Patent Citations and Patent Value: Through the Lens of a Social Network Approach

Abstract

Timely identifying valuable technological inventions for further development and commercialization is an important strategic issue for organizations to maximize social welfare and return on investments. Inspired by the accuracy of social network approach in predicting performance in short investigation periods, this study represents one of the first attempts to creatively associate social network structural indicators with patent value. In particular, structural indicators, such as structural holes, size, density, hierarchy, clustering, and centrality, in patent backward, forward, and comprehensive citation networks are differently associated with knowledge generation and diffusion, i.e. patent value. The proposed approach arguably would alleviate truncation problem suffered by existing patent evaluation methods based only on forward citation counts. This study contributes to patent economics literature and research fields that would benefit from a more accurate measure of valuable technological inventions, such as technology and innovation management, strategic management, and organizational learning fields. The proposed approach also has practical and commercial value to research institutes and industrial firms.