Ghost in the machine: On organizational theory in the age of machine learning

With rapid advancements inmachine learning,weconsider the epistemological opportunities presented by thisnovel tool for promoting organizational theory. Ourpaperunfolds in three sections.Webeginwithanoverviewof the three formsofmachinelearning(supervised, reinforcement, and unsupervised), translating these onto our common modes of research (deductive, abductive, inductive, respectively). Next, we present frank critiques ofmachinelearningapplications for science, aswell asof the state of organizational scholarshipwrit large,highlightingcontemporarychallengesinbothdomains.Wedosotomake the case thatmachine learning and theory are not in competition but have the potential to playcomplementaryrolesinmovingourfieldbeyondsiloeddomainsandincrementaltheory. Our final sectionspeaksto thissynergy.Wepropose thatmachinelearningcanact asa 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-functionalpurposesofinspiringandfacilitatinglong-termepistemologicalprogress across domains.