Abstract
Purpose This study aims to present a very recent literature review on tourism demand forecasting based on 50 relevant articles published between 2013 and June 2016. Design/methodology/approach For searching the literature, the 50 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to three main dimensions: the method or technique used for analyzing data; the location of the study; and the covered timeframe. Findings The most widely used modeling technique continues to be time series, confirming a trend identified prior to 2011. Nevertheless, artificial intelligence techniques, and most notably neural networks, are clearly becoming more used in recent years for tourism forecasting. This is a relevant subject for journals related to other social sciences, such as Economics, and also tourism data constitute an excellent source for developing novel modeling techniques. Originality/value The present literature review offers recent insights on tourism forecasting scientific literature, providing evidences on current trends and revealing interesting research gaps.
Highlights
Forecasting tourism demand is a top rated subject for both researchers and practitioners, holding a profound impact on the tourism and hospitality industry
Purpose – This study presents a very recent literature review on tourism demand forecasting, based on fifty relevant articles published between 2013 and June 2016
Such numbers prove that tourism forecasting is not entirely restricted to specific tourism literature, even though tourism gets the largest share, with 44% of articles; instead, the empirical studies found a vaster range of sciences, with a special emphasis on Management, Economics and Technology literature (Table 6)
Summary
Forecasting tourism demand is a top rated subject for both researchers and practitioners, holding a profound impact on the tourism and hospitality industry. Seasonality has been intuitively considered one of the most influencing factors on tourism (Butler, 1998) Such intuitive knowledge has been solidly confirmed by numerous research studies (e.g., Alexandrova & Vladimirov, 2016). Another influencing factor is the knowledge possessed on today’s tourists’: besides the fact that a satisfied and pleased tourist is more likely to repeat its destination visit, the characteristics of the tourists of a specific destination may configure common profiles (Emel et al, 2007); it is necessary to know better today’s tourists for anticipating tomorrow’s tourists’ profiles and flows. Other much lesser common factors that may influence tourism demand include unexpected events, whether in the form of natural disasters or human-driven crises (e.g., economic, political or military) (e.g., Veiga, 2014)
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