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Ensemble Modeling for Sustainable Technology Transfer

Author

Listed:
  • Junseok Lee

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

  • Ji-Ho Kang

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

  • Sunghae Jun

    (Department of Big Data and Statistics, Cheongju University, Chungbuk 28503, Korea)

  • Hyunwoong Lim

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

  • Dongsik Jang

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

  • Sangsung Park

    (Graduate School of Management of Technology, Korea University, Seoul 02841, Korea)

Abstract

These days, technological advances are being made through technological conversion. Following this trend, companies need to adapt and secure their own sustainable technological strategies. Technology transfer is one such strategy. This method is especially effective in coping with recent technological developments. In addition, universities and research institutes are able to secure new research opportunities through technology transfer. The aim of our study is to provide a technology transfer prediction model for the sustainable growth of companies. In the proposed method, we first collected patent data from a Korean patent information service provider. Next, we used latent Dirichlet allocation, which is a topic modeling method used to identify the technical field of the collected patents. Quantitative indicators on the patent data were also extracted. Finally, we used the variables that we obtained to create a technology transfer prediction model using the AdaBoost algorithm. The model was found to have sufficient classification performance. It is expected that the proposed model will enable universities and research institutes to secure new technology development opportunities more efficiently. In addition, companies using this model can maintain sustainable growth in line, coping with the changing pace of society.

Suggested Citation

  • Junseok Lee & Ji-Ho Kang & Sunghae Jun & Hyunwoong Lim & Dongsik Jang & Sangsung Park, 2018. "Ensemble Modeling for Sustainable Technology Transfer," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2278-:d:155675
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    References listed on IDEAS

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    3. Adriano Mesquita Soares & João Luiz Kovaleski & Silvia Gaia & Daiane Maria de Genaro Chiroli, 2020. "Building Sustainable Development through Technology Transfer Offices: An Approach Based on Levels of Maturity," Sustainability, MDPI, vol. 12(5), pages 1-22, February.
    4. Jagriti Singh & Krishan Kumar Pandey & Anil Kumar & Farheen Naz & Sunil Luthra, 2023. "Drivers, barriers and practices of net zero economy: An exploratory knowledge based supply chain multi-stakeholder perspective framework," Operations Management Research, Springer, vol. 16(3), pages 1059-1090, September.
    5. Lisa Craiut & Constantin Bungau & Paul Andrei Negru & Tudor Bungau & Andrei-Flavius Radu, 2022. "Technology Transfer in the Context of Sustainable Development—A Bibliometric Analysis of Publications in the Field," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    6. Won Jun Choe & Ilyong Ji, 2019. "The Performance of Supply-Push Versus Demand-Pull Technology Transfer and the Role of Technology Marketing Strategies: The Case of a Korean Public Research Institute," Sustainability, MDPI, vol. 11(7), pages 1-18, April.
    7. Sunghae Jun, 2019. "Bayesian Structural Time Series and Regression Modeling for Sustainable Technology Management," Sustainability, MDPI, vol. 11(18), pages 1-12, September.
    8. Youngho Kim & Sangsung Park & Junseok Lee & Dongsik Jang & Jiho Kang, 2021. "Integrated Survival Model for Predicting Patent Litigation Hazard," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    9. Farheen Naz & Anil Kumar & Abhijit Majumdar & Rohit Agrawal, 2022. "Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research," Operations Management Research, Springer, vol. 15(1), pages 378-398, June.
    10. Jihoo Jung & Jehyun Lee & Sangjin Choi & Woonho Baek, 2022. "Information Analysis on Foreign Institution for International R&D Collaboration Using Natural Language Processing," Energies, MDPI, vol. 16(1), pages 1-17, December.
    11. Sandeep Singhai & Ritika Singh & Harish Kumar Sardana & Anuradha Madhukar, 2021. "Analysis of Factors Influencing Technology Transfer: A Structural Equation Modeling Based Approach," Sustainability, MDPI, vol. 13(10), pages 1-15, May.
    12. 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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