Jee, S.J. orcid.org/0000-0001-9582-8289 and Sohn, S.Y. orcid.org/0000-0002-3958-2269 (2023) A firm’s creation of proprietary knowledge linked to the knowledge spilled over from its research publications: the case of artificial intelligence. Industrial and Corporate Change, 32 (4). pp. 876-900. ISSN: 0960-6491
Abstract
This study investigates the mechanism by which knowledge spilled over from a firm’s research publication consequently spills into the focal firm as a form of proprietary knowledge when it is engaged in an emerging science-related technology. We define the knowledge spillover pool (KSP) as an evolving group of papers citing a paper published by a firm. Focusing on the recent development of artificial intelligence, on which firms have published actively, we compare the KSP conditions related to the increase in patents created by the focal firm with those created by external actors. Using a Cox regression and subsequent contrast test, we find that both an increasing KSP and an increasing similarity between the idea published by the focal firm and KSP are positively related to the proprietary knowledge creation of both the focal firm and external actors, with such relations being significantly stronger for the focal firm than for external actors. On the contrary, an increasing proportion of industry papers in the KSP are positively associated with the proprietary knowledge creation not only by the focal firm but also by external actors to a similar degree. We contribute to the literature on selective revealing and to the firms’ publishing strategies.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2023. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Analytics, Technology & Ops Department |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Jul 2025 13:27 |
Last Modified: | 25 Jul 2025 13:27 |
Status: | Published |
Publisher: | Oxford University Press |
Identification Number: | 10.1093/icc/dtad002 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:229445 |