Optional Verification and Signaling in Online Matching Markets: Evidence from a Randomized Field Experiment

Lanfei Shi () and Siva Viswanathan ()
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Lanfei Shi: McIntire School of Commerce, University of Virginia, Charlottesville, Virginia 22901
Siva Viswanathan: Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20740

Information Systems Research, 2023, vol. 34, issue 4, 1603-1621

Abstract: Online matching platforms lack common informational mechanisms such as ratings and reviews that serve to reduce information asymmetry in transactional platforms. The lack of verified information about participants further exacerbates issues of information asymmetry in such markets. This study focuses on a novel role of verification in such matching markets—its ability to serve as a credible signal for a user when such verification is made optional and visible to other users. In collaboration with a leading online dating platform with no reputation mechanisms and where most of the information is self-disclosed, we design and conduct a randomized field experiment to examine not only who chooses to verify but also, the effectiveness of such optional verification for different types of users. Interestingly, we find that users on the two sides use the same signal very differently. Males act consistent with the conventional prediction of signaling, with high-type males being more likely to opt in to verification. As for females, we find that medium-type females are the most likely to opt in to verification as compared with high-type females. We also find that such differential opt-in decisions are related to the differences in the credibility of the existing key attribute of each side (viz., income for males and beauty for females). In examining the outcomes of verification, we find that verified users receive more contacts from higher-type users, with the high-type males and medium-type females benefitting the most. More interestingly, we find that verified users become more proactive and reach out to more and better potential partners. Further, the introduction of this voluntary verification facilitates desirable matching outcomes and benefits the platform as a whole. These findings have useful implications for research as well as practice.

Keywords: two-sided matching markets; signaling; optional verification; randomized field experiment; deep learning (search for similar items in EconPapers)
Date: 2023
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