Select, Swipe, and Serve: Examining the Impact of Food-Delivery Platforms on Restaurant Demand Characteristics

Abstract

Problem Definition: Restaurants increasingly depend on food-delivery platforms to cater to consumer demand. To devise an effective platform strategy, restaurants must understand how these platforms change the characteristics of their demand and the factors that moderate this change.
Methodology and Results: Using a unique transaction-level dataset of over 50 million transactions from a quick service restaurant chain comprised of 99 restaurants, we assess the effect of platform dependence on restaurant demand characteristics, focusing on order composition and demand forecast accuracy. Our analysis reveals that a ten-percentage point increase in delivery platform dependence leads to a 10.5% reduction in the proportion of sales revenue from high-margin items and a 2.83% rise in demand forecast error. Moreover, we show that the influence on high-margin sales is moderated by the following customer characteristics: affluence, group size, and order timing.
Managerial Implications: These findings underscore hidden costs associated with platform dependence, extending beyond commission fees to include operational challenges and profit margin reductions. For practitioners, our research prompts restaurants to re-examine their digital strategies, balancing the benefits of platform reach with the need for optimized operational efficiency and profitability. For academics, this study paves the way for further explorations into the dynamic relationship between restaurants and digital platforms, a critical aspect of the modern food service industry.

Keywords: Digital Platforms, Demand Characteristics, Operational Efficiency, Panel Analysis