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Quantitative identification of technological paradigm changes using knowledge persistence
Author(s) -
Changbae Mun,
Sejun Yoon,
Yongmin Kim,
Nagarajan Raghavan,
Hyun-Seok Park
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0220819
Subject(s) - persistence (discontinuity) , identification (biology) , paradigm shift , data science , citation , computer science , domain (mathematical analysis) , technological change , knowledge management , biology , artificial intelligence , epistemology , engineering , ecology , world wide web , mathematics , mathematical analysis , philosophy , geotechnical engineering
This paper proposes a method to quantitatively identify the changes of technological paradigm over time. Specifically, the method identifies previous paradigms and predicts future paradigms by analyzing a patent citation-based knowledge network. The technological paradigm can be considered as dominantly important knowledge in a specific period. Therefore, we adopted the knowledge persistence which can quantify technological impact of an invention to recent technologies in a knowledge network. High knowledge persistence patents are dominant or paradigmatic inventions in a specific period and so changes of top knowledge persistence patents over time can show paradigm shifts. Moreover, since knowledge persistence of paradigmatic inventions are increasing dramatically faster than other ordinary inventions, recent patents having similar increasing trends in knowledge persistence with previous paradigms are identified as future paradigm inventions. We conducted an empirical case study using patents related to the genome sequencing technology. The results show that the identified previous paradigms are widely recognized as critical inventions in the domain by other studies and the identified future paradigms are also qualitatively significant inventions as promising technologies.

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