The Impact of Weather on Shopping Behavior: An Exploratory Study of Vending Machines on College Campuses

Weather conditions significantly influence consumer shopping behavior, including purchases in unmanned retail settings. This study investigates how weather conditions (such as sunny, cloudy, and rainy days), temperature, and air pollution affect consumption demand through vending machines—a channel well-suited for isolating the pure effect of environmental factors due to the absence of sales personnel. Using a dataset of 157,928 transaction records from 54 vending machines across nine college campuses, we employ multiple panel data models to analyze granular sales data alongside real-time meteorological and air pollution indicators, while controlling for heteroskedasticity and autocorrelation. The results indicate that cloudy and overcast weather increase purchases, whereas poor air quality reduces them. Temperature exhibits an inverted U-shaped relationship with consumption demand, peaking at moderate levels before declining. These findings are consistent with the “environmental stimuli–adaptation scope–retail performance” framework grounded in adaptation level theory, illustrating how environmental stimuli shape purchase decisions. By examining transaction-level behavioral adjustments, this study contributes to the theoretical understanding of weather impacts on unmanned retail performance.