Evaluation of Collaborative Consumption of Food Delivery Services through Web Mining Techniques

Online food delivery services rely on urban transportation to alleviate customers’ burden of traveling in highly dense cities. As new business models, these services exploit user-generated contents to promote collaborative consumption among its members. This study aims to evaluate the impact of traffic conditions (through the use of Google Maps API) on key performance indicators of online food delivery services (through the use of web scraping techniques to retrieve customer’s ratings and the physical location of restaurants as provided by Facebook). From a collection of 19,934 possible routes between the physical location of 787 online providers and 4,296 customers in Bogotá city, we found that traffic conditions exerted no practical effects on transactions volume and delivery time fulfillment, even though early deliveries showed a mild association with the number of comments provided by customers after receiving their orders at home.

Authors: 
Juan C. Correa, Wilmer Garzon, Phillip Brooker, Gopal Sakarkar, Steven Carranza, Leidy Yunado and Alejandro Rincon
Room: 
1
Date: 
Tuesday, September 25, 2018 - 17:45 to 18:00

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