Hierarchical main path analysis to identify decompositional multi-knowledge trajectories. Issue 2 (12th June 2020)

• Record Type: Journal Article
• Authors: Yoon, Sejun; Mun, Changbae; Raghavan, Nagarajan; Hwang, Dongwook; Kim, Sohee; Park, Hyunseok
• Is Part Of: Journal of knowledge management. Volume 25:Issue 2(2021)
• Page Start: 454
• Page End: 476
• Publication Date: 2020-06-12
• Subjects: Technological trajectories -- Technology decomposition -- Knowledge persistence -- Citation network -- Knowledge network -- Technological trends; Knowledge management -- Periodicals; 658.4
• DOI: 10.1108/JKM-01-2020-0030
• Languages: English
• ISSNs: 1367-3270

Abstract

Abstract : Purpose: The purpose of this paper is to propose a quantitative method for identifying multiple and hierarchical knowledge trajectories within a specific technological domain (TD). Design/methodology/approach: The proposed method as a patent-based data-driven approach is basically based on patent classification systems and patent citation information. Specifically, the method first analyzes hierarchical structure under a specific TD based on patent co-classification and hierarchical relationships between patent classifications. Then, main paths for each sub-TD and overall-TD are generated by knowledge persistence-based main path approach. The all generated main paths at different level are integrated into the hierarchical main paths. Findings: This paper conducted an empirical analysis by using Genome sequencing technology. The results show that the proposed method automatically identifies three sub-TDs, which are major functionalities in the TD, and generates the hierarchical main paths. The generated main paths show knowledge flows across different sub-TDs and the changing trends in dominant sub-TD over time. Originality/value: To the best of the authors' knowledge, the proposed method is the first attempt to automatically generate multiple hierarchical main paths using patent data. The generated main paths objectively show not only knowledge trajectories for each sub-TD but also interactive knowledge flows among sub-TDs. Therefore, the method is definitely helpful to reduce manual work for TD decomposition and useful to understand major trajectories for TD.