Why Mobile Food Ordering Apps are the Future of Food Delivery
As recent business trends, both OFD and also CC platforms come in their earliest stage. None the less, the results of most recent studies show exciting implications for business research. As an instance, Gupta and Paul (2016) noted that OFD users from Eastern countries such as China or Malaysia concentrate over the chances these businesses offer seeing convenience and time saving as an alternative of their usage cost. In Western nations such as Brazil or England, OFD is also over-growing, providing added comfort to their customers when combined with the comparative ease of access afforded by the ubiquity of mobile Internet devices (Pigatto et al., 20 17 ).
Scholars characterized these platforms within an innovation of restaurants or grocery stores in aspen colorado providers intended to boost their validity (Yeo et al., 2017; Pigatto et al., 2017; Cavusoglu, 2012). This competitiveness may be assessed by customers’ transactions volume and delivery time fulfillment, according to the typical traffic conditions that providers face. From a web mining perspective, it is imperative to point out how to collect these metrics. Both applications provide online visualizations of real traffic conditions for almost any city in the world. Waze has been used in Israel for detecting road safety events (Fire et al., 2012) and in Brazil (Silva et al., 2013) for characterizing traffic alerts at the city scale, and Google maps have been used for similar purposes (Kahle and Wickham, 2013). However, their potential use for OFD platforms remains unknown. Here we provide a procedure that illustrates their potential for new business models that rely on urban mobility to promote the use of OFD platforms.
Materials and method
We developed a procedure for retrieving key performance indicators of 1106 fast-food providers available at a Colombian OFD (https://domicilios.com/bogota). This platform allows providers to receive customer’s orders if they are within a radius of 6 km. By using an advanced web scraper named “Agency” (https://www.agenty.com/), we extracted the following indicators. First, we removed the cost of the delivery, which reflects the amount of money charged for dispatching the food from the provider to the customer. Second, we obtained the expected delivery time, which is the providers’ declared times to send their orders to their customers.
Third we now got the minimum investment; in other words, the minimum charge demanded providers to send their orders to the customer. We also collected the number of comments that clients have registered for each provider. The amount of comments is the most critical indicator of transactions volume. This number, in no way, equals the entire quantity of clients who arranged a service. However, it shows the number of consumers who ordered some left and service a negative or positive comment about it. As such, the range of opinions is fundamentally lower than the number of customers who left a transaction with the food provider, but it’s sensibly informative concerning the customers who care for allowing other customers understand their experience with the food provider, which shows a more conceptual suit with the notion of collaborative consumption.