{"title":"Algorithmic management in scientific research","authors":"Maximilian Koehler, Henry Sauermann","doi":"10.1016/j.respol.2024.104985","DOIUrl":null,"url":null,"abstract":"<div><p>Artificial intelligence (AI) can perform core research tasks such as generating research questions, processing data, and solving problems. We shift the focus from AI as a “worker” to ask whether, how, and when AI can also “manage” human workers who perform such tasks. Focusing on the context of crowd science, we find examples of algorithmic management (AM) in five key functions highlighted in prior organizational literature: task division and task allocation, direction, coordination, motivation, and supporting learning. These applications benefit from the instantaneous, comprehensive, and interactive capabilities of AI, and reflect several more general underlying functions such as matching, clustering, and forecasting. Quantitative comparisons show that projects using AM are larger and more likely to be associated with platforms than projects not using AM, pointing to potentially important contingency factors. We conclude by outlining an agenda for future research on algorithmic management in scientific research.</p></div>","PeriodicalId":48466,"journal":{"name":"Research Policy","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Policy","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0048733324000349","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial intelligence (AI) can perform core research tasks such as generating research questions, processing data, and solving problems. We shift the focus from AI as a “worker” to ask whether, how, and when AI can also “manage” human workers who perform such tasks. Focusing on the context of crowd science, we find examples of algorithmic management (AM) in five key functions highlighted in prior organizational literature: task division and task allocation, direction, coordination, motivation, and supporting learning. These applications benefit from the instantaneous, comprehensive, and interactive capabilities of AI, and reflect several more general underlying functions such as matching, clustering, and forecasting. Quantitative comparisons show that projects using AM are larger and more likely to be associated with platforms than projects not using AM, pointing to potentially important contingency factors. We conclude by outlining an agenda for future research on algorithmic management in scientific research.
期刊介绍：
Research Policy (RP) articles explore the interaction between innovation, technology, or research, and economic, social, political, and organizational processes, both empirically and theoretically. All RP papers are expected to provide insights with implications for policy or management.
Research Policy (RP) is a multidisciplinary journal focused on analyzing, understanding, and effectively addressing the challenges posed by innovation, technology, R&D, and science. This includes activities related to knowledge creation, diffusion, acquisition, and exploitation in the form of new or improved products, processes, or services, across economic, policy, management, organizational, and environmental dimensions.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
