【公开学术报告】Please Don’t Make Me Wait! Influence of Customers’ Waiting Preference and No-Show Behavior on Appointment Systems

Guest Speaker:      Rujie ZHANG (SMU)

Date & Time:         14:00-15:30 (Beijing Time), Fri. 2nd Dec. 2022

Zoom Meeting:      840 078 01213（Password: 819409）

Click the Link:       https://us02web.zoom.us/j/84007801213

Job Market Paper 1: Please Don’t Make Me Wait! Influence of Customers’ Waiting Preference and No-Show Behavior on Appointment Systems

Appointment systems are widely adopted in many service organizations. The simplest and most common format is the Equally-Spaced (ES) system, in which the inter-appointment times between consecutive arrivals are equal. One major drawback of such a system is the long expected waiting time for later arrivals, which makes later appointment positions unappealing to customers. As a result, customers who take these positions are more likely to abandon their appointments, leading to a higher no-show rate. To address this issue, we examine a novel Equal-Waiting (EW) scheduling system under which the expected waiting times are equal across appointments. Through a series of controlled lab experiments, we estab[1]lish that the EW system increases the attractiveness of later appointments and that customers who are willing to take these appointments are more likely to show up. We then incorporate this individual-level preference and no-show behavior into models to evaluate its impact on the system-level performance. We find that, compared with the traditional ES system, the EW system can significantly increase customers’ show-up rate and improve system utilization.

Keywords: appointment systems; no-show; waiting time; behavioral experiment

Job Market Paper 2: Flash Sales versus Traditional sales: Price Optimization for an Online Retailer with Pre-planned Logistics

This paper examines a new flash-sale model where consumers first pay the deposit and then wait several days to make the final payment. The deposit determines the discount strength that the customer can enjoy due to the Double Deposit Inflation and provides a signal to the retailer on potential demands, allowing the retailer to reduce the logistical cost incurred from bottlenecked demand surges. We propose a pricing optimization model and jointly decide the optimal deposit and the product’s full price. We identify the value of introducing the flash-sale channel for the retailer and the conditions under which the value can be realized. We also provide the optimal flash-sale duration. In addition, our findings indicate the importance of considering the production cost in the optimal pricing strategy, especially for the linear demand function. In the case study, we calibrated our model with real data from an e-commerce company in China, and the results from a 5-fold cross-validation show that our model can predict demand well. Besides, by applying the pricing strategy proposed in this paper, we can dramatically improve the profit.

Keywords: flash sales; waiting time; joint pricing; data-driven