Restaurant survival prediction using machine learning : do the variance and sources of customers’ online reviews matter?

Abstract

Restaurant constitutes an essential part of the tourism industry. In times of uncertainty and transition, restaurant survival prediction is vital for deepening organizations' understanding of business performance and facilitating decisions. By tapping into online reviews, a prevalent form of user-generated content, this study identifies review variance as a leading indicator of restaurants’ survival drawing from data on 2838 restaurants in Boston and their corresponding reviews. Machine learning–based survival analysis shows that models integrating fine-grained review variance (i.e., review rating variance, overall review sentiment variance, and fine-grained review sentiment variance) outperform models that do not account for these factors in restaurant survival prediction before and during the pandemic. Furthermore, in most cases, expert reviews hold stronger predictive power for pre-pandemic restaurant survival than non-expert and all forms of reviews. This study contributes to the literature on business survival prediction and guides industry practitioners in monitoring and enhancing their enterprises.