Information Design for Online Platforms

In this dissertation we focus on strategic operations problems that arise within the technology- enhanced platform economy. Particularly, we explore how online platforms such as Amazon, Google and Facebook can improve their revenue performance by designing and controlling the information flow in the marketplace. In that, taking the online platforms’ perspective, we investigate the strategic interactions between online platforms and their market participants in an information-decentralized environment. We consider novel, profit-maximizing, information provision policies in this study, both in dynamic and static modeling environments. First, we uncover an online platform’s optimal information strategy for a time-locked sales campaign, whereby the platform selectively provides information about historical purchase decisions to influence future customers’ product evaluation. Next, we study a practically relevant problem on consumer privacy concerns. Particularly, in a setting where users can choose whether and what personal preference information to share with the platform, we study how the platform should design its profit-maximizing recommender system, that also strategically persuades users about the relevance of recommended items. The characterization of the platform’s optimal design of its provision policies permit us to draw important managerial and regulatory insights.