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Search engine traffic as input for predicting tourist arrivals.
Universtiy of Applied Sience, Weingarten-Ravensburg, Germany.
University of Applied Science, Weingarten-Ravensburg.
Mid Sweden University, Faculty of Human Sciences, Department of Tourism Studies and Geography. (ETOUR)ORCID iD: 0000-0003-3964-2716
Mid Sweden University, Faculty of Human Sciences, Department of Tourism Studies and Geography. (ETOUR)ORCID iD: 0000-0002-6610-9303
2018 (English)In: Information and Communication Technologies in Tourism 2018: Proceedings of the International Conference in Jönköping, Sweden, January 24-26, 2018 / [ed] Stangl Brigitte & Pesonen Juho, New York: Springer, 2018, p. 381-393Conference paper, Published paper (Refereed)
Abstract [en]

Due to the perishable nature of tourism services and the limited capacity of tourism firms in serving customers, accurate forecasts of tourism demand are of utmost relevance for the success of tourism businesses. Nowadays, travellers extensively search the web to form expectations and to base their travel decision before visiting a destination. This study 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 corresponding tourist arrivals), and to aggregate these time series into an overall web search index with maximal effect on tourism arrivals. 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, thus, to increase the predictive power in forecasting tourism demand.

Place, publisher, year, edition, pages
New York: Springer, 2018. p. 381-393
Keywords [en]
Tourist arrival prediction, Web search traffic, Google Trends, Data Mining
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:miun:diva-33173DOI: 10.1007/978-3-319-72923-7_29Local ID: ETOURISBN: 978-3-319-72922-0 (print)ISBN: 978-3-319-72923-7 (electronic)OAI: oai:DiVA.org:miun-33173DiVA, id: diva2:1188074
Conference
Information and Communication Technologies in Tourism 2018
Note

Awarded by the 1st place of Best Conference Paper

https://www.miun.se/en/ETOUR/nyheter/nyhetsarkiv/2018-2/enter-best-research-paper-award/

Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2018-08-20Bibliographically approved

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Fuchs, MatthiasLexhagen, Maria

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Citation style
  • apa
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Output format
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  • asciidoc
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