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

ABSTRACT

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.

KEYWORDS:

• E-commerce platforms
• quality misrepresentation
• agency selling
• product returns
• online reviews
• platform policies
• analytical modeling
• online platforms

Supplementary information

Supplemental data for this article can be accessed on the publisher’s website

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

1. We assume that consumers do not know that the quality signals are manipulated at the pre-purchase stage [49]. This assumption is consistent with practice. For example, the accuracy of individuals’ lie-truth judgments is found to be only 54 percent [5]. Since all product returns are caused by expectation disconfirmation, consumers are not able to anticipate the product return at the pre-purchase stage.

2. This condition can also be illustrated as $\mathrm{\Delta }q>\frac{3t\left(1-\eta \right)}{1+\eta }$; The feasible region of $\mathrm{\Delta }q$ (or $q_H$) always exists because $\frac{6\alpha t-\left(1-r\right)\left(1-kt\right)\left(1-2kt\right)}{2\alpha +k\left(1-r\right)\left(1-2kt\right)}>\frac{3t\left(1-\eta \right)}{1+\eta }$ always holds.

3. This assumption is aligned with the reality. For example, electronic marketplaces normally list the products with similar average rating and popularity in the same recommendation list [45].

4. If we set the total demand as $D^T$, Assumption A2 would be accordingly updated as $k<\frac{D^T}{2t}$.

5. For example, Narvar investigates on consumers’ satisfaction with the online product return process. The survey collected in 2019 shows that a substantial portion of online shoppers do not find the returns process easy, and only 60 percent of consumers indicate they are satisfied with their recent return.

See https://see.narvar.com/Consumer_Report-Returns_LP.html (last accessed on May 8th, 2020).

6. If we follow Dellarocas [19] and adopt a model where the retail prices are pre-determined as $p_H^{C1\ast }$ and $p_L^{C1\ast }$, both sellers would suffer from the prisoner’s dilemma. In such case, $\widetilde{{\pi }_H^{C4\ast }}<{\pi }_H^{C1\ast }$ and $\widetilde{{\pi }_L^{C4\ast }}<{\pi }_L^{C1\ast }$always hold. The detailed proof is in the Online Supplemental Appendix A.

7. In the equilibrium results, high initial quality difference also stands for high perceived quality difference between the two products in the equilibrium (see Eq. (4)).

8. For example, Amazon specifies that for the media orders, it credits back all the original order-related fees to sellers. For the non-media orders, only a small portion of original order-related fees will be retained (up to $5) by Amazon. See https://sellercentral.amazon.com/gp/help/external/G21531?language=en_US (last accessed on May 8, 2020).

9. In economics, an externality is the cost or benefit that affects a third party who does not choose to incur that cost or benefit [7]. We focus on the negative externality, which is the activity that imposes a negative effect on an unrelated third party. In our context, a seller’s manipulation activity can hurt the other seller’s demand on the same platform. The negative externality in this setting is that even if a focal seller does not involve much in quality misrepresentation, consumers may quit the platform and do not purchase the product of the focal seller because the misrepresentation level of its competitor on the platform is high.

Additional information

Notes on contributors

Jingchuan Pu

Jingchuan Pu ([email protected]) is an assistant professor in the Department of Information Systems and Operations Management, Warrington College of Business, University of Florida. He was an assistant professor at Smeal College of Business, Pennsylvania State University. He received his Ph.D. in Information Systems from University of Florida. Dr. Pu’s research focuses on social media (public and corporate), fintech, and e-commerce. His work has appeared in premier academic journals, such as Information Systems Research, Journal of Management Information Systems, MIS Quarterly, and Production and Operations Management.

Tingting Nian

Tingting Nian ([email protected]) is an assistant professor of Information Systems and a Hellman fellow in the Paul Merage School of Business at University of California at Irvine. She received her Doctoral degree in Information Systems from the Leonard N. Stern School of Business, New York University. Dr. Nian has received several grants and awards from institutions including Think Forward Initiative, Hellman Foundation, Wharton Customer Analytics Initiative and INFORMS. Her work has appeared in Management Science, MIS Quarterly, and Information Systems Research.

Liangfei Qiu

Liangfei Qiu ([email protected]; corresponding author) is the PricewaterhouseCoopers Associate Professor in the Department of Information Systems and Operations Management, Warrington College of Business, University of Florida. He received his Ph.D. from University of Texas at Austin. Dr. Qiu’s research focuses on prediction markets, social networks and social media platforms, telecommunications networks, and economics of information systems. His work has appeared in premier academic journals, such as Information Systems Research, Journal of Management Information Systems, MIS Quarterly and, Production and Operations Management. He serves as Associate Editor at MIS Quarterly, Senior Editor at Production and Operations Management, and Associate Editor at Decision Support Systems.

Hsing Kenneth Cheng

Hsing Kenneth Cheng ([email protected]) is the John B. Higdon Eminent Scholar and Department Chair in the Department of Information Systems and Operations Management, Warrington College of Business, University of Florida. He received his Ph.D. from University of Rochester. Dr. Cheng’s research interests focus on analyzing the impact of Internet technology on software development and marketing, and on information systems policy issues. His work has appeared in premier academic journals, such as Information Systems Research, Journal of Management Information Systems, MIS Quarterly, and Production and Operations Management.