Predicting restaurant survival using nationwide Google Maps data

Abstract

The restaurant sector is pivotal to firm exit research, which influences economic policy and managerial strategy recommendations. Recent studies using online data are based on geographically limited datasets and have largely omitted temporal dynamics in user interactions. Additionally, these studies rely on manual labeling for text analysis, a resource-intensive approach. Built upon the case of Poland, our study introduces the first comprehensive, nationwide analysis of restaurant survival using Google Maps data. We enhance predictive model performance by incorporating time-sensitive user interactions. Our model controls for established determinants of business exit and proves robust regarding data quality issues associated with user-provided business directories. We apply an efficient, label-free method for extracting semantic content from reviews, thereby creating useful features for firm exit prediction. Furthermore, we present an efficient feature selection strategy using hierarchical agglomerative clustering that retains predictive power while reducing the model complexity. Our model has broad applications ranging from credit scoring to early-warning systems for business closures, while our data collection method opens doors to large-scale firm exit studies in regions where official records are lacking and online sources used in previous studies are less prevalent.