Forecasting tourism demand with helpful online reviews
Abstract
Purpose
Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.
Design/methodology/approach
The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.
Findings
The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.
Originality/value
First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.
Keywords
Acknowledgements
This work was supported by Guanghua Talent Project of Southwestern University of Finance and Economics and received grants from the National Natural Science Foundation of China (No. 71701167) and Humanities and Social Science Projects of the Ministry of Education of China (No. 17YJC630078).
Declaration of competing interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Author contributions: Zhixue Liao, Conceptualization, Formal analysis, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review and editing.
Xinyu Gou, Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review and editing.
Qiang Wei, Data curation, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing.
Zhibin Xing, Data curation, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing.
Citation
Liao, Z., Gou, X., Wei, Q. and Xing, Z. (2024), "Forecasting tourism demand with helpful online reviews", Nankai Business Review International, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/NBRI-10-2023-0097
Publisher
:Emerald Publishing Limited
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