SUBSTITUTING HUMAN DECISION-MAKING WITH MACHINE LEARNING: IMPLICATIONS FOR ORGANIZATIONAL LEARNING

Natarajan Balasubramanian, Yang Ye, Mingtao Xu

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

The richness of organizational learning relies on the ability of humans to develop diverse patterns of action by actively engaging with their environments and applying substantive rationality. The substitution of human decision-making with machine learning has the potential to alter this richness of organizational learning. Though machine learning is significantly faster and seemingly unconstrained by human cognitive limitations and inflexibility, it is not true sentient learning and relies on formal statistical analysis for decision-making. We propose that the distinct differences between human learning and machine learning risk decreasing the within-organizational diversity in organizational routines and the extent of causal, contextual, and general knowledge associated with routines. We theorize that these changes may affect organizational learning by exacerbating the myopia of learning, and highlight some important contingencies that may mute or amplify the risk of such myopia.

Original languageEnglish (US)
Pages (from-to)448-465
Number of pages18
JournalAcademy of Management Review
Volume47
Issue number3
DOIs
StatePublished - Jul 2022

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Strategy and Management
  • Management of Technology and Innovation

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