DISA SEMINAR

To Brush or Not to Brush: Product Rankings, Customer Search, and Fake Orders

Speaker
Dr. Jin, Chen, Assistant Professor, School of Computing

22 Feb 2019 Friday, 10:30 AM to 12:00 PM

Executive Classroom, COM2-04-02

Abstract:

"Brushing"- the practice of online merchants placing fake orders of their own products to artificially inflate sales on e-commerce platforms- has recently received widespread public attention. On one hand, brushing enables merchants to boost their rankings in search results, because products with higher sales volume are often ranked higher. On the other hand, rankings matter because search frictions faced by customers narrow their attention to only a few products that show up at the top. Thus, fake orders from brushing may affect customer choice. We build a stylized model to understand merchants' strategic brushing behavior and its welfare implications. We consider two competing merchants selling substitutable products (one of high quality, the other of low quality) in an evolutionary sales-based ranking system that assigns a higher ranking to a product with higher sales. In principle, such an adaptive system improves customer welfare relative to a case in which products are randomly ranked, but it also triggers brushing as an unintended consequence. Since the high-quality merchant receives a favorable bias in the sales-based ranking, he mainly has a defensive brushing incentive, whereas the low-quality merchant mostly has an offensive brushing incentive. As a result, brushing is a double-edged sword for customers. It may lead customer welfare to be even lower than what it would be in a random-ranking system, but in some other cases, it can surprisingly improve customer welfare. If brushing is more difficult for merchants (e.g., due to tougher regulations), it may make customers worse off as it attenuates brushing by the high-quality merchant but induces the low-quality one to brush more aggressively. If search is easier for customers, it can actually hurt them as it may discourage the high-quality merchant from brushing disproportionately more than it does the low-quality one.

Biodata:

Dr. Jin, Chen is an assistant professor in the Department of Information Systems and Analytics at the National University of Singapore (NUS). Before joining NUS, Chen obtained his Ph.D. at Northwestern University, Industrial Engineering and Management Sciences department and was a postdoc research fellow at the Wharton School, Operations Information and Decisions group. His research interest lies in e-commerce, online platforms, and service operations.