IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v15y2021i2s1751157721000146.html
   My bibliography  Save this article

Identifying the impact of patent family on the patent trajectory: A case of thin film solar cells technological trajectories

Author

Listed:
  • Lai, Kuei-Kuei
  • Bhatt, Priyanka C.
  • Kumar, Vimal
  • Chen, Hsueh-Chen
  • Chang, Yu-Hsin
  • Su, Fang-Pei

Abstract

Incessant technology development and evolution has thrusted the need to recognize promising technological opportunities for organizations. Since patents contain information about an innovation, they are considered essential data sources for the development of new technology. The greater the size of international patent families, the more valuable they are. This study analyzed the technological changes in a technology domain by eliminating the effects on evolution path caused by patent families and discussed the implications of the technological path obtained from different parameters. This study builds on the assumption that the large number of patent family citations results in their increased influence, which affects the calculation of information flow in the path, and ultimately impacts the patent trajectory. An iterative process was employed in this study to reduce the effect of patent families, by establishing information flow, and map the main path network until the interference path from the main path was bifurcated. Technological innovation and the path phenomenon were identified through the key-route search attained using different paths. This study chose the case of thin-film solar technology and retrieved its patent data from the United States Patent and Trademark Office (USPTO) database. The Main Path Analysis (MPA) and the technological trajectories’ analysis were utilized. The weight values calculated by the self-citation of the patent family create an interference path which are then adjusted in the global search, and a new main path is generated. Through this adjustment, the accuracy rate of identifying technological evolution and breakthrough innovation is enhanced.

Suggested Citation

  • Lai, Kuei-Kuei & Bhatt, Priyanka C. & Kumar, Vimal & Chen, Hsueh-Chen & Chang, Yu-Hsin & Su, Fang-Pei, 2021. "Identifying the impact of patent family on the patent trajectory: A case of thin film solar cells technological trajectories," Journal of Informetrics, Elsevier, vol. 15(2).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:2:s1751157721000146
    DOI: 10.1016/j.joi.2021.101143
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157721000146
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2021.101143?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Juan Alcácer & Michelle Gittelman, 2006. "Patent Citations as a Measure of Knowledge Flows: The Influence of Examiner Citations," The Review of Economics and Statistics, MIT Press, vol. 88(4), pages 774-779, November.
    2. Comins, Jordan A. & Carmack, Stephanie A. & Leydesdorff, Loet, 2018. "Patent citation spectroscopy (PCS): Online retrieval of landmark patents based on an algorithmic approach," Journal of Informetrics, Elsevier, vol. 12(4), pages 1223-1231.
    3. Robinson, Douglas K.R. & Huang, Lu & Guo, Ying & Porter, Alan L., 2013. "Forecasting Innovation Pathways (FIP) for new and emerging science and technologies," Technological Forecasting and Social Change, Elsevier, vol. 80(2), pages 267-285.
    4. Jiaojiao Ji & George A. Barnett & Jianxun Chu, 2019. "Global networks of genetically modified crops technology: a patent citation network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 737-762, March.
    5. Jiang, Xiaorui & Zhuge, Hai, 2019. "Forward search path count as an alternative indirect citation impact indicator," Journal of Informetrics, Elsevier, vol. 13(4).
    6. Kim, Dong-hyu & Lee, Heejin & Kwak, Jooyoung, 2017. "Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: An investigation of the M2M/IoT patent network," Research Policy, Elsevier, vol. 46(7), pages 1234-1254.
    7. Junmo Kim & Juneseuk Shin, 2018. "Mapping extended technological trajectories: integration of main path, derivative paths, and technology junctures," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1439-1459, September.
    8. Ke, Qing, 2018. "Comparing scientific and technological impact of biomedical research," Journal of Informetrics, Elsevier, vol. 12(3), pages 706-717.
    9. Antoine Dechezleprêtre & Yann Ménière & Myra Mohnen, 2017. "International patent families: from application strategies to statistical indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 793-828, May.
    10. Peter Neuhäusler & Rainer Frietsch, 2013. "Patent families as macro level patent value indicators: applying weights to account for market differences," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 27-49, July.
    11. John S. Liu & Louis Y.Y. Lu, 2012. "An integrated approach for main path analysis: Development of the Hirsch index as an example," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(3), pages 528-542, March.
    12. Hiroko Nakamura & Shinji Suzuki & Yuya Kajikawa & Masataka Osawa, 2015. "The effect of patent family information in patent citation network analysis: a comparative case study in the drivetrain domain," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(2), pages 437-452, August.
    13. Kuan, Chung-Huei & Chen, Dar-Zen & Huang, Mu-Hsuan, 2019. "Bibliographically coupled patents: Their temporal pattern and combined relevance," Journal of Informetrics, Elsevier, vol. 13(4).
    14. John S. Liu & Louis Y. Y. Lu & Mei Hsiu-Ching Ho, 2019. "A few notes on main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 379-391, April.
    15. Donghyun You & Hyunseok Park, 2018. "Developmental Trajectories in Electrical Steel Technology Using Patent Information," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    16. D.K. Robinson & Lu Huang & Ying Guo & Alan L. Porter, 2013. "Forecasting Innovation Pathways (FIP) for new and emerging science and technologies," Post-Print hal-01071140, HAL.
    17. Michael Roach & Wesley M. Cohen, 2013. "Lens or Prism? Patent Citations as a Measure of Knowledge Flows from Public Research," Management Science, INFORMS, vol. 59(2), pages 504-525, October.
    18. Kuan, Chung-Huei & Huang, Mu-Hsuan & Chen, Dar-Zen, 2013. "Cross-field evaluation of publications of research institutes using their contributions to the fields’ MVPs determined by h-index," Journal of Informetrics, Elsevier, vol. 7(2), pages 455-468.
    19. Mei Hsiu-Ching Ho & Vincent H. Lin & John S. Liu, 2014. "Exploring knowledge diffusion among nations: a study of core technologies in fuel cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 149-171, July.
    20. Park, Inchae & Yoon, Byungun, 2018. "Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network," Journal of Informetrics, Elsevier, vol. 12(4), pages 1199-1222.
    21. Bekkers, Rudi & Martinelli, Arianna, 2012. "Knowledge positions in high-tech markets: Trajectories, standards, strategies and true innovators," Technological Forecasting and Social Change, Elsevier, vol. 79(7), pages 1192-1216.
    22. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    23. Lee, Taesoo D. & Ebong, Abasifreke U., 2017. "A review of thin film solar cell technologies and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1286-1297.
    24. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Carloni, Massimiliano, 2019. "The balance of knowledge flows," Journal of Informetrics, Elsevier, vol. 13(1), pages 1-9.
    25. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
    26. Wu, Ching-Yan & Mathews, John A., 2012. "Knowledge flows in the solar photovoltaic industry: Insights from patenting by Taiwan, Korea, and China," Research Policy, Elsevier, vol. 41(3), pages 524-540.
    27. Hills, Jeremy M. & Μichalena, Evanthie & Chalvatzis, Konstantinos J., 2018. "Innovative technology in the Pacific: Building resilience for vulnerable communities," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 16-26.
    28. Jae Ha Gwak & So Young Sohn, 2018. "A novel approach to explore patent development paths for subfield technologies," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(3), pages 410-419, March.
    29. Jang, Hyun Jin & Woo, Han-Gyun & Lee, Changyong, 2017. "Hawkes process-based technology impact analysis," Journal of Informetrics, Elsevier, vol. 11(2), pages 511-529.
    30. Wang, Lili & Jiang, Shan & Zhang, Shiyun, 2020. "Mapping technological trajectories and exploring knowledge sources: A case study of 3D printing technologies," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    31. Yu-Hsin Chang & Kuei-Kuei Lai & Chien-Yu Lin & Fang-Pei Su & Ming-Chung Yang, 2017. "A hybrid clustering approach to identify network positions and roles through social network and multivariate analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1733-1755, December.
    32. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    33. Jee, Su Jung & Kwon, Minji & Ha, Jung Moon & Sohn, So Young, 2019. "Exploring the forward citation patterns of patents based on the evolution of technology fields," Journal of Informetrics, Elsevier, vol. 13(4).
    34. Mina, A. & Ramlogan, R. & Tampubolon, G. & Metcalfe, J.S., 2007. "Mapping evolutionary trajectories: Applications to the growth and transformation of medical knowledge," Research Policy, Elsevier, vol. 36(5), pages 789-806, June.
    35. Harhoff, Dietmar & Scherer, Frederic M. & Vopel, Katrin, 2003. "Citations, family size, opposition and the value of patent rights," Research Policy, Elsevier, vol. 32(8), pages 1343-1363, September.
    36. Cohen, Wesley M. & Goto, Akira & Nagata, Akiya & Nelson, Richard R. & Walsh, John P., 2002. "R&D spillovers, patents and the incentives to innovate in Japan and the United States," Research Policy, Elsevier, vol. 31(8-9), pages 1349-1367, December.
    37. Douglas K. R. Robinson & Lu Huang & Yan Guo & Alan L. Porter, 2013. "Forecasting Innovation Pathways (FIP) for new and emerging science and technologies," Post-Print hal-01070417, HAL.
    38. Catalina Martínez, 2011. "Patent families: When do different definitions really matter?," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(1), pages 39-63, January.
    39. Gomes, Leonardo Augusto de Vasconcelos & Facin, Ana Lucia Figueiredo & Salerno, Mario Sergio & Ikenami, Rodrigo Kazuo, 2018. "Unpacking the innovation ecosystem construct: Evolution, gaps and trends," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 30-48.
    40. Feder, Christophe, 2018. "The effects of disruptive innovations on productivity," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 186-193.
    41. John S. Liu & Louis Y.Y. Lu, 2012. "An integrated approach for main path analysis: Development of the Hirsch index as an example," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(3), pages 528-542, March.
    42. Song, Kisik & Kim, Kyuwoong & Lee, Sungjoo, 2018. "Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 118-132.
    43. Huang, Mu-Hsuan & Yang, Hsiao-Wen & Chen, Dar-Zen, 2015. "Increasing science and technology linkage in fuel cells: A cross citation analysis of papers and patents," Journal of Informetrics, Elsevier, vol. 9(2), pages 237-249.
    44. Loet Leydesdorff, 2008. "Patent classifications as indicators of intellectual organization," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(10), pages 1582-1597, August.
    45. Francois P. Kabore & Walter G. Park, 2019. "Can patent family size and composition signal patent value?," Applied Economics, Taylor & Francis Journals, vol. 51(60), pages 6476-6496, December.
    46. Lee, Kyungpyo & Lee, Sungjoo, 2013. "Patterns of technological innovation and evolution in the energy sector: A patent-based approach," Energy Policy, Elsevier, vol. 59(C), pages 415-432.
    47. Chen, Lixin, 2017. "Do patent citations indicate knowledge linkage? The evidence from text similarities between patents and their citations," Journal of Informetrics, Elsevier, vol. 11(1), pages 63-79.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hu, Zewen & Zhou, Xiji & Lin, Angela, 2023. "Evaluation and identification of potential high-value patents in the field of integrated circuits using a multidimensional patent indicators pre-screening strategy and machine learning approaches," Journal of Informetrics, Elsevier, vol. 17(2).
    2. Mohd Shadab Danish & Pritam Ranjan & Ruchi Sharma, 2022. "Assessing the Impact of Patent Attributes on the Value of Discrete and Complex Innovations," Papers 2208.07222, arXiv.org.
    3. Jianhua Hou & Xiucai Yang & Haoyang Song & Haiyue Yao, 2023. "Will patent family be dormant? Research on the identification and characteristics of sleeping beauty’s patent family," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5361-5387, October.
    4. Mohd Shadab Danish & Pritam Ranjan & Ruchi Sharma, 2021. "Identification of “Valuable” Technologies via Patent Statistics in India: An Analysis Based on Renewal Information," BASE University Working Papers 13/2021, BASE University, Bengaluru, India.
    5. Yu, Dejian & Sheng, Libo, 2021. "Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks," Journal of Informetrics, Elsevier, vol. 15(4).
    6. Yu, Dejian & Yan, Zhaoping, 2023. "Main path analysis considering citation structure and content: Case studies in different domains," Journal of Informetrics, Elsevier, vol. 17(1).
    7. Hou, Jianhua & Tang, Shiqi & Zhang, Yang & Song, Haoyang, 2023. "Does prior knowledge affect patent technology diffusion? A semantic-based patent citation contribution analysis," Journal of Informetrics, Elsevier, vol. 17(2).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lai, Kuei-Kuei & Chen, Yu-Long & Kumar, Vimal & Daim, Tugrul & Verma, Pratima & Kao, Fang-Chen & Liu, Ruirong, 2023. "Mapping technological trajectories and exploring knowledge sources: A case study of E-payment technologies," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    2. Bhatt, Priyanka C. & Lai, Kuei-Kuei & Drave, Vinayak A. & Lu, Tzu-Chuen & Kumar, Vimal, 2023. "Patent analysis based technology innovation assessment with the lens of disruptive innovation theory: A case of blockchain technological trajectories," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    3. Kuan, Chung-Huei & Lin, Jia-Tian & Chen, Dar-Zen, 2021. "Characterizing Patent Assignees by Their Structural Positions Relative to a Field’s Evolutionary Trajectory," Journal of Informetrics, Elsevier, vol. 15(4).
    4. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
    5. Alessandri, Enrico, 2023. "Identifying technological trajectories in the mining sector using patent citation networks," Resources Policy, Elsevier, vol. 80(C).
    6. Hwang, Seonho & Shin, Juneseuk, 2019. "Extending technological trajectories to latest technological changes by overcoming time lags," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 142-153.
    7. Flavia Filippin, 2021. "Do main paths reflect technological trajectories? Applying main path analysis to the semiconductor manufacturing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6443-6477, August.
    8. Dejian Yu & Zhaoping Yan, 2022. "Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4251-4274, July.
    9. Mohd Shadab Danish & Pritam Ranjan & Ruchi Sharma, 2022. "Assessing the Impact of Patent Attributes on the Value of Discrete and Complex Innovations," Papers 2208.07222, arXiv.org.
    10. Mohd Shadab Danish & Pritam Ranjan & Ruchi Sharma, 2021. "Identification of “Valuable” Technologies via Patent Statistics in India: An Analysis Based on Renewal Information," BASE University Working Papers 13/2021, BASE University, Bengaluru, India.
    11. Wang, Lili & Jiang, Shan & Zhang, Shiyun, 2020. "Mapping technological trajectories and exploring knowledge sources: A case study of 3D printing technologies," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    12. Yu, Dejian & Yan, Zhaoping, 2023. "Main path analysis considering citation structure and content: Case studies in different domains," Journal of Informetrics, Elsevier, vol. 17(1).
    13. Xiaorui Jiang & Junjun Liu, 2023. "Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(5), pages 546-569, May.
    14. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Xu, Haiyun & Yang, Guancan, 2022. "A semantic main path analysis method to identify multiple developmental trajectories," Journal of Informetrics, Elsevier, vol. 16(2).
    15. Kuan, Chung-Huei & Chen, Dar-Zen & Huang, Mu-Hsuan, 2020. "The overlooked citations: Investigating the impact of ignoring citations to published patent applications," Journal of Informetrics, Elsevier, vol. 14(1).
    16. John S. Liu & Louis Y. Y. Lu & Mei Hsiu-Ching Ho, 2019. "A few notes on main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 379-391, April.
    17. Feng, Sida & Magee, Christopher L., 2020. "Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees," Applied Energy, Elsevier, vol. 260(C).
    18. Coccia, Mario & Wang, Lili, 2015. "Path-breaking directions of nanotechnology-based chemotherapy and molecular cancer therapy," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 155-169.
    19. Ying Huang & Donghua Zhu & Yue Qian & Yi Zhang & Alan L. Porter & Yuqin Liu & Ying Guo, 2017. "A hybrid method to trace technology evolution pathways: a case study of 3D printing," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 185-204, April.
    20. Song, Haoyang & Hou, Jianhua & Zhang, Yang, 2023. "The measurements and determinants of patent technological value: Lifetime, strength, breadth, and dispersion from the technology diffusion perspective," Journal of Informetrics, Elsevier, vol. 17(1).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:15:y:2021:i:2:s1751157721000146. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.