Courteous or crude? Managing user conduct to improve on-demand service platform performance

Forthcoming at Management Science

53 Pages Posted: 27 Nov 2018 Last revised: 26 Apr 2022

See all articles by Yunke Mai

Yunke Mai

University of Kentucky - Gatton College of Business and Economics

Bin Hu

University of Texas at Dallas - Department of Information Systems & Operations Management

Saša Pekeč

Fuqua School of Business, Duke University

Date Written: December 15, 2018

Abstract

In this paper, we study how an on-demand service platform could improve its performance through managing user conduct. In such a platform, service providers may reject certain platform-proposed service requests, and their responses in turn incentivize users to adjust their conduct. We develop an evolutionary game theory model of user conduct and provider responses that shows that the platform could improve user conduct through either setting a low wage for service providers or implementing priority matching. Building upon these results, we further model providers and users joining and leaving the platform by once again utilizing the evolutionary game theory approach. We find that wage setting alone is a blunt instrument to improve platform performance via managing user conduct, whereas supplementing the wage decision with priority matching could overcome its limitations and serve as an effective strategy to further improve platform performance in terms of growth and profitability. This finding suggests that matching prioritization could be an important strategy for managing platforms with user and provider heterogeneities. In addition, our analysis and results also demonstrate the potential of the evolutionary game theory approach for analyzing the impact of pricing and matching decisions on the performance of large markets.

Keywords: on-demand service platform, evolutionary game theory, priority matching

Suggested Citation

Mai, Yunke and Hu, Bin and Pekeč, Saša, Courteous or crude? Managing user conduct to improve on-demand service platform performance (December 15, 2018). Forthcoming at Management Science, Available at SSRN: https://ssrn.com/abstract=3263680 or http://dx.doi.org/10.2139/ssrn.3263680

Yunke Mai (Contact Author)

University of Kentucky - Gatton College of Business and Economics ( email )

550 South Limestone
Lexington, KY 40506
United States

Bin Hu

University of Texas at Dallas - Department of Information Systems & Operations Management ( email )

P.O. Box 830688
Richardson, TX 75083-0688
United States

Saša Pekeč

Fuqua School of Business, Duke University ( email )

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