GHOST IN THE MACHINE: ON ORGANIZATIONAL THEORY IN THE AGE OF MACHINE LEARNING

• 成果类型: Article
• 署名作者: Leavitt, Keith; Schabram, Kira; Hariharan, Prashanth; Barnes, Christopher M.
• 署名单位: Oregon State University; University of Washington; University of Washington Seattle; Indian School of Business (ISB); University of Washington; University of Washington Seattle
• 刊物名称: ACADEMY OF MANAGEMENT REVIEW
• ISSN/ISSBN: 0363-7425
• DOI: 10.5465/amr.2019.0247
• 发表日期: 2021
• 页码: 750-777
• 关键词: PRESIDENTIAL-ADDRESS; JOB-PERFORMANCE; self-esteem; management; diversity; MODEL; discourse; EVOLUTION; culture; CONSTRUCTION

摘要

With rapid advancements inmachine learning, we consider the epistemological opportunities presented by thisnovel tool for promoting organizational theory. Our paperun folds in three sections. We begin with an overview of the three forms of machine learning(supervised, reinforcement, and unsupervised), translating these onto our common modes of research (deductive, abductive, inductive, respectively). Next, we present frank critiques of machine learning applications for science, aswell as of the state of organizational scholarship writ large, high lighting contemporary challenges in both domains. We do so to make the case that machine learning and theory are not in competition but have the potential to play complementary roles inmovingour field beyondsiloeddo mainsand incremental theory. Our final sectionspeaks to this synergy. We propose that machine learning canact as a tool to test and prunemidrange theory, and as a catalyst to expand the explanatory spectrumthat theory can inhabit. Specifically, we outline how machine learning can support local but perishable theory targeting pragmatic problems in the here and now, and grand theory that is sufficiently bold and generalizable across contexts and time to serve the social-functional purposes of inspiring and facilitating long-termep is temological progress across domains.