Lee, Myunghwan, Timo Sturm, Gene Moo Lee “Unlocking the Impact of Machine Learning on Organizational Learning: Evidence from US Public Firms”, Work-in-Progress.

• Presentations: KrAIS Summer 2024

Organizational learning is a critical core process that fundamentally controls organizations’ innovation and thus affects organizational performance and long-term survival. Due to their ability to learn, recent research has recognized the far-reaching influence of machine learning (ML) systems’ contributions to organizational learning. So far, however, the emerging discourse on the role of ML in organizational learning has remained largely theoretical, offering helpful initial insights but inconclusive predictions about ML’s impact. To resolve existing conflicts by adding empirical evidence, we explore the real-world innovations of 265 ML and 700 non-ML organizations from 2006 to 2019. Based on a novel ML measure constructed from ML patents, publications, and transaction datasets, we find that ML primarily contributes to shifting organizational learning toward exploration. Our results further show that ML’s influence depends on external environmental factors: ML’s effect increases with higher levels of market concentration and competitors’ strategic orientation towards ML. Lastly, we find that organizations using ML tend to survive longer due to increased performance and more balanced innovation. To the best of our knowledge, this is the first large-scale empirical study of the impact of ML on organizational learning outcomes, contributing to rethinking organizational learning in the era of ML.