Real‐time demands, restaurant density, and delivery reliability: An empirical analysis of on‐demand meal delivery

Abstract A surge in technological advancements and innovations has spurred the rise of on‐demand meal delivery platforms. Despite their widespread appeal, these platforms face two critical challenges (i.e., order batching and demand allocation) in effectively managing the delivery process while maintaining reliability. In response, this study aims to address these two challenges by examining the effects of real‐time demands and restaurant density on delivery reliability, as well as how the type of driver (i.e., in‐house versus crowdsourced drivers) moderates these effects. We evaluated our model with a unique dataset obtained from one of the top three on‐demand meal delivery platforms in China, and our research sheds light on several key findings. Specifically, our study finds inverted U‐shaped relationships between real‐time demands and delivery reliability and a positive relationship between restaurant density and delivery reliability. In addition, it reveals that crowdsourced drivers perform better than in‐house drivers under high real‐time demands. This study contributes to the literature by clarifying how delivery reliability can be influenced by real‐time demands and restaurant density. The results offer important implications for on‐demand meal delivery platforms to improve delivery performance and allocate demands amid complicated market conditions.