Exploring the effect of structural patent indicators in forward patent citation networks on patent price from firm market value

Abstract

This paper introduces structural patent indicators in the forward patent citation networks (FPCNs). Using the estimates of their relationship with firm market value, it explores the effect of the structural patent indicators on patent price. When applied to US smartphone industry and US drug and biotechnology industry, the common finding is that a firm's registered patent gets higher price if influenced faster by the previously registered patents of the same firm through self-citations. The empirical results imply that merely counting forward citations, shown related to patent price by the previous works, is insufficient; the structural properties in FPCNs are essential for the more accurate estimation of patent price. They also show that the relationships between structural patent indicators in the FPCNs and patent price are different between industries. It urges the necessity of selecting structural patent indicators, well fitted to each industry, for the better patent valuation using forward citations.

Keywords:

• forward patent citation networks
• structural patent indicators
• patent price
• panel data econometrics
• US smartphone industry
• US drug and biotechnology industry

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes on contributor

Dr Jong Hwan Suh is currently a senior research scientist of the Research Center for Future Strategy in Graduate School of Future Strategy at Korea Advanced Institute of Science and Technology (KAIST). He received his PhD, MS, and BS from Industrial & Systems Engineering at KAIST. He has professional experience as a senior researcher and manager at Technology Management Division of Samsung SDS Co., Ltd. He was also a visiting scholar of AI Lab, headed by Dr Hsinchun Chen, in the MIS Department at the University of Arizona. His research works include data/text mining, machine learning, operation research, econometrics, sentiment analysis, and social network analysis for data science in information systems, technology and service management, politics, future studies, and their interdisciplinary areas.

ORCID

Jong Hwan Suh http://orcid.org/0000-0002-5465-1103

Additional information

Funding

This work was supported by National Research Foundation of Korea Grant NRF-357-2011-B00002, funded by the Korean Government.