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Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting: The case of Åre, Sweden
Universtiy of Applied Sience, Weingarten-Ravensburg, Germany.
Business Informatics Group, University of Applied Sciences Ravensburg-Weingarten, Weingarten, Germany.
Mid Sweden University, Faculty of Human Sciences, Department of Economics, Geography, Law and Tourism. (ETOUR)ORCID iD: 0000-0003-3964-2716
Mid Sweden University, Faculty of Human Sciences, Department of Economics, Geography, Law and Tourism. (ETOUR)ORCID iD: 0000-0002-6610-9303
2019 (English)In: Information Technology & Tourism, ISSN 1098-3058, E-ISSN 1943-4294, Vol. 21, no 1, p. 45-62Article in journal (Refereed) Published
Abstract [en]

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 2005–2012. Findings demonstrate the ability

of the proposed approach to outperform traditional autoregressive approaches, by

increasing the predictive power in forecasting tourism demand.

Place, publisher, year, edition, pages
Springer, 2019. Vol. 21, no 1, p. 45-62
Keywords [en]
Google Trends data, Search word analysis, Online search pattern, Tourist arrival prediction, Autoregressive time series forecasting, Big data
National Category
Economics and Business
Identifiers
URN: urn:nbn:se:miun:diva-35036DOI: 10.1007/s40558-018-0129-4ISI: 000467723600005Scopus ID: 2-s2.0-85064535365OAI: oai:DiVA.org:miun-35036DiVA, id: diva2:1267956
Available from: 2018-12-04 Created: 2018-12-04 Last updated: 2019-07-08Bibliographically approved

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Fuchs, MatthiasLexhagen, Maria

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