Manipulation for Competition : Agency Pricing in the Presence of Quality Misrepresentation

In e-commerce platforms, consumers rely heavily on product reviews, sales volume, and number of product page visits to infer product quality. The past decade witnessed an explosive growth of seller-initiated quality misrepresentation by using fake reviews, fake sales volume, and fake clicks to manipulate consumers' quality perception on product. We develop an analytical model to investigate sellers' quality misrepresentation under the agency pricing regime, wherein sellers compete along price and quality misrepresentation. We examine three different strategies the platform can adopt to counter sellers' quality misrepresentations: increasing the cost of misrepresentation, implementing a more lenient product return policy, and enhancing consumers' identification ability. We find that as the cost of misrepresentation increases or product return policy becomes more lenient, the misrepresentation level of the high-quality seller always decreases but the misrepresentation level of the low-quality seller may surprisingly increase. In other words, a stricter anti-misrepresentation strategy may unintendedly incentivize the low-quality seller to conduct more quality misrepresentation. Our analyses show that implementing a more lenient product return policy is more effective to decrease the misrepresentation level of low-quality seller than the other two strategies. These findings demonstrate the necessity to evaluate different anti-misrepresentation strategies, and the effectiveness of those strategies on different sellers