Data-enabled learning, network effects, and competitive advantage

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.

Automated summary

This study models dynamic competition between firms that improve their products through learning from customer data. It examines how the shape of firms' learning functions, asymmetries between them, data accumulation, and customer beliefs affect a firm's competitive advantage. The study also explores the impact of public policies on data sharing, user privacy, and killer data acquisitions on competitive dynamics and efficiency.