A household-based online cooked meal delivery demand generation model
基于随机效用最大化原理，利用加拿大多伦多地区数据，构建了外卖熟食需求生成模型，分析影响一周内家庭外卖使用频率的因素，并评估消费者剩余，为商家和监管者提供决策支持。
Online cooked meal deliveries (CMD) have become prevalent with the advancement of on-demand delivery services offered by vendors such as Uber Eats and DoorDash. Thus, the development of a CMD demand generation model holds significant importance for CMD vendors, consumers, and policymakers. The model serves as a strategic tool for CMD vendors to address consumer needs. At the same time, it also holds substantial relevance for policymakers seeking to understand CMD demand and formulate effective regulatory measures for CMD operations. This paper presents such a modelling framework. The model is developed under the behavioural principle of random utility maximization (RUM) and explicitly represents various socioeconomic characteristics in the CMD demand generation process. The model is estimated using a Greater Toronto Area, Canada dataset. The empirical model provides insights into the factors influencing week-long CMD usage. The model also offers assessments for households’ consumer surplus brought by CMD, which can inform public policies through well-fare analysis.