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

https://doi.org/10.1287/orsc.2019.1308

65 Pages Posted: 27 Dec 2018 Last revised: 9 Sep 2019

See all articles by Jeffrey L. Furman

Jeffrey L. Furman

Boston University - Department of Strategy & Policy; National Bureau of Economic Research (NBER)

Florenta Teodoridis

University of Southern California - Marshall School of Business

Date Written: April 23, 2019

Abstract

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 the 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 as well as 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.

Keywords: 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

JEL Classification: O10, O31, O33, O39, O40, O49

Suggested Citation

Furman, Jeffrey L. and Teodoridis, Florenta, Automation, Research Technology, and Researchers’ Trajectories: Evidence from Computer Science and Electrical Engineering (April 23, 2019). https://doi.org/10.1287/orsc.2019.1308. Available at SSRN: https://ssrn.com/abstract=3285286 or http://dx.doi.org/10.2139/ssrn.3285286

Jeffrey L. Furman

Boston University - Department of Strategy & Policy ( email )

595 Commonwealth Avenue
Boston, MA 02215
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Florenta Teodoridis (Contact Author)

University of Southern California - Marshall School of Business ( email )

701 Exposition Blvd
Los Angeles, CA 90089
United States

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