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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Wolfram Höpken & Tobias Eberle & Matthias Fuchs & Maria Lexhagen, 2019. "Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting: the case of Åre, Sweden," Information Technology & Tourism, Springer, vol. 21(1), pages 45-62, March.
    • Handle: RePEc:spr:infott:v:21:y:2019:i:1:d:10.1007_s40558-018-0129-4
    DOI: 10.1007/s40558-018-0129-4
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    References listed on IDEAS

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    Cited by:

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    2. Fernando, Angeline Gautami & Aw, Eugene Cheng-Xi, 2023. "What do consumers want? A methodological framework to identify determinant product attributes from consumers’ online questions," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    3. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
    4. Gyongyver Măduța, 2023. "Use Of Google Trends In Determining Romanian Public Opinion Towards English Language Courses," Romanian Economic Business Review, Romanian-American University, vol. 18(1), pages 41-46, March.
    5. Caetano, Marco Antonio Leonel, 2021. "Political activity in social media induces forest fires in the Brazilian Amazon," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    6. Michael Olumekor & Hossam Haddad & Nidal Mahmoud Al-Ramahi, 2023. "The Relationship between Search Engines and Entrepreneurship Development: A Granger-VECM Approach," Sustainability, MDPI, vol. 15(6), pages 1-16, March.

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