Influential factors for online food delivery platform drivers’ order acceptance

Purpose The delivery drivers this paper surveyed generally intend to accept all orders to avoid missing out on potential earnings. However, uncertainty about the timing of future orders and variability in their potential earnings raises a crucial question: Would it be more beneficial for drivers to decline orders that are likely to involve low earnings or a long idle time after delivery? If so, how can they make informed choices when selecting orders? This paper aims to explore the key factors that can support drivers in making these decisions. Design/methodology/approach The role of order cancelation in Taiwan’s Uber Eats delivery process was first highlighted. This paper followed the grounded theory methodology and collected the data of completed orders from delivery drivers and authors’ participation in deliveries. The data included variables representing order characteristics, such as departure neighborhood, destination and duration for completing the order. Regression methods were then used to identify the variables that affect the driver’s evaluation of a received order in terms of the order’s earnings and idle time after completing the order and determine whether the order is desirable to the driver. Findings Upon receiving an order, drivers can decide whether to accept it by evaluating the earnings they will make for completing the order. The earnings are likely higher if the pay rate multiplier is higher that day or the order departure neighborhood is in a hot zone. After arriving at the restaurant, drivers can again make this decision by estimating the idle time they will spend waiting for their next order after delivering the current order. This idle time is likely longer if the driver is expected to spend greater time fulfilling the order since receipt, or if the earnings for completing the order are greater. This idle time is likely shorter if there are more restaurants in the order destination neighborhood, or if the order departure neighborhood or the order destination neighborhood is in a hot zone. Orders can be categorized as good or poor and the key factors for this categorization are whether the order departure and destination neighborhoods are in a hot zone, and the waiting time at the restaurant. Originality/value While food delivery research has commonly focused on customer satisfaction and platform efficiency, less attention has been paid to the strategic decision-making of delivery drivers – a relatively underrepresented group in the gig economy. This study aims to help these drivers become more adept participants in the competitive landscape of food delivery platforms. By examining how Taiwanese drivers navigate platform structures to maximize profitability and improve their work experience, this research contributes valuable insights to discussions on the sustainability of gig work.