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

【Author】 Bhatt, Priyanka C. C.; Lu, Tzu-Chuen

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】Organizations strive to achieve technological competence in the current era of inevitable technological progress. 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 relationship 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.

【Keywords】Patents; Technological innovation; Organizations; Knowledge engineering; Position measurement; Trajectory; Research and development; Patent analysis; innovation assessment; k-means clustering; patent centrality analysis; social network analysis

【发表时间】2023

【收录时间】2023-05-04

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-企业管理