Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach
The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have hig...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Public Library of Science
2017
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| Online Access: | http://hdl.handle.net/1721.1/110026 https://orcid.org/0000-0001-5316-8358 |
| _version_ | 1811085310699765760 |
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| author | Park, Hyunseok Magee, Christopher L |
| author2 | Massachusetts Institute of Technology. Institute for Data, Systems, and Society |
| author_facet | Massachusetts Institute of Technology. Institute for Data, Systems, and Society Park, Hyunseok Magee, Christopher L |
| author_sort | Park, Hyunseok |
| collection | MIT |
| description | The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method can dramatically reduce network complexity without missing any dominantly important patents. The main paths identified by our approach for two test cases are almost 10x less complex than the main paths identified by the existing approach. The proposed approach identifies all dominantly important patents on the main paths, but the main paths identified by the existing approach miss about 20% of dominantly important patents. |
| first_indexed | 2024-09-23T13:06:56Z |
| format | Article |
| id | mit-1721.1/110026 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:06:56Z |
| publishDate | 2017 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/1100262022-10-01T13:09:15Z Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach Park, Hyunseok Magee, Christopher L Massachusetts Institute of Technology. Institute for Data, Systems, and Society SUTD-MIT International Design Centre (IDC) Park, Hyunseok Magee, Christopher L The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method can dramatically reduce network complexity without missing any dominantly important patents. The main paths identified by our approach for two test cases are almost 10x less complex than the main paths identified by the existing approach. The proposed approach identifies all dominantly important patents on the main paths, but the main paths identified by the existing approach miss about 20% of dominantly important patents. 2017-06-19T19:54:03Z 2017-06-19T19:54:03Z 2016-10 2017-01 Article http://purl.org/eprint/type/JournalArticle 1932-6203 http://hdl.handle.net/1721.1/110026 Park, Hyunseok and Magee, Christopher L. “Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach.” Edited by Zhong-Ke Gao. PLOS ONE 12, no. 1 (January 2017): e0170895 © 2017 Park, Magee https://orcid.org/0000-0001-5316-8358 en_US http://dx.doi.org/10.1371/journal.pone.0170895 PLoS ONE Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science PLoS |
| spellingShingle | Park, Hyunseok Magee, Christopher L Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach |
| title | Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach |
| title_full | Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach |
| title_fullStr | Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach |
| title_full_unstemmed | Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach |
| title_short | Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach |
| title_sort | tracing technological development trajectories a genetic knowledge persistence based main path approach |
| url | http://hdl.handle.net/1721.1/110026 https://orcid.org/0000-0001-5316-8358 |
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