal autoregressive moving average with explanatory variables, SARMAX-mixed frequency data sampling and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances the forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models’ forecasting accuracy varies for short- and long-term demand predictions. The application of higher frequency search query data allows for the generation of weekly predictions, which are essential for attraction- and destination-level planning.

    MobilEuropan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    Katerina Volchek, H. Song, R. Law, D. Buhalis

    Forecasting London Museum Visitors Using Google Trends Data

    Proceedings of the ENTER2018 eTourism Conference


    Abstract anzeigen

    Information search is an indicator of tourist interest in a specificservice and potential purchase decision. User online search patterns are a well-known toolfor forecasting pre-trip consumerbehaviour, such as hotel demand and international tourist arrivals. However, the potential of search engine data for estimating thedemand for tourist attractions, which is created both before and during a trip, remains underexplored. This research note investigates the relationships between Google search queries for the most popular London museums and actual visits to theseattractions. Preliminary findings indicatehigh correlation between monthly series data. Search query data isexpected togenerate reliable forecasts ofvisits toLondon museums.