Data Network Effects: Key Conditions, Shared Data, and the Data Value Duality

Abstract

Clough and Wu (2020) provide an interesting and thought-provoking response to our article (Gregory, Henfridsson, Kaganer, & Kyriakou, 2020) on the role of Artificial Intelligence (AI) and data network effects for the creation of user value. We welcome the debate around data network effects as a new category of network effects. In this response note, we build upon the points raised by Clough and Wu (2020) to outline three clarifications to our theory of data network effects concerning: (1) conditions when data network effects accrue, (2) the importance of theorizing shared data, and (3) the model’s ability to explain the cumulative effect of data-driven learning on value creation and value capture.