Data‐enabled learning, network effects, and competitive advantage
研究了企业通过客户数据改进产品时的动态竞争，分析了学习函数形状、数据积累和客户信念如何影响竞争优势，并探讨了数据共享、隐私等公共政策对竞争效率的作用。
Abstract We model dynamic competition between firms which improve their products through learning from customer data, either by pooling different customers' data (across‐user learning) or by learning from repeated usage of the same customers (within‐user learning). We show how a firm's competitive advantage is affected by the shape of firms' learning functions, asymmetries between their learning functions, the extent of data accumulation, and customer beliefs. We also explore how public policies toward data sharing, user privacy, and killer data acquisitions affect competitive dynamics and efficiency. Finally, we show conditions under which a consumer coordination problem arises endogenously from data‐enabled learning.