So, Who Likes You? Evidence from a Randomized Field Experiment

MANAGEMENT SCIENCE(2022)

引用 0|浏览10
暂无评分
摘要
With one-third of marriages in the United States beginning online, online dating platforms have become important curators of the modern social fabric. Prior work on online dating has elicited two critical frictions in the heterosexual dating market. Women, governed by age-old social norms of not making the first move, are inhibited in their interactions in that they do not initiate contact with men. On the other side, men send an abundance of messages, the majority of which do not convert to matches. A key distinguishing feature of online dating versus its traditional counterpart is the ability to leave a range of digital signals not replicable in the offline world. These digital signals can impact the nature of online dating platform outcomes. In this paper, we study the impact of a feature that reveals "who likes you" (WLY) on engagement, the number of matches, match efficiency, and match sorting in online dating. This feature reveals the identity of the voters who have rated the focal user with a like. To causally identify the effect of this feature, we conduct a large-scale randomized control trial in collaboration with a major North American dating platform. The treatment causes women to be more proactive, sending 7.4% more messages, which is a highly desirable market improvement given that men send double the number of messages compared with women. Further, we find that the women endowed with this feature increase their matches by 14.4%, whereas men increase their matches by 11.5%. Analyzing the moderating impact of desirability-a key aspect of the WLY feature-provides us with nuanced findings. Depending on the levels of each of the two parties' desirability, we see evidence of sorting, encouragement, and discouragement. History: Accepted by Anandhi Bharadwaj, information systems. Supplemental Material: The Online Appendix is available at https://doi.org/10.1287/mnsc.2022.4576.
更多
查看译文
关键词
online dating,IT-enabled features,identity revelation,digital signals,randomized trial,field experiment,matching markets,desirability
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要