IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i20p14724-d1257344.html
   My bibliography  Save this article

Exploring Potential R&D Collaboration Partners Using Embedding of Patent Graph

Author

Listed:
  • Juhyun Lee

    (Institute of Engineering Research, Korea University, Seoul 02841, Republic of Korea)

  • Sangsung Park

    (Department of Data Science, Cheongju University, Cheongju 28503, Republic of Korea)

  • Junseok Lee

    (Department of AI Convergence Engineering, College of Engineering, Kangnam University, Youngin 16979, Republic of Korea)

Abstract

Rapid market change is one of the reasons for accelerating a technology lifecycle. Enterprises have socialized, externalized, combined, and internalized knowledge for their survival. However, the current era requires ambidextrous innovation through the diffusion of knowledge from enterprises. Accordingly, enterprises have discovered sustainable resources and increased market value through collaborations with research institutions and universities. Such collaborative activities effectively improve enterprise innovation, economic growth, and national competence. However, as such collaborations are conducted continuously and iteratively, their effect has gradually weakened. Therefore, we focus on exploring potential R&D collaboration partners through patents co-owned by enterprises, research institutions, and universities. The business pattern of co-applicants is extracted through a patent graph, and potential R&D collaboration partners are unearthed. In this paper, we propose a method of converting a co-applicant-based graph into a vector using representation learning. Our purpose is to explore potential R&D collaboration partners from the similarity between vectors. Compared to other methods, the proposed method contributes to discovering potential R&D collaboration partners based on organizational features. The following questions are considered in order to discover potential R&D partners in collaborative activities: Can information about co-applicants of patents satisfactorily explain R&D collaboration? Conversely, can potential R&D collaboration partners be discovered from co-applicants? To answer these questions, we conducted experiments using autonomous-driving-related patents. We verified that our proposed method can explore potential R&D collaboration partners with high accuracy through experiments.

Suggested Citation

  • Juhyun Lee & Sangsung Park & Junseok Lee, 2023. "Exploring Potential R&D Collaboration Partners Using Embedding of Patent Graph," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14724-:d:1257344
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/20/14724/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/20/14724/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Janghyeok Yoon & Hyunseok Park & Kwangsoo Kim, 2013. "Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 313-331, January.
    2. Antonio Messeni Petruzzelli & Gianluca Murgia, 2020. "University–Industry collaborations and international knowledge spillovers: a joint-patent investigation," The Journal of Technology Transfer, Springer, vol. 45(4), pages 958-983, August.
    3. Antonio Capaldo, 2007. "Network structure and innovation: The leveraging of a dual network as a distinctive relational capability," Strategic Management Journal, Wiley Blackwell, vol. 28(6), pages 585-608, June.
    4. Chen, Kun & Kenney, Martin, 2007. "Universities/Research Institutes and Regional Innovation Systems: The Cases of Beijing and Shenzhen," World Development, Elsevier, vol. 35(6), pages 1056-1074, June.
    5. Gianluca Murgia, 2021. "The impact of collaboration diversity and joint experience on the reiteration of university co-patents," The Journal of Technology Transfer, Springer, vol. 46(4), pages 1108-1143, August.
    6. Hu, Fang & Liu, Jia & Li, Liuhuan & Liang, Jun, 2020. "Community detection in complex networks using Node2vec with spectral clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    7. Ardito, Lorenzo & D'Adda, Diego & Messeni Petruzzelli, Antonio, 2018. "Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 317-330.
    8. Changyong Lee & Gyumin Lee, 2019. "Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 603-632, November.
    9. Obradović, Tena & Vlačić, Božidar & Dabić, Marina, 2021. "Open innovation in the manufacturing industry: A review and research agenda," Technovation, Elsevier, vol. 102(C).
    10. Mario Maggioni & Teodora Uberti, 2009. "Knowledge networks across Europe: which distance matters?," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(3), pages 691-720, September.
    11. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    12. Yoon, Janghyeok & Park, Hyunseok & Seo, Wonchul & Lee, Jae-Min & Coh, Byoung-youl & Kim, Jonghwa, 2015. "Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 153-167.
    13. Ikujiro Nonaka, 1994. "A Dynamic Theory of Organizational Knowledge Creation," Organization Science, INFORMS, vol. 5(1), pages 14-37, February.
    14. Erwin Danneels, 2007. "The process of technological competence leveraging," Strategic Management Journal, Wiley Blackwell, vol. 28(5), pages 511-533, May.
    15. Yi Zhang & Yue Qian & Ying Huang & Ying Guo & Guangquan Zhang & Jie Lu, 2017. "An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1925-1946, June.
    16. Kim, Chang-Su & Inkpen, Andrew C., 2005. "Cross-border R&D alliances, absorptive capacity and technology learning," Journal of International Management, Elsevier, vol. 11(3), pages 313-329, September.
    17. Briggs, Kristie, 2015. "Co-owner relationships conducive to high quality joint patents," Research Policy, Elsevier, vol. 44(8), pages 1566-1573.
    18. Xia Gao & Jiancheng Guan & Ronald Rousseau, 2011. "Mapping collaborative knowledge production in China using patent co-inventorships," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 343-362, August.
    Full references (including those not matched with items on IDEAS)

    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. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    2. Wei Chen & Hui Qu & Kuo Chi, 2021. "Partner Selection in China Interorganizational Patent Cooperation Network Based on Link Prediction Approaches," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    3. Choi, Jaewoong & Lee, Changyong & Yoon, Janghyeok, 2023. "Exploring a technology ecology for technology opportunity discovery: A link prediction approach using heterogeneous knowledge graphs," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    4. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    5. Xuan Shi & Lingfei Cai & Hongfang Song, 2019. "Discovering Potential Technology Opportunities for Fuel Cell Vehicle Firms: A Multi-Level Patent Portfolio-Based Approach," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    6. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    7. Wang, Yunmin & Cao, Guohua & Yan, Youliang & Wang, Jingjing, 2022. "Does high-speed rail stimulate cross-city technological innovation collaboration? Evidence from China," Transport Policy, Elsevier, vol. 116(C), pages 119-131.
    8. Xiaotian Yang, 2022. "Coopetition for innovation in R&D consortia: Moderating roles of size disparity and formal interaction," Asia Pacific Journal of Management, Springer, vol. 39(1), pages 79-102, March.
    9. Yindan Ye & Kevin De Moortel & Thomas Crispeels, 2020. "Network dynamics of Chinese university knowledge transfer," The Journal of Technology Transfer, Springer, vol. 45(4), pages 1228-1254, August.
    10. Prokop, Daniel, 2021. "University entrepreneurial ecosystems and spinoff companies: Configurations, developments and outcomes," Technovation, Elsevier, vol. 107(C).
    11. Byeongki Jeong & Janghyeok Yoon, 2017. "Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information," Sustainability, MDPI, vol. 9(4), pages 1-22, March.
    12. Antonio Messeni Petruzzelli & Gianluca Murgia, 2020. "University–Industry collaborations and international knowledge spillovers: a joint-patent investigation," The Journal of Technology Transfer, Springer, vol. 45(4), pages 958-983, August.
    13. An, Jaehyeong & Kim, Kyuwoong & Mortara, Letizia & Lee, Sungjoo, 2018. "Deriving technology intelligence from patents: Preposition-based semantic analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 217-236.
    14. Hortovanyi, Lilla & Szabo, Roland Zs & Fuzes, Peter, 2021. "Extension of the strategic renewal journey framework: The changing role of middle management," Technology in Society, Elsevier, vol. 65(C).
    15. Lee, MyoungHoon & Kim, Suhyeon & Kim, Hangyeol & Lee, Junghye, 2022. "Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    16. Yutao Sun & Kai Liu, 2016. "Proximity effect, preferential attachment and path dependence in inter-regional network: a case of China’s technology transaction," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 201-220, July.
    17. Giovanni Abramo & Francesca Apponi & Ciriaco Andrea D’Angelo, 2022. "The geographic proximity effect on domestic cross-sector vis-à-vis intra-sector research collaborations," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3505-3521, June.
    18. Yan Yan & Jiancheng Guan, 2018. "How multiple networks help in creating knowledge: evidence from alternative energy patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 51-77, April.
    19. Qadri, Mubashar & Mamoon, Dawood, 2016. "Creating Shared Value: Social Capital as a Source to Drive Next Wave of Innovation for Socioeconomic Revenues," MPRA Paper 72554, University Library of Munich, Germany.
    20. Giovanni Abramo & Francesca Apponi & Ciriaco Andrea D'Angelo, 2022. "The geographic proximity effect on domestic cross-sector vis-a-vis intra-sector research collaborations," Papers 2202.10347, arXiv.org.

    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:gam:jsusta:v:15:y:2023:i:20:p:14724-:d:1257344. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.