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.

With growing computational power and the availability of large-scale data, machine learning (ML) has emerged as a new important driver of organizational learning, yet our understanding of ML’s precise role remains conflicted. To help unpack ML’s role, we examine how ML investments shift organizations’ learning toward exploitation versus exploration and how these shifts influence organizational performance and survival. Drawing on data from 3,383 publicly listed U.S. organizations from 2005 to 2019, our findings suggest that increased ML use generally tends to shift organizations towards exploration. This ML-induced learning tendency mediates the positive relationship between ML investments and organizational survival, with effects particularly pronounced among non-IT organizations with established exploitative tendencies. We further find that ML acts as a catalyst for context-dependent balancing: in stable environments, ML nudges exploitative organizations toward greater exploration, whereas in dynamic environments, ML tempers explorative organizations by reinforcing exploitation. This study provides the first large-scale empirical evidence on how ML reshapes organizational learning and its organizational impacts, contributing new empirical insights to the largely theoretical and contested discourse to help further rethink organizational learning in the age of ML