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: KrAIS Summer 2024, Hong Kong Workshop 2024

Organizational learning is a core process that controls organizations’ innovation and thus affects organizational performance and long-term survival. Due to the learning capability of machine learning (ML), recent research has recognized the far-reaching influence of 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 this tension by adding empirical evidence, we explore the innovations of 265 ML and 700 non-ML organizations from 2006 to 2017. Based on a comprehensive ML measure based on datasets on employees, patents, and academic publications, our results suggest that ML primarily contributes to shifting organizational learning towards exploration. Our results further show that ML’s influence depends on external environmental factors: ML’s effect increases with higher levels of 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.