Modeling and Analyzing Online Food Delivery Services Using Design Thinking: An Optimization Approach

Abstract

The COVID-19 pandemic has significantly strained online food delivery services (OFDS) globally. This has challenged OFDS businesses to redesign and deploy technologies to meet customer demand. The purpose of this article is to identify the optimal factors contributing to customer experience with OFDS services during a black swan event such as the COVID-19 pandemic. We followed a four-step research design to identify the optimal factors for OFDS. First, we identified the major episodes in the OFDS process. Second, these episodes were evaluated by customers using the sequential incidence technique. Third, we used an orthogonal design to analyze the episodes at different levels based on customer preferences. Finally, we used the Taguchi approach to calculate the signal-to-noise ratios and identify the optimal factors and their preferred levels. We classify the optimal factors into customer-oriented and service-provider-oriented propositions. The option to select the delivery person and delivery conditions was found to be the most optimal customer-oriented attribute. We discuss the theoretical and managerial implications of the study and suggest major avenues for digital transformations in OFDS for better customer experience.