Learning Effects: The Role of Digital Technology on Platforms

Abstract

Platforms that interconnect people, organizations and resources into a generative ecosystem of value creation and capture are becoming increasingly important to compete in the age of digital technology. Yet, the role of digital technology for the design and management of platforms is not well understood, and oftentimes black-boxed, in the literature on strategic management and related fields concerned with platform competition. The recent phenomenon of the rise of machine learning and related advancements in digital technology on platforms highlights the need for extending platform research beyond the concept of network effects. While this established concept has been very useful to understand the rapid scaling of platform user bases, it does not shed light on the nature of the interactions that occur within those user bases and on platforms themselves through applications of digital technology such as machine learning. To shed light on this important topic, we examine the role of digital technology on platforms and introduce the novel concept of learning effects as a complementary concept to our existing understanding of network effects. Based on the alternative assumption of platform self-tuning, which contrasts with established assumptions in the literature, we define learning effects as the impact that the acquisition of new data, information or knowledge by users or machines on a platform has on the value created for each participant, and build a conceptual model that details the various types of intended and unintended learning effects that occur in the relationship between humans and machines on platforms. Our model contributes to literature with a more integrative perspective on platform dynamics.