Consumer Preference Information Sharing of Online Platforms with Competing Suppliers and Customization

Problem definition: Online platforms have access to massive data about consumer preferences while upstream suppliers typically do not. This study explores how an online platform should strategically share its privately-owned consumer preference information with suppliers to help them determine optimal prices under the marketplace or reselling model. Enabled by advanced technological developments such as flexible manufacturing, 3D printing and AI, suppliers may have the capability to offer customized or personalized products based on shared consumer preference information. We next examine the implications of the customization policies for suppliers if preference information is shared. Methodology/results: Using a game-theoretic framework, we find that the platform may share information with one supplier only under the marketplace model, but never does under the reselling model. When the preference uncertainty is low, the platform may not share information with either supplier under the marketplace model, but may share with both due to weakened double marginalization under the reselling model. The platform adopts the marketplace model when the product quality is low, but switches to reselling when the product quality is high. Managerial implications: Interestingly, the customization capability for suppliers may weaken the platform’s incentive of information sharing under the marketplace model, whereas it may strengthen it under the reselling model. While the option of customization may boost consumers and society under the marketplace model, it never increases consumer surplus and social welfare under the reselling model. Our findings provide useful managerial guidelines for an online platform on when and whom to share customer preference information with, and for policy makers on how to design information or data sharing policies to benefit society, especially with the new advanced technological developments.