On the Design of Service platform: pricing, delay promise, and information
Prof. Li XIAO
Assistant Professor
Tsinghua-Berkeley Shenzhen Institute (Tsinghua University)
Date: 10 November 2023 (Friday)
Time: 10:00 am to 11:30 am
Venue: E22-G015
Host: Prof. Yan LIN, Assistant Professor in Business Intelligence and Analytics
Online registration: https://umac.au1.qualtrics.com/jfe/form/SV_b8dPKZqsKdzIzBA
Abstract
Service platforms often operate a “virtual queue”. Customers are heterogeneous in their willingness to wait and do not observe their position in the queue. To take advantage of this heterogeneity, the service provider (SP) often provides waiting time information (WTI) to each arriving customer, who then decides whether or not to join the service. One of the commonest practices adopted by many service platforms is to disclose the average waiting time. However, as suggested by empirical studies and anecdotal evidence, this strategy may not be efficient for an SP. This paper considers the joint pricing and information disclosure problem for an SP within the framework of a virtual queue. We first characterize the joint optimal pricing and information disclosing policy, and then examine the impact of information disclosure on the pricing and profit of the SP. We show that adding noise to the WTI disclosure is a powerful instrument to improve the SP’s revenue by as much as 60%, when the service value is low and the congestion level is high. However, the SP has no incentive to add noise when the service value is high and congestion is low. We further show that these managerial insights carry over to general settings such as the number of servers or the service rate can be endogenously determined by the SP. Overall, our findings provide useful guidance to service platforms on the integrated design of pricing, information disclosure, and service configuration.
Speaker
Li XIAO is an assistant professor at the Data Science and Information Technology Center and Tsinghua-Berkeley Shenzhen Institute (Tsinghua University) since 2017. Prior to this, she worked as a post-doc fellow and Research Assistant Professor at CUHK Business School from 2015 to 2017. She obtained her Ph.D. degree in 2015 from the Department of Analytics and Operations at the Business School of National University of Singapore. Her research interests include the application of analytics across various domains (i.e., analytics in supply chain, stochastic service systems, and other data-intensive areas). She has published nine articles in management and analytics related journals (three of them are published in UTD journals), including two articles in Operations Research (UTD, FT50, ABS4*) and one article in Production and Operations Management (UTD, FT50, ABS4). She has served as the Principal Investigator of two grants from National Natural Science Foundation of China. She taught “Decision modeling and analytics” at CUHK Business School and supply chain related courses at Tsinghua.
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