Automation, Research Technology, and Researchers' Trajectories: Evidence from Computer Science and Electrical Engineering

• 成果类型: Article
• 署名作者: Furman, Jeffrey L.; Teodoridis, Florenta
• 署名单位: Boston University; National Bureau of Economic Research; University of Southern California
• 刊物名称: ORGANIZATION SCIENCE
• ISSN/ISSBN: 1047-7039
• DOI: 10.1287/orsc.2019.1308
• 发表日期: 2020
• 页码: 330-354
• 关键词: automation; Knowledge production; INNOVATION; research technology; rate and direction of innovation; technological change; topic modeling; Machine Learning; idea space; research trajectories; knowledge trajectories; diversification; breadth and depth of knowledge

摘要

Y We examine how the introduction of a technology that automates research tasks influences the rate and type of researchers' knowledge production. To do this, we leverage the unanticipated arrival of an automating motion-sensing research technology that occurred as a consequence of the introduction and subsequent hacking of the Microsoft Kinect system. To estimate whether this technology induces changes in the type of knowledge produced, we employ novel measures based on machine learning (topic modeling) techniques and traditional measures based on bibliometric indicators. Our analysis demonstrates that the shock associated with the introduction of Kinect increased the production of ideas and induced researchers to pursue ideas more diverse than and distant from their original trajectories. We find that this holds for both researchers who had published in motion-sensing research prior to the Kinect shock (within-area researchers) and those who did not (outside-area researchers), with the effects being stronger among outside-area researchers.