Previous studies have extensively investigated the effects of online word-of-mouth (eWOM) factors such as volume and valence on product sales. However, studies of the effect of eWOM factors on product prices are lacking. It is necessary to examine how various eWOM factors can either explain or affect product prices. The objective of this study is to suggest explanatory and predictive analytics using a regression analysis and ensemble-based machine learning methods for eWOM factors and hotels booking prices. This study utilizes publicly available data from a hotel booking site to build a sample of eWOM factors. The final study sample was comprised of 927 hotels. The important eWOM factors found to affect hotel prices are the review depth and the review rating, which are moderated by a number of reviews to affect prices. The effect of the number of positive words is moderated by the review helpfulness to affect the price. The review depth and rating, along with the number of reviews, should be considered in the design of hotel services, as these provide the rationale for adjusting the prices of various aspects of hotel services. Furthermore, the comparison results when applying various ensemble-based machine learning methods to predict prices using eWOM factors based on a 46-fold cross-validation partition method indicated that ensemble methods (bagging and boosting) based on decision trees outperformed ensemble methods based on k-nearest neighbor methods and neural networks. This shows that bagging and boosting methods are effective ways to improve the prediction performance outcomes when using decision trees. The explanatory and predictive analytics using eWOM factors for hotel booking prices offers a better understanding in terms of how the accommodation prices of hotel services can be explained and predicted by eWOM factors.
Citation: Sangjae Lee, Joon Yeon Choeh. Exploring the influence of online word-of-mouth on hotel booking prices: insights from regression and ensemble-based machine learning methods[J]. Data Science in Finance and Economics, 2024, 4(1): 65-82. doi: 10.3934/DSFE.2024003
Previous studies have extensively investigated the effects of online word-of-mouth (eWOM) factors such as volume and valence on product sales. However, studies of the effect of eWOM factors on product prices are lacking. It is necessary to examine how various eWOM factors can either explain or affect product prices. The objective of this study is to suggest explanatory and predictive analytics using a regression analysis and ensemble-based machine learning methods for eWOM factors and hotels booking prices. This study utilizes publicly available data from a hotel booking site to build a sample of eWOM factors. The final study sample was comprised of 927 hotels. The important eWOM factors found to affect hotel prices are the review depth and the review rating, which are moderated by a number of reviews to affect prices. The effect of the number of positive words is moderated by the review helpfulness to affect the price. The review depth and rating, along with the number of reviews, should be considered in the design of hotel services, as these provide the rationale for adjusting the prices of various aspects of hotel services. Furthermore, the comparison results when applying various ensemble-based machine learning methods to predict prices using eWOM factors based on a 46-fold cross-validation partition method indicated that ensemble methods (bagging and boosting) based on decision trees outperformed ensemble methods based on k-nearest neighbor methods and neural networks. This shows that bagging and boosting methods are effective ways to improve the prediction performance outcomes when using decision trees. The explanatory and predictive analytics using eWOM factors for hotel booking prices offers a better understanding in terms of how the accommodation prices of hotel services can be explained and predicted by eWOM factors.
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Sangjae Lee, Joon Yeon Choeh. Exploring the influence of online word-of-mouth on hotel booking prices: insights from regression and ensemble-based machine learning methods[J]. Data Science in Finance and Economics, 2024, 4(1): 65-82. doi: 10.3934/DSFE.2024003
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