DOI: 10.1287/isre.2022.0233 ISSN: 1047-7047

Mr. Right or Mr. Best: The Role of Information Under Preference Mismatch in Online Dating

Hongchuan Shen, Chu (Ivy) Dang, Xiaoquan (Michael) Zhang
  • Library and Information Sciences
  • Information Systems and Management
  • Computer Networks and Communications
  • Information Systems
  • Management Information Systems

The rise of two-sided matching platforms such as Uber, Airbnb, Upwork, and Tinder has changed the way we commute, travel, work, and even date. The success of these platforms depends on the role of information: What information and how much information should be provided? In this study, we focus on a defining characteristic of two-sided matching markets—that is, a match depends on the possibly different preferences of the two sides—and argue that the optimal amount of information released depends on the extent to which the preferences of the two sides are mismatched. Specifically, in an empirical context of online dating, we find that when there exists preference mismatch between the two sides, having less match-relevant information about the other side leads to a better matching outcome. Our study provides insights into how the amount of information available to each side affects matching outcomes on two-sided platforms and offers guidance on information design strategies. Additionally, our findings are not confined to dating websites and can be extended to other matching platforms, such as Airbnb and Upwork, where misaligned preferences can exist between the two sides.

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