Lee, Myunghwan, Timo Sturm, Gene Moo Lee “Exploring the Influence of Machine Learning on Organizational Learning: An Empirical Analysis of Publicly Listed Organizations”, Work-in-Progress.

• Presentations: JUSWIS 2024, KrAIS Summer 2024
• Best Short Paper Award at KrAIS Summer Workshop 2024.

We contribute to the literature on the role of machine learning (ML) in organizational learning by examining two key learning tendencies: exploitation and exploration. We analyze the effect of ML investments on organizations’ learning tendency, which in turn influences firm performance and survival. Our findings suggest that ML primarily shifts organizations towards exploration and that ML-induced learning tendency fully mediates the positive relationship between ML investments and organization survival. Notably, we find that non-IT organizations with exploitative tendencies can effectively shift towards exploration through ML investments. To our knowledge, this study provides the first large-scale empirical insights into ML’s impact on organizations’ learning tendency and performance outcomes, offering valuable insights for rethinking organizational learning in the age of ML.