Platform Policies and Sellers’ Competition in Agency Selling in the Presence of Online Quality Misrepresentation

Jingchuan Pu, Tingting Nian, Liangfei Qiu, Hsing Kenneth Cheng

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


On e-commerce platforms, consumers rely heavily on online reviews, sales volume, and social media discussions to infer product quality. As a result, the past decade has witnessed an explosive growth of seller-initiated misrepresentation of quality through fake reviews, fake sales, and fake posts. We develop an analytical model to investigate sellers’ competition in quality misrepresentation in agency pricing and the platform’s policies. The platform can discourage sellers’ quality misrepresentations by increasing the cost of misrepresentation or implementing a more lenient product return policy. We find that while a stricter anti-misrepresentation policy deters the misrepresentation of the high-quality seller, such a strategy may unintendedly incentivize the low-quality seller to misrepresent the quality more. Furthermore, increasing return leniency deters low-quality seller’s misrepresentation in a wider range of market conditions than increasing the misrepresentation cost. We show sellers’ online quality misrepresentation behaviors in a competitive setting, and our results have practical implications for platform policies.

Original languageEnglish (US)
Pages (from-to)159-186
Number of pages28
JournalJournal of Management Information Systems
Issue number1
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Computer Science Applications
  • Management Science and Operations Research
  • Information Systems and Management


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