![]() However, predicting future tourism demand is a difficult and non-trivial task, due to the lack of historical data, seasonal fluctuations, influences of unexpected events, the variety of input factors and the complexity of visitors’ travel decision-making process (Song et al. 2017).Īccordingly, in the travel and tourism domain, the accuracy of demand forecasts can hardly be overestimated for businesses and policy makers, likewise (Frechtling 2002). Furthermore, predictions of tourist arrivals help governments in shaping medium and long-term strategies for local and regional tourism development and planning (Fuchs et al. ![]() Hence, knowledge about long-term trends, imminent changes and short-term intra-period fluctuations of customer demand is essential for tourism management in planning resource capacities. More precisely, for tourism businesses it is pivotal to respond promptly to upcoming demand, thus, making limited resources available and ready for co-creative service production processes (Fitzsimmons and Fitzsimmons 2001 Grönroos 2008 Chekalina et al. the fact that services ‘perish’ in case of non-use), accurate forecasts of tourism demand are of utmost relevance (Frechtling 2002). Due to the perishable nature of tourism services (i.e. However, the success of tourism-related businesses, such as airlines or hotels, largely depends on the capacity to accurately predict tourism demand. On a global scale, nearly every tenth job relates directly or indirectly to the travel and tourism industry (WTTC 2016). With a worldwide turnover of more than 7 trillion US dollars in 2015 and a total share of around a tenth of global GDP, travel and tourism significantly contributes to the global economy. Findings demonstrate the ability of the proposed approach to outperform traditional autoregressive approaches, by increasing the predictive power in forecasting tourism demand. The study is conducted at the leading Swedish mountain destination, Åre, using arrival data and Google web search data for the period 2005–2012. 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. 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. ![]() More precisely, the study presents a method with the capacity to identify relevant search terms and time lags (i.e. This paper presents a novel approach that extends autoregressive forecasting models by considering travellers’ web search behaviour as additional input for predicting tourist arrivals. Accurate forecasting of tourism demand is of utmost relevance for the success of tourism businesses. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |