Strukturbaum
| Publikation |
| Autoren des Beitrags | |||
| Titel des Beitrags | Google Trends data for analysing tourists online search behaviour and improving demand forecasting: the case of Åre, Sweden | ||
| Titel veröffentlicht in ... | Information Technology & Tourism | ||
| Erscheinungsjahr | 2019 | Seite (von - bis) | 45-62 |
| ISBN | 1098-3058 | Publikations-Art | Peer-Reviewed Article |
| Band/Jahrgang/Volume | 21 | Serie/Bezeichnung | 1 |
| URL : | https://doi.org/10.1007/s40558-018-0129-4 | ||
| Abstract : | Accurate forecasting of tourism demand is of utmost relevance for the success of
tourism businesses. This paper presents a novel approach that extends autoregressive forecasting models by considering travellers web search behaviour as additional input for predicting tourist arrivals. More precisely, the study presents a method with the capacity to identify relevant search terms and time lags (i.e. time difference between web search activities and tourist arrivals), and to aggregate these time series into an overall web search index with maximal forecasting power on tourism arrivals. The proposed approach enables a thorough analysis of temporal relationships between search terms and tourist arrivals, thus, identifying patterns that reflect online planning behaviour of travellers before visiting a destination. The study is conducted at the leading Swedish mountain destination, Åre, using arrival data and Google web search data for the period 20052012. Findings demonstrate the ability of the proposed approach to outperform traditional autoregressive approaches, by increasing the predictive power in forecasting tourism demand. |
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| Einrichtungen |
Fakultät Elektrotechnik und Informatik
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| Beteiligte Personen |
Höpken
, Wolfram
Prof.
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| Projekte |
Wissenschaftliche Veröffentlichungen |
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