Examining the Effects of Demand Information Disclosure on Congestion and Matching Efficiency in Online Dating
Abstract
A common issue in online dating platforms is that many users focus their attention on a subset of popular peers, which leads to congestion and inefficiency. Our study examines this issue with a randomized natural field experiment, wherein we partnered with a large online dating platform to experimentally test an information disclosure intervention, informing a random set of users about their peers' recent dating request volumes. We examine whether and for whom this intervention facilitated the redistribution of attention away from popular individuals, i.e., a reduction in demand concentration. We first conceptualize the nature of demand information and discuss that the benefits of disclosing demand information are not altogether clear in this setting, a priori, because dating platforms are distinct from most other multi-sided platforms in several important respects. Dating platforms facilitate social relationships, rather than trade in goods and services. Therefore, they operate on different norms and typically lack common levers that platform operators employ to balance supply and demand, such as pricing mechanisms and reputation systems. Dating app users may therefore pay greater attention to the quality implications of peer demand information, worsening congestion. On the other hand, demand information disclosure may be atypically effective at mitigating congestion in a dating context, because daters will not simply consider the potential of being ignored, creating fears of social rejection, and leading them to shy away from in-demand users. These unique aspects call into question whether our intervention will affect congestion, and whether that effect will be desirable. Our results show that demand information disclosure, when presented in tandem with 'capacity cue,' is beneficial to the platform; it increases an average user's attention toward low-demand partners, decreases attention toward high-demand peers, and ultimately drives greater matching efficiency. Further, heterogeneity analyses demonstrate that these effects are driven predominantly by those users who are congestion-sensitive; those users who tend to rely most heavily upon outbound messages for matching, and those who do not tend to be on the receiving end of matching requests. Our study provides theoretical and practical implications for the literature on online dating, platform design, and congestion management.
Bio:
Kevin is a Professor of Information System and Business Analytics at the C. T. Bauer College of Business at University of Houston. He is also the Director of the Bauer College PhD Programs and is leading the Bauer College Initiative on AI and Digital Society. Kevin is a Senior Editor of Production and Operations Management, and an Associate Editor at Information Systems Research.
Kevin's research interests are in the areas of Future of Work, Digital Platforms, User-generated Content, and Human-AI Interactions. His research has been published in journals such as Management Science, Information Systems Research, MIS Quarterly, Production and Operations Management, and INFORMS Journal on Computing. His work has been supported by multiple grants, from the NET Institute, the Department of Education, Robert Wood Johnson Foundation, and the National Science Foundation. He was awarded the Association for Information Systems Early Career Award in 2018 and the INFORMS Information Systems Society Sandy Slaughter Early Career Award in 2019. And recently, he was awarded the Associate Editor of the Year Award from Information Systems Research.