E-commerce marketplaces provide business opportunities to millions of sellers worldwide. Some of these sellers have special relationships with the marketplace by virtue of using their subsidiary services (e.g., fulfillment and/or shipping services provided by the marketplace) -- we refer to such sellers collectively as Related Sellers. When multiple sellers offer to sell the same product, the marketplace helps a customer in selecting an offer (by a seller) through (a) a default offer selection algorithm, (b) showing features about each of the offers and the corresponding sellers (price, seller performance metrics, seller's number of ratings etc.), and (c) finally evaluating the sellers along these features. In this paper, we perform an end-to-end investigation into how the above apparatus can nudge customers toward the Related Sellers on Amazon's four different marketplaces in India, USA, Germany and France. We find that given explicit choices, customers' preferred offers and algorithmically selected offers can be significantly different. We highlight that Amazon is adopting different performance metric evaluation policies for different sellers, potentially benefiting Related Sellers. For instance, such policies result in notable discrepancy between the actual performance metric and the presented performance metric of Related Sellers. We further observe that among the seller-centric features visible to customers, sellers' number of ratings influences their decisions the most, yet it may not reflect the true quality of service by the seller, rather reflecting the scale at which the seller operates, thereby implicitly steering customers toward larger Related Sellers. Moreover, when customers are shown the rectified metrics for the different sellers, their preference toward Related Sellers is almost halved.

翻译：电子商务平台为数百万全球卖家提供了商业机遇。其中部分卖家因使用平台的附属服务（例如平台提供的仓储和/或物流服务）而与平台存在特殊关联——我们将此类卖家统称为关联卖家。当多个卖家提供同一商品时，平台通过以下方式协助消费者选择销售方案（即卖家）：(a) 默认销售方案选择算法，(b) 展示各销售方案及对应卖家的特征信息（价格、卖家绩效指标、卖家评价数量等），(c) 最终依据这些特征对卖家进行评估。本文对亚马逊在印度、美国、德国和法国四个不同市场的平台如何通过上述机制引导消费者选择关联卖家进行了端到端的系统性研究。研究发现，在提供明确选择的情况下，消费者偏好的销售方案与算法选择的销售方案可能存在显著差异。我们指出亚马逊对不同卖家采用差异化的绩效指标评估策略，这可能使关联卖家获益。例如，此类策略导致关联卖家的实际绩效指标与平台展示的绩效指标之间存在明显差异。进一步观察发现，在面向消费者的卖家特征中，卖家评价数量对其决策影响最大，但该指标可能并不反映卖家的真实服务质量，而是体现其经营规模，从而隐性地引导消费者选择规模更大的关联卖家。此外，当向消费者展示不同卖家的修正指标后，其对关联卖家的偏好程度几乎降低了一半。