Forecasting tourism demand with helpful online reviews

• Published in: Nankai business review international Vol. 15; no. 4; pp. 703 - 726
• Main Authors: Liao, Zhixue, Gou, Xinyu, Wei, Qiang, Xing, Zhibin
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Publishing Limited 29.10.2024; Emerald Group Publishing Limited
• Subjects: Accuracy; Artificial intelligence; Big Data; Consumers; Decision making; Econometrics; Forecasting; Helping behavior; Internet resources; Memory; Methods; Neural networks; Predictive analytics; Reliability; Robustness; Search engines; Short term memory; Social networks; Time series; Tourism; Tourism development; User generated content; Hong Kong China; China; Helpful online reviews; Tourism demand forecasting; Helpfulness analysis
• ISSN: 2040-8749; 2040-8757; 2040-8757
• DOI: 10.1108/NBRI-10-2023-0097

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.