Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/13309
Author(s): | Moro, S. Rita, P. |
Date: | 2016 |
Title: | Forecasting tomorrow’s tourist |
Volume: | 8 |
Number: | 6 |
Pages: | 643 - 653 |
ISSN: | 1755-4217 |
DOI (Digital Object Identifier): | 10.1108/WHATT-09-2016-0046 |
Keywords: | Tourism forecasting Tourism demand Tourists’ behavior Modeling Tourism prediction |
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. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Files in This Item:
File | Description | Size | Format | |
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AntecipatingTomorrowsTourist_Manuscript.pdf | Pré-print | 584,88 kB | Adobe PDF | View/Open |
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