Identifying Firm Significance and Positions in the Patent Innovation Based on Centrality Measures’ Clustering Approach

Abstract

In the current era of inevitable technological progress, organizations strive to achieve technological competence. One way to measure the adaptability of firms to huge technological shifts is through various parameters, including patenting activities. This study presents a method for identifying the significance of firms in an innovation network using patent citation analysis and centrality measures. Specifically, the study employs k-means clustering to classify firms into similar clusters based on Network-based centrality measures such as betweenness, closeness, and eigenvector centrality. The study then develops a cluster relational network by establishing a cluster adjacency network and identifying firm positions within and between clusters. By examining the relation between clusters, the cluster network identifies the significance of firms. The study identifies four positions, namely, leader, follower, knowledge inertia, and significantly emerging, that align with the status of firms in patenting innovation capability. The method is implemented using blockchain technology as a case study. The novelty of the study lies in the structured approach to identifying firm significance by adding another layer of adjacency network to existing patent citation analysis techniques.