Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting: the case of Åre, Sweden

Wolfram Höpken (), Tobias Eberle, Matthias Fuchs and Maria Lexhagen
Additional contact information
Wolfram Höpken: University of Applied Sciences Ravensburg-Weingarten
Tobias Eberle: University of Applied Sciences Ravensburg-Weingarten
Matthias Fuchs: Mid-Sweden University
Maria Lexhagen: Mid-Sweden University

Information Technology & Tourism, 2019, vol. 21, issue 1, No 5, 45-62

Abstract: 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 2005–2012. Findings demonstrate the ability of the proposed approach to outperform traditional autoregressive approaches, by increasing the predictive power in forecasting tourism demand.

Keywords: Google Trends data; Search word analysis; Online search pattern; Tourist arrival prediction; Autoregressive time series forecasting; Big data (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s40558-018-0129-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infott:v:21:y:2019:i:1:d:10.1007_s40558-018-0129-4

Ordering information: This journal article can be ordered from
http://www.springer. ... ystems/journal/40558

DOI: 10.1007/s40558-018-0129-4

Access Statistics for this article

Information Technology & Tourism is currently edited by Zheng Xiang

More articles in Information Technology & Tourism from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().