Disruptive development path measurement for emerging technologies based on the patent citation network
Xiaoli Wang,
Wenting Liang,
Xuanting Ye,
Lingdi Chen and
Yun Liu
Journal of Informetrics, 2024, vol. 18, issue 1
Abstract:
Studying disruptive innovation development paths for emerging technologies helps trace and grasp key core technologies development, promoting innovation and development in emerging technologies and industries. This paper measures the innovation development path for emerging technology, including: (1) improving the triple citation network and quantifying disruptive measurement by designing a technological disruption model; (2) proposing a contraction method for the citation network from the dataset perspective; (3) proposing a method to extract the main path using technology disruption degree as a criterion for citation networks importance; (4) taking the sintering technology in 3-D printing technology as the empirical object with 12,662 patent families from 1997 to 2019. The empirical results indicate that the disruption degree value is determined by the transitive citation relationship without the co-citation relationship, and the closed-loop structures are effectively removed, thereby reducing the size of the dataset. The proposed disruption quantification method can support effective evaluation of technological innovation levels and decision-making for the research and development (R&D) direction and resource allocation.
Keywords: Disruption degree; Development path; Patent citation network; Emerging technologies; Triplet (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1751157724000063
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:18:y:2024:i:1:s1751157724000063
DOI: 10.1016/j.joi.2024.101493
Access Statistics for this article
Journal of Informetrics is currently edited by Leo Egghe
More articles in Journal of Informetrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().