Managing Quality Information Through AI-Assisted Platform Certification and Seller Voluntary Disclosure in Competitive Online Retail

In online retail, consumers cannot experience product quality before purchase. With the adoption of artificial intelligence (AI), platforms can certify product quality information. However, stronger platform certification may reduce sellers’ incentives to disclose and limit personalized information such as product fit. This study examines the conditions under which a platform should adopt AI-assisted platform certification (AIPC). We develop a game-theoretic model with one platform and two competing sellers. We compare the case of not adopting AIPC with adopting AIPC, and examine how AIPC affects seller disclosure, pricing, and profits. Sellers decide whether to disclose product information and set prices. Consumers update their quality beliefs based on seller disclosure and platform labels. Our results show that AIPC is not always the preferred strategy. When product-fit information spillovers between competing sellers are strong, the platform may be better off not adopting AIPC. When information spillovers are weak, AIPC adoption depends on consumers’ prior belief regarding product quality. Specifically, when consumers have a low prior belief that an uncertified or undisclosed product is of high quality, AIPC benefits the platform and sellers but reduces consumer surplus. When this prior belief is sufficiently high, AIPC creates a win–win–win outcome for the platform, sellers, and consumers.