Skip to main content
Journal of Evolutionary EconomicsVolume 24, Issue 3, July 2014, Pages 623-652

Measuring knowledge persistence: A genetic approach to patent citation networks(Article)

  Save all to author list
  • aLEM (Laboratory of Economics and Management), Scuola Superiore Sant'Anna, Piazza Martiri della Libertá 33, 56127 Pisa, Italy
  • bSchool of Innovation Sciences, Eindhoven University of Technology, 5600 MB, Eindhoven, Netherlands

Abstract

The aim of this paper is to propose a new empirical method for identifying technologically important patents within a patent citation network and to apply it to the telecommunication switching industry. The method proposed is labelled the genetic approach, as it is inspired by population genetics: as geneticists are interested in studying patterns of migration and therefore the common origins of people, in innovation studies we are interested in tracing the origin and the evolution of today knowledge. In the context of patent and citation networks, this is done by calculating the patent's persistence index, i.e., decomposing patent's knowledge applying the Mendelian law of gene inheritance. This draws on the idea that the more a patent is related (through citations) to "descendent" patents, the more it affects future technological development and therefore its contribution persists in the technology. Results show that the method proposed is successful in reducing the number of both nodes and links considered. Furthermore, our method is indeed successful in identifying technological discontinuities where previous knowledge is not relevant for current technological development. © 2014 Springer-Verlag Berlin Heidelberg.

Author keywords

Patent citation networkPatent dataTechnology dynamicsTelecommunication manufacturing industry
  • ISSN: 09369937
  • Source Type: Journal
  • Original language: English
  • DOI: 10.1007/s00191-014-0349-5
  • Document Type: Article
  • Publisher: Springer New York LLC

  Martinelli, A.; LEM (Laboratory of Economics and Management), Scuola Superiore Sant'Anna, Piazza Martiri della Libertá 33, Italy;
© Copyright 2014 Elsevier B.V., All rights reserved.

Cited by 28 documents

Yoon, S. , Mun, C. , Raghavan, N.
Hierarchical main path analysis to identify decompositional multi-knowledge trajectories
(2021) Journal of Knowledge Management
Perez-Molina, E. , Loizides, F.
Novel data structure and visualization tool for studying technology evolution based on patent information: The DTFootprint and the TechSpectrogram
(2021) World Patent Information
Mun, C. , Yoon, S. , Raghavan, N.
Function score-based technological trend analysis
(2021) Technovation
View details of all 28 citations
{"topic":{"name":"Technology Roadmapping; Patent Analysis; Technological Competitiveness","id":5722,"uri":"Topic/5722","prominencePercentile":98.074455,"prominencePercentileString":"98.074","overallScholarlyOutput":0},"dig":"d43be3da057ee89a701c1bbef465d9cf31391903656f486a256cf4ca0f88a8ce"}

SciVal Topic Prominence

Topic:
Prominence percentile: