Abstract
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 4296 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.
۱- Introduction
Collaborative consumption (CC) is a new form of consumer behavior with important implications for business research (Benoit et al., 2017). CC takes place when people coordinate the acquisition and distribution of a resource. This coordination is frequently done for a fee or other non-monetary compensation through trading, bartering, and swapping (Belk, 2014). It is “the peer-to-peer-based activity of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services” (Hamari et al., 2016), p. 2047. According to some scholars, it is not clear the future growth of CC and its impact on incumbent industries (Barnes and Mattsson, 2017). However, as an emerging phenomenon from the computer-mediated interaction between customers and providers, CC is present in a vast range of business, such as transportation (Uber, Zipcar), lodging (Airbnb), tourism (Couchsurfing), entertainment (Spotify) and online food delivery services (Just-Eat.com, Clickdelivery.com, UberEATS) (Pigatto et al., 2017). Online food delivery services (OFD) offer opportunities for research as they are underrepresented in the literature of CC. According to Pigatto et al. (2017) these services can be characterized as business platforms that provide order services, payment and monitoring of the process but are not responsible for the preparation and order delivery operations. Although large fast-food chains like McDonald’s or Domino’s Pizza offer their delivery services, small or medium restaurants chains have seized the emergence of intermediaries that provide these sort of services (Yeo et al., 2017).
Despite the popularity of these platforms nowadays, their connection with CC remains neglected. The words “Online Food Delivery” are missing in most recent papers of CC (Pigatto et al., 2017; Hamari et al., 2016; Benoit et al., 2017; de Rivera et al., 2017), and the publications that aim the study of OFD (Hong et al., 2016; Gupta and Paul, 2016; Yeo et al., 2017) do not mention CC either. This lack of connection does not imply that they are independent and unrelated consumption phenomena. It only means that a relevant theoretical framework that illustrates the relationship between the two remains fragmented and illdefined. Our aim here is to tackle this gap. Our approach posits the relevance of evaluating the impact of traffic conditions on the customer-provider relationship because OFD and CC are intrinsically related to urban transportation. This approach deviates from what we call the “standard methodological approach” that relies on surveys for data collection purposes. Although survey data is often intended to measure quality service through customers’ perception, this perception does not reflect delivery time accurately.
