Measuring digital reputation: a Business Reputation Score (BRS) and its structural and spatial drivers

Abstract

Digital reputation has become a critical asset for place-based service businesses, where customer experiences are generated in physical locations but communicated through digital platforms. However, digital reputation is typically measured using isolated indicators such as average ratings or review volume, overlooking its multidimensional nature. This paper introduces the Business Reputation Score (BRS), a composite indicator integrating perceived quality and market recognition into a theoretically grounded measure of digital reputation. Using the Yelp Open Dataset and an explainable machine learning framework (XGBoost–SHAP), we examine how business characteristics, spatial context, and business category jointly shape business reputation. The results highlight the importance of structural attributes, neighbourhood quality spillovers, and category-specific effects, while demonstrating the value of interpretable machine learning for explaining digital reputation. The findings are based on structured platform data and should be interpreted alongside other reputation-forming mechanisms, including review content and platform governance.