Paper Number
1689
Paper Type
Completed
Description
Organizational learning is a fundamental process that defines organizational behavior and thereby strongly influences organizational performance. As organizations increasingly adopt machine learning (ML) systems in their routines, the need to illuminate the impact of learning machines on organizational learning processes becomes increasingly urgent. In particular, due to their highly interdisciplinary and collaborative nature, ML developments—as organizations’ activities aimed at creating productively usable ML systems—may hereby represent an important but not yet well understood mechanism for fostering organizational learning. To explore how ML developments affect organizational learning processes, we interviewed 42 experts who are frequently involved in ML developments. Our findings suggest that ML developments can enhance organizational learning by stimulating a variety of organizational learning processes that generate a wealth of explicit and tacit knowledge in organizations.
Recommended Citation
Vetter, Oliver A.; Sturm, Timo; Fecho, Mariska; and Buxmann, Peter, "Machine Learning Developments as Stimuli for Organizational Learning" (2023). ICIS 2023 Proceedings. 5.
https://aisel.aisnet.org/icis2023/gov_strategy/gov_strategy/5
Machine Learning Developments as Stimuli for Organizational Learning
Organizational learning is a fundamental process that defines organizational behavior and thereby strongly influences organizational performance. As organizations increasingly adopt machine learning (ML) systems in their routines, the need to illuminate the impact of learning machines on organizational learning processes becomes increasingly urgent. In particular, due to their highly interdisciplinary and collaborative nature, ML developments—as organizations’ activities aimed at creating productively usable ML systems—may hereby represent an important but not yet well understood mechanism for fostering organizational learning. To explore how ML developments affect organizational learning processes, we interviewed 42 experts who are frequently involved in ML developments. Our findings suggest that ML developments can enhance organizational learning by stimulating a variety of organizational learning processes that generate a wealth of explicit and tacit knowledge in organizations.
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