Data-enabled learning, network effects, and competitive advantage

• 成果类型: Article
• 署名作者: Hagiu, Andrei; Wright, Julian
• 署名单位: Boston University; National University of Singapore
• 刊物名称: RAND JOURNAL OF ECONOMICS
• ISSN/ISSBN: 0741-6261
• DOI: 10.1111/1756-2171.12453
• 发表日期: 2023
• 页码: 638-667
• 关键词: incumbency; privacy; price

摘要

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.