IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v128y2023i5d10.1007_s11192-023-04682-w.html
   My bibliography  Save this article

Government R&D spending as a driving force of technology convergence: a case study of the Advanced Sequencing Technology Program

Author

Listed:
  • Chen Zhu

    (Department of Technology Management for Innovation, The University of Tokyo)

  • Kazuyuki Motohashi

    (Department of Technology Management for Innovation, The University of Tokyo
    Research Institute of Economy, Trade and Industry (RIETI))

Abstract

This study investigates the impact of government R&D spending on promoting technology convergence. We test the hypotheses that a government funding program positively affects technology convergence, and that the effects vary depending on the participant (i.e., academic and industrial inventors). We used the Advanced Sequencing Technology Program (ASTP) as an example to investigate this issue. We develop a novel dataset by linking the ASTP grantee information with the PATSTAT patent database. On this basis, we develop inventor-level characteristics for propensity score matching, selecting a control group of inventors from among those enrolled in the ASTP. Then, we employ difference-in-difference models to assess the program’s impact on the matched sample. The results support the program’s role as a driving force of technology convergence. The findings also indicate that the program has a greater influence on industry inventors than on academic counterparts. Furthermore, we conceptualize the program’s “leverage effect” and demonstrate that it can attract more external industrial inventors than academic inventors. The work advances our understanding of the role of a government-funded program in encouraging convergence and has implications for developing convergence-related R&D programs in the future.

Suggested Citation

  • Chen Zhu & Kazuyuki Motohashi, 2023. "Government R&D spending as a driving force of technology convergence: a case study of the Advanced Sequencing Technology Program," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 3035-3065, May.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:5:d:10.1007_s11192-023-04682-w
    DOI: 10.1007/s11192-023-04682-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-023-04682-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-023-04682-w?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. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1002-1037, October.
    2. Park, Hyunwoo & Lee, Jeongsik (Jay) & Kim, Byung-Cheol, 2015. "Project selection in NIH: A natural experiment from ARRA," Research Policy, Elsevier, vol. 44(6), pages 1145-1159.
    3. Meyer, Martin, 2000. "Does science push technology? Patents citing scientific literature," Research Policy, Elsevier, vol. 29(3), pages 409-434, March.
    4. Klein, Julie Thompson & Falk-Krzesinski, Holly J., 2017. "Interdisciplinary and collaborative work: Framing promotion and tenure practices and policies," Research Policy, Elsevier, vol. 46(6), pages 1055-1061.
    5. Karvonen, Matti & Kässi, Tuomo, 2013. "Patent citations as a tool for analysing the early stages of convergence," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1094-1107.
    6. Arnold, Austin & Cafer, Anne & Green, John & Haines, Seena & Mann, Georgianna & Rosenthal, Meagen, 2021. "“Perspective: Promoting and fostering multidisciplinary research in universities”," Research Policy, Elsevier, vol. 50(9).
    7. Feldman, Maryann P. & Kelley, Maryellen R., 2006. "The ex ante assessment of knowledge spillovers: Government R&D policy, economic incentives and private firm behavior," Research Policy, Elsevier, vol. 35(10), pages 1509-1521, December.
    8. Kim, Juram & Kim, Seungho & Lee, Changyong, 2019. "Anticipating technological convergence: Link prediction using Wikipedia hyperlinks," Technovation, Elsevier, vol. 79(C), pages 25-34.
    9. Zhou, Yuan & Dong, Fang & Kong, Dejing & Liu, Yufei, 2019. "Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 205-220.
    10. Caviggioli, Federico, 2016. "Technology fusion: Identification and analysis of the drivers of technology convergence using patent data," Technovation, Elsevier, vol. 55, pages 22-32.
    11. Jeeeun Kim & Sungjoo Lee, 2017. "Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 47-65, April.
    12. Erika Check Hayden, 2014. "Technology: The $1,000 genome," Nature, Nature, vol. 507(7492), pages 294-295, March.
    13. Christine Greenhalgh & Mark Rogers, 2010. "Innovation, Intellectual Property, and Economic Growth," Economics Books, Princeton University Press, edition 1, volume 0, number 9221.
    14. Martin, Stephen & Scott, John T., 2000. "The nature of innovation market failure and the design of public support for private innovation," Research Policy, Elsevier, vol. 29(4-5), pages 437-447, April.
    15. Zhu, Chen & Motohashi, Kazuyuki, 2022. "Identifying the technology convergence using patent text information: A graph convolutional networks (GCN)-based approach," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    16. Littler, Dale & Coombs, Rod, 1988. "Current and future trends in European technological development: New patterns in the funding of R&D," European Management Journal, Elsevier, vol. 6(2), pages 102-113, June.
    17. Veugelers, Reinhilde & Wang, Jian, 2019. "Scientific novelty and technological impact," Research Policy, Elsevier, vol. 48(6), pages 1362-1372.
    18. Manuel Trajtenberg & Rebecca Henderson & Adam Jaffe, 1997. "University Versus Corporate Patents: A Window On The Basicness Of Invention," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 5(1), pages 19-50.
    19. Henry Sauermann & Paula Stephan, 2013. "Conflicting Logics? A Multidimensional View of Industrial and Academic Science," Organization Science, INFORMS, vol. 24(3), pages 889-909, June.
    20. Scott Shane, 2001. "Technological Opportunities and New Firm Creation," Management Science, INFORMS, vol. 47(2), pages 205-220, February.
    21. Gambardella, Alfonso & Giarratana, Marco S., 2013. "General technological capabilities, product market fragmentation, and markets for technology," Research Policy, Elsevier, vol. 42(2), pages 315-325.
    22. Bruce Kogut, 1988. "Joint ventures: Theoretical and empirical perspectives," Strategic Management Journal, Wiley Blackwell, vol. 9(4), pages 319-332, July.
    23. Vikas A. Aggarwal & David H. Hsu & Andy Wu, 2020. "Organizing Knowledge Production Teams Within Firms for Innovation," Strategy Science, INFORMS, vol. 5(1), pages 1-16, March.
    24. Jeong, Seongkyoon, 2014. "Strategic collaboration of R&D entities for technology convergence: Exploring organizational differences within the triple helix," Journal of Management & Organization, Cambridge University Press, vol. 20(2), pages 227-249, March.
    25. Seongkyoon Jeong & Jae Young Choi & Jaeyun Kim, 2011. "The determinants of research collaboration modes: exploring the effects of research and researcher characteristics on co-authorship," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 967-983, December.
    26. 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.
    27. Kenneth Arrow, 1962. "Economic Welfare and the Allocation of Resources for Invention," NBER Chapters, in: The Rate and Direction of Inventive Activity: Economic and Social Factors, pages 609-626, National Bureau of Economic Research, Inc.
    28. Mowery, David & Rosenberg, Nathan, 1993. "The influence of market demand upon innovation: A critical review of some recent empirical studies," Research Policy, Elsevier, vol. 22(2), pages 107-108, April.
    29. Euiseok Kim & Yongrae Cho & Wonjoon Kim, 2014. "Dynamic patterns of technological convergence in printed electronics technologies: patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 975-998, February.
    30. Roessner, J. David, 1989. "Evaluating government innovation programs: Lessons from the U.S. experience," Research Policy, Elsevier, vol. 18(6), pages 343-359, December.
    31. Wang, Jian & Lee, You-Na & Walsh, John P., 2018. "Funding model and creativity in science: Competitive versus block funding and status contingency effects," Research Policy, Elsevier, vol. 47(6), pages 1070-1083.
    32. Appio, Francesco Paolo & Martini, Antonella & Fantoni, Gualtiero, 2017. "The light and shade of knowledge recombination: Insights from a general-purpose technology," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 154-165.
    33. Kim, Tae San & Sohn, So Young, 2020. "Machine-learning-based deep semantic analysis approach for forecasting new technology convergence," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    34. Wirsich, Alexander & Kock, Alexander & Strumann, Christoph & Schultz, Carsten, 2016. "Effects of University Industry Collaboration on Technological Newness," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 85098, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    35. Seongkyoon Jeong & Jong-Chan Kim & Jae Young Choi, 2015. "Technology convergence: What developmental stage are we in?," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 841-871, September.
    36. Michelle Gittelman & Bruce Kogut, 2003. "Does Good Science Lead to Valuable Knowledge? Biotechnology Firms and the Evolutionary Logic of Citation Patterns," Management Science, INFORMS, vol. 49(4), pages 366-382, April.
    37. Andrei Rikkiev & Saku J. Mäkinen, 2013. "Technology Convergence And Intercompany R&D Collaboration: Across Business Ecosystems Boundaries," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 1-28.
    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. Baaden, Philipp & Rennings, Michael & John, Marcus & Bröring, Stefanie, 2024. "On the emergence of interdisciplinary scientific fields: (how) does it relate to science convergence?," Research Policy, Elsevier, vol. 53(6).

    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. ZHU Chen & MOTOHASHI Kazuyuki, 2022. "Government R&D spending as a driving force of technology convergence," Discussion papers 22030, Research Institute of Economy, Trade and Industry (RIETI).
    2. Zhao, Shengchao & Zeng, Deming & Li, Jian & Feng, Ke & Wang, Yao, 2023. "Quantity or quality: The roles of technology and science convergence on firm innovation performance," Technovation, Elsevier, vol. 126(C).
    3. Sick, Nathalie & Bröring, Stefanie, 2022. "Exploring the research landscape of convergence from a TIM perspective: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    5. Park, Mingyu & Geum, Youngjung, 2022. "Two-stage technology opportunity discovery for firm-level decision making: GCN-based link-prediction approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    6. Zhu, Chen & Motohashi, Kazuyuki, 2022. "Identifying the technology convergence using patent text information: A graph convolutional networks (GCN)-based approach," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    7. Ying Tang & Xuming Lou & Zifeng Chen & Chengjin Zhang, 2020. "A Study on Dynamic Patterns of Technology Convergence with IPC Co-Occurrence-Based Analysis: The Case of 3D Printing," Sustainability, MDPI, vol. 12(7), pages 1-26, March.
    8. 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.
    9. Wang, Fang, 2024. "Does the recombination of distant scientific knowledge generate valuable inventions? An analysis of pharmaceutical patents," Technovation, Elsevier, vol. 130(C).
    10. Sajad Ashouri & Anne-Laure Mention & Kosmas X. Smyrnios, 2021. "Anticipation and analysis of industry convergence using patent-level indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5727-5758, July.
    11. Sun, Bing & Yang, Xueting & Zhong, Shen & Tian, Shengnan & Liang, Tian, 2024. "How do technology convergence and expansibility affect information technology diffusion? Evidence from the internet of things technology in China," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    12. Jungpyo Lee & So Young Sohn, 2021. "Recommendation system for technology convergence opportunities based on self-supervised representation learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 1-25, January.
    13. Chen, Xi & Mao, Jin & Ma, Yaxue & Li, Gang, 2024. "The knowledge linkage between science and technology influences corporate technological innovation: Evidence from scientific publications and patents," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    14. Changyong Lee & Suckwon Hong & Juram Kim, 2021. "Anticipating multi-technology convergence: a machine learning approach using patent information," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 1867-1896, March.
    15. Chul Lee & Gunno Park & Jina Kang, 2018. "The impact of convergence between science and technology on innovation," The Journal of Technology Transfer, Springer, vol. 43(2), pages 522-544, April.
    16. Kwangsoo Shin & Minkyung Choy & Chul Lee & Gunno Park, 2019. "Government R&D Subsidy and Additionality of Biotechnology Firms: The Case of the South Korean Biotechnology Industry," Sustainability, MDPI, vol. 11(6), pages 1-22, March.
    17. Qian Xu & Hua Cheng, 2021. "Research on the Evolution of Textile Technological Convergence in China," Sustainability, MDPI, vol. 13(5), pages 1-13, February.
    18. Caviggioli, Federico, 2016. "Technology fusion: Identification and analysis of the drivers of technology convergence using patent data," Technovation, Elsevier, vol. 55, pages 22-32.
    19. Lai, I-Chun & Su, Hsin-Ning, 2024. "Knowledge spectrum explored: Understanding source-recipient interactions and their influence on technology convergence," Technovation, Elsevier, vol. 133(C).
    20. Kolesnikov, Sergey & Goldstein, Anna P. & Sun, Bixuan & Chan, Gabriel & Narayanamurti, Venkatesh & Anadon, Laura Diaz, 2024. "A framework and methodology for analyzing technology spillover processes with an application in solar photovoltaics," Technovation, Elsevier, vol. 134(C).

    More about this item

    Keywords

    Technology convergence; NIH program; Policy analysis;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

    Statistics

    Access and download statistics

    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:spr:scient:v:128:y:2023:i:5:d:10.1007_s11192-023-04682-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.