Mobile Location-based Recommendation Systems and Local Businesses: Evidence from the Restaurant Industry

We investigate the impact of mobile location-based recommendation systems (MLBRS) on the performance of local businesses. Leveraging a comprehensive dataset of 16,720 restaurants and quasi-experimental variation generated by the official launch of a leading MLBRS, we employ a 2-Stage Least Squares instrumental-variable framework to estimate the effect of algorithmic exposure on consumer engagement. Our analysis reveals that a 1% increase in exposure is associated with a 0.078% increase in clicks; separately, a 1% increase in clicks is associated with a 0.053% increase in visit intentions. Importantly, the estimated exposure effect is larger for geographically disadvantaged restaurants, but primarily at the discovery stage (clicks). This pattern highlights a boundary condition of MLBRS: algorithmic visibility can aid, but it does not by itself resolve the uncertainty involved in acting on a recommendation. Downstream conversion is stronger when restaurants provide richer digital information and reputation signals, including consumer reviews and owner-curated information such as photos and menus. Auxiliary credit-card sales data from a restricted Seoul subsample provide consistent evidence of a positive association between algorithmic exposure and revenue, suggesting that MLBRS can shape the connection of online recommendations to offline economic outcomes.