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

Chen Jin (), Luyi Yang () and Kartik Hosanagar ()
Additional contact information
Chen Jin: National University of Singapore, School of Computing, Department of Information Systems and Analytics, 13 Computing Drive, Singapore 117417, Republic of Singapore
Luyi Yang: Johns Hopkins University, Carey Business School, 100 International Drive, Baltimore, MD, 21202, USA
Kartik Hosanagar: The University of Pennsylvania, the Wharton School, 3730 Walnut Street, 552 Jon M. Huntsman Hall, Philadelphia, PA, 19104, USA

No 19-02, Working Papers from NET Institute

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 the 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 the 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 (e.g., due to improved search technologies), it can actually hurt them as it may disproportionately discourage the high-quality merchant from brushing.

Keywords: search; rankings; brushing; fake; customer welfare (search for similar items in EconPapers)
Pages: 70 pages
Date: 2019-09
New Economics Papers: this item is included in nep-mic and nep-sea
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