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A Methodology of Partner Selection for Sustainable Industry-University Cooperation Based on LDA Topic Model

Author

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  • Jiho Kang

    (Department of Industrial Management Engineering, Korea University, Seoul 02841, Korea)

  • Junseok Lee

    (Department of Industrial Management Engineering, Korea University, Seoul 02841, Korea)

  • Dongsik Jang

    (Department of Industrial Management Engineering, Korea University, Seoul 02841, Korea)

  • Sangsung Park

    (Division of Software Convergence, Cheongju University, Chungbuk 28503, Korea)

Abstract

In today’s knowledge-based society, industry-university cooperation (IUC) is recognized as an effective tool for technological innovation. Many studies have shown that selecting the right partner is essential to the success of the IUC. Although there have been a lot of studies on the criteria for selecting a suitable partner for IUC or strategic alliances, there has been a problem of making decisions depending on the qualitative judgment of experts or staff. While related works using patent analysis enabled the quantitative analysis and comparison of potential research partners, they overlooked the fact that there are several sub-technologies in one specific technology domain and that the applicant’s research concentration and competency are not the same for every sub-technology. This study suggests a systematic methodology that combines the Latent Dirichlet Allocation (LDA) topic model and the clustering algorithm in order to classify the sub-technology categories of a particular technology domain, and identifies the best college partners in each category. In addition, a similar-patent density (SPD) index was proposed and utilized for an objective comparison of potential university partners. In order to investigate the practical applicability of the proposed methodology, we conducted experiments using real patent data on the electric vehicle domain obtained from the Korean Intellectual Property Office. As a result, we identified 10 research and development sectors wherein Hyundai Motor Company (HMC) focuses using LDA and clustering. The universities with the highest values of SPD for each sector were chosen to be the most suitable partners of HMC for collaborative research.

Suggested Citation

  • Jiho Kang & Junseok Lee & Dongsik Jang & Sangsung Park, 2019. "A Methodology of Partner Selection for Sustainable Industry-University Cooperation Based on LDA Topic Model," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3478-:d:242669
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    References listed on IDEAS

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    2. Artūras Kaklauskas & Audrius Banaitis & Fernando A. F. Ferreira & João J. M. Ferreira & Dilanthi Amaratunga & Natalija Lepkova & Ieva Ubartė & Nerija Banaitienė, 2018. "An Evaluation System for University–Industry Partnership Sustainability: Enhancing Options for Entrepreneurial Universities," Sustainability, MDPI, vol. 10(1), pages 1-17, January.
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    Cited by:

    1. Chung, Jaemin & Ko, Namuk & Yoon, Janghyeok, 2021. "Inventor group identification approach for selecting university-industry collaboration partners," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    2. Jin Guo & Bingmei Gu & Xialing Sun & Jinli Xue & Baiyun Yuan, 2019. "Communication Barrier, Spillover Effect and Industrial-Technological Innovation," Sustainability, MDPI, vol. 11(18), pages 1-20, September.
    3. Jie Liang & Peng Shao, 2019. "Sequential Alliance Portfolios, Partner Reconfiguration and Firm Performance," Sustainability, MDPI, vol. 11(21), pages 1-20, October.
    4. Sergio Cuellar & Alberto Méndez-Morales & Milton M. Herrera, 2022. "Location Matters: a Novel Methodology for Patent’s National Phase Process," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(3), pages 2138-2163, September.
    5. Yuan, Xiaodong & Li, Xiaotao, 2021. "The evolution of the industrial value chain in China's high-speed rail driven by innovation policies: A patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    6. Yan Qi & Xin Zhang & Zhengyin Hu & Bin Xiang & Ran Zhang & Shu Fang, 2022. "Choosing the right collaboration partner for innovation: a framework based on topic analysis and link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5519-5550, September.
    7. Juhyun Lee & Jiho Kang & Sangsung Park & Dongsik Jang & Junseok Lee, 2020. "A Multi-Class Classification Model for Technology Evaluation," Sustainability, MDPI, vol. 12(15), pages 1-16, July.

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