题　目：Fulfillment by Amazon as a Strategic Lever: Anticompetitive or Welfare Enhancing?

嘉　宾：Yao Li, Ph.D. Candidate, Northwestern University

主持人：唐卓栋 助理教授 欧宝app官方网站下载安泰经济与管理学院

时　间：2025年12月25日（周四）14:00-15:30

地　点：安泰楼A305室

内容简介：

Sellers on Amazon Marketplace can adopt Fulfillment by Amazon to fulfill orders. The Federal Trade Commission (FTC) alleges that the service harms sellers and consumers through coerced adoption and excessive fees, while Amazon defends it as helping sellers deliver superior service to consumers. Motivated by this antitrust debate, we develop a stylized model with rival platforms, where Platform A offers a quality-improving fulfillment service (hereafter, FBA) alongside third-party logistics (3PL), while Platform B only offers 3PL. Sellers pay commissions and fulfillment fees. We define FBA as “subsidized” when it delivers a positive net fulfillment value (NFV), i.e., its logistics quality advantage over 3PL exceeds their fulfillment fee differential. Seller adoption of FBA is profitable and voluntary when the NFV is sufficiently large, but a higher commission rate makes adoption less likely. Platform A’s provision of FBA is profitable under high quality advantage and moderate fulfillment fee. These conditions give rise to three equilibrium regions: 1) In the region of profitable provision and voluntary adoption, FBA always improves consumer surplus and may increase social welfare under certain conditions. 2) In the region of profitable provision but involuntary adoption, Platform A may have incentives to push FBA adoption at the expense of consumers, demonstrating the potential and consequences of coerced adoption. 3) In the region of unprofitable provision, a sufficient subsidy by Platform A induces seller adoption of FBA, leading to increased consumer surplus, which effectively turns FBA into a form of predatory pricing to erode Platform B’s market share.

演讲人简介：

Yao Li is a Ph.D. candidate in Operations Management at Northwestern University’s Kellogg School of Management. Her research focuses on platform operations, human-AI interaction and data-driven decision making. Across these contexts, she examines how design choices affect both system performance and participant behavior. Methodologically, she draws on game theoretic modeling, stochastic modeling, causal inference and field experiments to generate insights that are both analytically rigorous and practice relevant. Prior to her doctoral studies, Yao studied Mathematics at Claremont McKenna College, where her work focused on economic forecasting.

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