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Forecasting tourism demand in Amsterdam with Google Trends: A research into the forecasting potential of Google Trends for tourism demand in Amsterdam

Rödel, E.L. (2017) Forecasting tourism demand in Amsterdam with Google Trends: A research into the forecasting potential of Google Trends for tourism demand in Amsterdam.

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Abstract:The tourism industry is still growing worldwide and is now responsible for 9 % of the Dutch domestic product, the tourism industry is contributing to the economic growth. Since tourism demand modelling and forecasting has attracted much attention from researchers and progress had been made in this area. This study focusses on the forecasting value of Google Trends for tourism demand by overnight stays in hotels in Amsterdam. The literature indicates that Google Trends has some value for forecasting tourism from which the extent will be measured in this study. The customer journey theory was used to subtract search query terms in a deductive way and the other way around tourist statistics were linked to Google Trends with the use of Google Correlate. The researcher found that data provided by Google Trends can be useful for forecasting night passes in hotels in Amsterdam if the fitting keywords are used. The extent to which the explorative research indicates usefulness is moderate with an average adjusted r-square of 37.4 %. The conclusion for the data driven phase of this research is that there are many correlating search queries, but only very view with possible predictive value or even Google Trends output. Therefore, it was concluded that the data generated from Google Trends in the inductive research has very low to zero usefulness for forecasting night passes in hotels in Amsterdam.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:76 recreation, leisure, 85 business administration, organizational science
Programme:Business Administration MSc (60644)
Link to this item:http://purl.utwente.nl/essays/73929
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