Algorithmic Management in Scientific Research

Growing evidence shows that artificial intelligence (AI) can perform core research activities such as reviewing prior literature, processing data, and solving problems. We shift the focus from AI as a “worker” to ask whether and how AI can also “manage” human workers who perform research tasks. We explore this question in a context that poses great management challenges and offers unusual transparency for scholars: Crowd science projects that involve many participants who vary greatly with respect to factors such as their skills, interests, as well as geographic location. Drawing on qualitative and quantitative data, we show evidence that AI can perform five key management functions highlighted in the broader organizational literature: task division and task allocation, direction, coordination, motivation, and supporting learning. We also explore four project and task-level contingency factors that appear to shape the use and effectiveness of AI: Project size and platform membership as well as the digitalization and routinization of tasks. We conclude by outlining an agenda for future research on algorithmic management in scientific research