Trade policy uncertainty and the patent bubble in China: evidence from machine learning
Publication Type
Journal Article
Publication Date
1-2024
Abstract
This paper draws upon resource dependence theory and investigates how trade policy uncertainty affects firm strategic innovation management in China. Adopting a novel machine learning approach called Word2Vec, we construct and validate a measure of firm-level managers’ perceived trade policy uncertainty (TPU). We find that TPU has a positive effect on the number of total patent applications, but this positive effect is totally driven by low-quality patents instead of high-quality patents. Moreover, we document that firms have stronger incentives for such strategic inno-vation behavior when the underlying firms are more financially con-strained, and/or when the management is more myopic.
Keywords
Machine learning, patent bubble, Resource dependence theory, strategic innovation management, Trade policy uncertainty
Discipline
Asian Studies | Databases and Information Systems
Research Areas
Information Systems and Management
Publication
Asia-Pacific Journal of Accounting & Economics
First Page
1
Last Page
22
ISSN
1608-1625
Identifier
10.1080/16081625.2023.2298934
Publisher
Taylor and Francis Group
Citation
XUE, Xingnan; LIANG, Peng; XUE, Fujing; HU, Nan; and LIU, Ling.
Trade policy uncertainty and the patent bubble in China: evidence from machine learning. (2024). Asia-Pacific Journal of Accounting & Economics. 1-22.
Available at: https://ink.library.smu.edu.sg/sis_research/8662
Copyright Owner and License
Authors
Additional URL
https://doi.org/10.1080/16081625.2023.2298934