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WINNING LONG AND FAR: PUBLICATIONS AND LONG-TERM INNOVATION PERFORMANCE OF ARTIFICIAL INTELLIGENCE FIRMS

Author
Shen, Xirong
Abstract
My dissertation examines the consequences of open-science publications for firms in artificial intelligence sectors. I develop and empirically test a theory of the impact of publications on a firm’s ability to shape external knowledge sources, innovate cumulatively, and realize long-term, generative value from their inventions. Publications trigger a “broadcast search” process that helps a firm attract a diverse pool of highly original external follow-up inventions. Moreover, publications serve as a common knowledge interface that facilitate the inventing firm’s learning from the external follow-up inventions. As a result, the focal firm not only creates more internal cumulative inventions, but also inventions from a broader spectrum of technology fields and of higher private value. I further explore various contingencies in the benefits of publications. Firms differ in their ability to benefit from the publication-triggered learning: diversification in a firm’s upstream technological assets facilitates the firm to learn more effectively, while diversification in a firm’s downstream industry scope enable the firm to capture more financial profits from publications. Empirical analyses based on publication and patenting activities of 237 AI-active publicly-traded firms in the U.S. provides support for my theoretical arguments.
Description
126 pages
Date Issued
2021-05Subject
corporate publications; general-purpose technology; technology innovation
Committee Chair
Sine, Wesley
Committee Member
Ahuja, Gautam; Tolbert, Pamela S.
Degree Discipline
Management
Degree Name
Ph. D., Management
Degree Level
Doctor of Philosophy
Type
dissertation or thesis