Exploring the influence of political connections and managerial overconfidence on R&D intensity in China's large-scale private sector firms

Wang, D.; Sutherland, D.; Ning, L.; Wang, Y.; Pan, X.

doi:10.1016/j.technovation.2017.10.007

Exploring the influence of political connections and managerial overconfidence on R&D intensity in China's large-scale private sector firms

Wang, D.; Sutherland, D.; Ning, L.; Wang, Y.; Pan, X.

Authors

D. Wang

Professor Dylan Sutherland dylan.sutherland@durham.ac.uk
Professor

L. Ning

Y. Wang

X. Pan

Abstract

Political ties and managerial cognitive biases, specifically overconfidence, have been identified as affecting firm-level R&D processes and outcomes. Here we further conceptually and empirically explore how these two factors may influence R&D intensity in an emerging market context. Our empirical results, based on panel data from 1293 Chinese publicly listed firms (between 2010 and 2014) show, contrary to some previous research, that stronger formal political ties somewhat reduce firm-level R&D intensity. Greater overconfidence in managers, by contrast, increases R&D intensity. Interestingly, moreover, overconfidence positively moderates the relationship between political ties and R&D intensity to the extent that the weak negative relationship becomes positive in the presence of overconfidence. Our results highlight the role of managerial mindset as an important determinant of R&D intensity in the emerging market context.

Citation

Wang, D., Sutherland, D., Ning, L., Wang, Y., & Pan, X. (2018). Exploring the influence of political connections and managerial overconfidence on R&D intensity in China's large-scale private sector firms. Technovation, 69, 40-53. https://doi.org/10.1016/j.technovation.2017.10.007

Journal Article Type	Article
Acceptance Date	Oct 28, 2017
Online Publication Date	Nov 3, 2017
Publication Date	Jan 1, 2018
Deposit Date	Nov 2, 2017
Publicly Available Date	Nov 3, 2019
Journal	Technovation
Print ISSN	0166-4972
Publisher	Elsevier
Peer Reviewed	Peer Reviewed
Volume	69
Pages	40-53
DOI	https://doi.org/10.1016/j.technovation.2017.10.007
Public URL	https://durham-repository.worktribe.com/output/1345188

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© 2017 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/