Abstract
Online platforms often assign sellers summary symbols based on whether their ratings pass certain thresholds. Consumers may focus on the symbols and pay limited attention to the ratings. This bias leads to discontinuously increased demand at the thresholds. I use a theoretical model to illustrate that sellers will lower the prices before their ratings reach the thresholds and increase their prices afterward due to the positive demand shock. I collect data from Taobao to test the theoretical predictions. Using regression discontinuity, I find that on the demand side, the hourly sales increase significantly when a seller passes the threshold, even conditional on the same item. On the supply side, the prices indeed exhibit a V-shaped pattern with respect to the ratings. Furthermore, sellers preemptively increase prices shortly before reaching thresholds, supporting the theoretical predictions. Copyright: © 2021 INFORMS
| Original language | English |
|---|---|
| Pages (from-to) | 4380-4397 |
| Journal | Management Science |
| Volume | 68 |
| Issue number | 6 |
| Online published | 20 Sept 2021 |
| DOIs | |
| Publication status | Published - Jun 2022 |
| Externally published | Yes |
Funding
The author thanks Ganesh Iyer, John Morgan, Stefano DellaVigna, Avi Goldfarb, Teck-Hua Ho, Minjung Park, Przemyslaw Jeziorski, J. Miguel Villas-Boas, Steve Tadelis, Yupeng Chen, and seminar participants at University of California Berkeley, Peking University, and the 13th International Industrial Organization Conference in Boston, MA, for useful feedback. The standard disclaimer applies.
Research Keywords
- behavioral IO
- limited attention
- online reputation
- regression discontinuity
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