Designing Demand Information Disclosure in the Presence of Capacity Constraints: A Large-Scale Randomized Field Experiment on a Matching Platform

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See all articles by Ni Huang

Ni Huang

Arizona State University (ASU) - W.P. Carey School of Business

Yumei He

Tsinghua University

Xingchen Xu

Tsinghua University

Yili Hong

Arizona State University (ASU) - W.P. Carey School of Business

Date Written: December 25, 2019

Abstract

Digital platforms provide value-added services to individuals in the realm of romance and dating. A common issue in online dating platforms is that users over-pursue popular prospective dating partners, which leads to low matching rates, particularly for users who are not popular on the platform. Our study seeks design solutions for online dating platforms to effectively address this problem. Specifically, we examine whether and how the disclosure of user demand information increases the matching rate by flattening the highly-skewed long-tail demand distribution. The theoretical tension is that on one hand, demand information is traditionally considered to be a signal for quality, as it enables observational learning, which in turn would exacerbate the demand distribution imbalance; on the other hand, demand information may signal available capacity, which helps balance the distribution. We report a large-scale randomized field experiment on Summer, a large mobile dating platform in China, to evaluate these two theoretical effects by comparing the efficacies of alternative demand information disclosure designs. Our results show that the design of demand information disclosure significantly affects the users’ pursuit of dating partners and their subsequent matching outcomes. In particular, compared to plainly disclosing demand information, the design of demand information in tandem with a capacity cue substantially increases the users’ matching requests for low-demand partners, decrease the users’ pursuit of high-demand partners, and results in a higher number of successful matches. Our study provides theoretical implications for the literature at the intersection of dating platform design and observational learning; it also provides actionable insights for matching platform operators regarding optimally disclosing demand information to users.

Keywords: online dating, market design, digital platform, observational learning, information disclosure, capacity constraint

Suggested Citation

Huang, Ni and He, Yumei and Xu, Xingchen and Hong, Yili, Designing Demand Information Disclosure in the Presence of Capacity Constraints: A Large-Scale Randomized Field Experiment on a Matching Platform (December 25, 2019). Available at SSRN: https://ssrn.com/abstract=

Ni Huang

Arizona State University (ASU) - W.P. Carey School of Business ( email )

Tempe, AZ 85287-3706
United States

Yumei He

Tsinghua University ( email )

Beijing, 100084
China

Xingchen Xu

Tsinghua University ( email )

Beijing, 100084
China

Yili Hong (Contact Author)

Arizona State University (ASU) - W.P. Carey School of Business ( email )

Tempe, AZ 85287-3706
United States

HOME PAGE: http://yilihong.github.io/

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