Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2023
Abstract
This paper draws upon resource dependence theory and investigates how trade policy uncertainty affects firm strategic innovation management in China. Adopting a machine learning approach called Word2Vec from computational linguistics, we construct and validate a measure of firm-level managers’ perceived trade war uncertainty (TWU). We find that TWU 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 innovation behavior when the underlying firms are more financially constrained, and/or when the management is more myopic. In addition, we open the behavioral black box of firms’ strategic innovation and demonstrate that patents can be employed opportunistically to meet government policies to further attract more government subsidies.
Discipline
Databases and Information Systems | Technology and Innovation
Research Areas
Data Science and Engineering
Publication
AMCIS 2023: Panama, August 10-12
First Page
1
Last Page
10
Publisher
AIS
City or Country
Atlanta, GA
Citation
YANG, Xu; HU, Nan; and LIANG, Peng.
Corporate trade war uncertainty and patent bubble. (2023). AMCIS 2023: Panama, August 10-12. 1-10.
Available at: https://ink.library.smu.edu.sg/sis_research/9322
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://aisel.aisnet.org/amcis2023/sig_dite/sig_dite/2