Value Creation through Machine Learning Using Data Network Effects

Abstract

The recently proposed theory of data network effects aims to account for how user value is created from the use of machine learning technology. The theory recognises the unique learning ability of machine learning, which uses large data sets for predictions and decision making. This paper offers a critical assessment of that theory, unearthing some of its strengths and limitations. The latter are transformed into a set of interrelated research questions that jointly constitute the proposed research programme into how to use machine learning technology in order to create and capture value. The paper contributes to the literature with an articulation of some novel knowledge gaps.