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. This paper was accepted by Juanjuan Zhang, marketing.
Major online platforms such as Amazon and eBay have invested significantly in search technologies to direct consumer searches to relevant products. These technologies lead to targeted search, implying consumers are visiting more relevant sellers first. For example, consumers may directly enter their desirable attributes into search queries, and the platform will retrieve relevant sellers accordingly. The platform may also let consumers refine the search outcomes by various criteria. This
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