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1.	Höpken, Wolfram, et al. (författare) Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting : The case of Åre, Sweden 2019 Ingår i: Information Technology & Tourism. - : Springer. - 1098-3058 .- 1943-4294. ; 21:1, s. 45-62 Tidskriftsartikel (refereegranskat)abstract Accurate forecasting of tourism demand is of utmost relevance for the success oftourism businesses. This paper presents a novel approach that extends autoregressiveforecasting models by considering travellers’ web search behaviour as additionalinput for predicting tourist arrivals. More precisely, the study presents a methodwith the capacity to identify relevant search terms and time lags (i.e. time differencebetween web search activities and tourist arrivals), and to aggregate these timeseries into an overall web search index with maximal forecasting power on tourismarrivals. The proposed approach enables a thorough analysis of temporal relationshipsbetween search terms and tourist arrivals, thus, identifying patterns that reflectonline planning behaviour of travellers before visiting a destination. The study isconducted at the leading Swedish mountain destination, Åre, using arrival data andGoogle web search data for the period 2005–2012. Findings demonstrate the abilityof the proposed approach to outperform traditional autoregressive approaches, byincreasing the predictive power in forecasting tourism demand.

Höpken, Wolfram, et al. (författare)
Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting : The case of Åre, Sweden
2019
Ingår i: Information Technology & Tourism. - : Springer. - 1098-3058 .- 1943-4294. ; 21:1, s. 45-62
Tidskriftsartikel (refereegranskat)abstract
- Accurate forecasting of tourism demand is of utmost relevance for the success oftourism businesses. This paper presents a novel approach that extends autoregressiveforecasting models by considering travellers’ web search behaviour as additionalinput for predicting tourist arrivals. More precisely, the study presents a methodwith the capacity to identify relevant search terms and time lags (i.e. time differencebetween web search activities and tourist arrivals), and to aggregate these timeseries into an overall web search index with maximal forecasting power on tourismarrivals. The proposed approach enables a thorough analysis of temporal relationshipsbetween search terms and tourist arrivals, thus, identifying patterns that reflectonline planning behaviour of travellers before visiting a destination. The study isconducted at the leading Swedish mountain destination, Åre, using arrival data andGoogle web search data for the period 2005–2012. Findings demonstrate the abilityof the proposed approach to outperform traditional autoregressive approaches, byincreasing the predictive power in forecasting tourism demand.

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