Information Design for On-Demand Service Platforms: A Queueing-Theoretic Approach

55 Pages Posted: 25 Jun 2023

See all articles by Donghao Zhu

Donghao Zhu

Technical University of Munich

Stefan Minner

Technische Universität München (TUM) - TUM School of Management

Martin Bichler

Technische Universität München (TUM)

Date Written: June 15, 2023

Abstract

Information design in on-demand service platforms matters in applications such as taxi services, ride-hailing platforms, and freight exchanges. Displayed service delay information significantly affects platform revenues, leading users to balk or renege. Information design is crucial for platforms with dynamic supply and demand; however, the effects of various information policies on user behavior are unclear. User arrival rates are not only influenced by the platform's information policy, but also by the perceived long-term matching probability in a model with multiple platforms. We use queueing theory to examine information disclosure policies for maximizing platform revenue in a marketplace featuring single- and double-sided queueing service systems. In a single-sided model, forming the queue on the side with the higher arrival rate generates higher expected revenue. The preferred information policy depends on the arrival rate and system load. In a double-sided model, hiding the queue-length information is preferred for the side with a lower arrival rate, whereas displaying it on both sides proves advantageous when both sides have high arrival rates. Considering the long-term influence of matching probability on user arrival rates, the recommendations for selecting the information policy remain qualitatively the same, but the revenue difference between information policies increases.

Keywords: sharing economy, information design, queueing system, balking and reneging

Suggested Citation

Zhu, Donghao and Minner, Stefan and Bichler, Martin, Information Design for On-Demand Service Platforms: A Queueing-Theoretic Approach (June 15, 2023). Available at SSRN: https://ssrn.com/abstract=4480537 or http://dx.doi.org/10.2139/ssrn.4480537

Donghao Zhu (Contact Author)

Technical University of Munich ( email )

Arcisstrasse 21
Munchen, Bayern 80333
Germany

HOME PAGE: http://https://donghaozhu.github.io/

Stefan Minner

Technische Universität München (TUM) - TUM School of Management ( email )

Arcisstrasse 21
München, 80333
Germany

HOME PAGE: http://www.log.wi.tum.de

Martin Bichler

Technische Universität München (TUM) ( email )

Arcisstrasse 21
Munich, DE 80333
Germany

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