IDEAS home Printed from https://ideas.repec.org/a/spr/reihed/v66y2025i1d10.1007_s11162-024-09822-6.html
   My bibliography  Save this article

Knowledge Innovation Effect of University Computing Power in China: Evidence from the top500 Supercomputers

Author

Listed:
  • Yang Haodong

    (University of Science and Technology of China)

  • Liu Jialin

    (University of Science and Technology of China)

  • Wang Gaofeng

    (University of Science and Technology of China)

Abstract

With the increasingly prominent characteristics of data-intensive and AI-driven scientific paradigms, computing power has become a crucial pillar of research activities. This study aims to examine the knowledge innovation effects of university supercomputing development by theoretically proposing two mechanisms: the efficiency effect (including technical and nontechnical factors) and the scale effect. Empirically, we match the extracted scientific publications of universities via Web of Science with supercomputer information from the top 500 rankings, constructing a panel dataset of 110 distinguished universities in China. Within the causal inference framework, the spatial difference-in-differences method (spatial DID) is employed to assess the impact of university computing power "upgrades" on knowledge innovation. The research findings include: (1) Overall, supercomputing construction stimulates knowledge innovation in universities, primarily manifested in the increase in the number of general papers and highly cited papers cataloged by the Science Citation Index (SCI). (2) Knowledge innovation effects have a lag period of approximately one–four years and may have a negative impact on innovation in geographically and economically adjacent universities. (3) Supercomputing construction mainly promotes university knowledge innovation by improving innovation efficiency (efficiency effect), accounting for 85.0% ~ 96.5% of the total effect. Among these factors, the proportion of nontechnical factors is at most 38.0%. In contrast, the scale effect accounts for a maximum of 15.0%, which is achieved mainly through an increase in the scale of research personnel. (4) There is significant interuniversity heterogeneity in the knowledge innovation effects of supercomputing, with Tsinghua University, Shanghai Jiao Tong University, and Jilin University showing the most significant effects. Additionally, we provide a series of potential optimization utility lists for universities, which, together with benchmark and mechanism tests, constitute a complete policy sandbox.

Suggested Citation

  • Yang Haodong & Liu Jialin & Wang Gaofeng, 2025. "Knowledge Innovation Effect of University Computing Power in China: Evidence from the top500 Supercomputers," Research in Higher Education, Springer;Association for Institutional Research, vol. 66(1), pages 1-30, February.
  • Handle: RePEc:spr:reihed:v:66:y:2025:i:1:d:10.1007_s11162-024-09822-6
    DOI: 10.1007/s11162-024-09822-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11162-024-09822-6
    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/s11162-024-09822-6?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. Ge, Shuang & Liu, Xielin, 2022. "The role of knowledge creation, absorption and acquisition in determining national competitive advantage," Technovation, Elsevier, vol. 112(C).
    2. V. Krishnan & Saurabh Gupta, 2001. "Appropriateness and Impact of Platform-Based Product Development," Management Science, INFORMS, vol. 47(1), pages 52-68, January.
    3. Yang, Xuehui & Zhang, Huirong & Lin, Shanlang & Zhang, Jiaping & Zeng, Jianlong, 2021. "Does high-speed railway promote regional innovation growth or innovation convergence?," Technology in Society, Elsevier, vol. 64(C).
    4. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    5. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    6. William D. Nordhaus, 2001. "The Progress of Computing," Cowles Foundation Discussion Papers 1324, Cowles Foundation for Research in Economics, Yale University.
    7. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    8. Marynia Kolak & Luc Anselin, 2020. "A Spatial Perspective on the Econometrics of Program Evaluation," International Regional Science Review, , vol. 43(1-2), pages 128-153, January.
    9. Akter, Shahriar & Hossain, Md Afnan & Sajib, Shahriar & Sultana, Saida & Rahman, Mahfuzur & Vrontis, Demetris & McCarthy, Grace, 2023. "A framework for AI-powered service innovation capability: Review and agenda for future research," Technovation, Elsevier, vol. 125(C).
    10. Koellinger, Philipp, 2008. "The relationship between technology, innovation, and firm performance--Empirical evidence from e-business in Europe," Research Policy, Elsevier, vol. 37(8), pages 1317-1328, September.
    11. Jiancheng Guan & He Wei, 2015. "A bilateral comparison of research performance at an institutional level," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 147-173, July.
    12. Etzkowitz, Henry & Leydesdorff, Loet, 2000. "The dynamics of innovation: from National Systems and "Mode 2" to a Triple Helix of university-industry-government relations," Research Policy, Elsevier, vol. 29(2), pages 109-123, February.
    13. Philip Cooke, 2012. "From Clusters to Platform Policies in Regional Development," European Planning Studies, Taylor & Francis Journals, vol. 20(8), pages 1415-1424, August.
    14. Thi Bich Hanh Tran & Anh Dung Vu, 2022. "Effect of university-enterprise alliance orientation on university’s innovation performance and market performance: evidence from Vietnam," Journal of Marketing for Higher Education, Taylor & Francis Journals, vol. 32(2), pages 238-258, July.
    15. Yun Liu & Mengya Zhang & Gupeng Zhang & Xiongxiong You, 2022. "Scientific elites versus other scientists: who are better at taking advantage of the research collaboration network?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3145-3166, June.
    16. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
    17. Klüppel, Leonardo & Knott, Anne Marie, 2023. "Are ideas being fished out?," Research Policy, Elsevier, vol. 52(2).
    18. Romer, Paul M, 1990. "Endogenous Technological Change," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 71-102, October.
    19. Wu, Ning & Liu, ZuanKuo, 2021. "Higher education development, technological innovation and industrial structure upgrade," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    20. Jonbekova, Dilrabo & Sparks, Jason & Hartley, Matthew & Kuchumova, Gulfiya, 2020. "Development of university–industry partnerships in Kazakhstan: Innovation under constraint," International Journal of Educational Development, Elsevier, vol. 79(C).
    21. Wang, Xuliang & Xu, Lulu & Ye, Qin & He, Shi & Liu, Yi, 2022. "How does services agglomeration affect the energy efficiency of the service sector? Evidence from China," Energy Economics, Elsevier, vol. 112(C).
    22. Fan-Chuan Tseng & Mu-Hsuan Huang & Dar-Zen Chen, 2020. "Factors of university–industry collaboration affecting university innovation performance," The Journal of Technology Transfer, Springer, vol. 45(2), pages 560-577, April.
    23. Chan, Yolande E. & Krishnamurthy, Rashmi & Sadreddin, Arman, 2022. "Digitally-enabled university incubation processes," Technovation, Elsevier, vol. 118(C).
    24. J. Paul Elhorst, 2014. "Matlab Software for Spatial Panels," International Regional Science Review, , vol. 37(3), pages 389-405, July.
    25. Zhang, Han & Patton, Donald & Kenney, Martin, 2013. "Building global-class universities: Assessing the impact of the 985 Project," Research Policy, Elsevier, vol. 42(3), pages 765-775.
    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. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    2. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    3. Callaway, Brantly & Karami, Sonia, 2023. "Treatment effects in interactive fixed effects models with a small number of time periods," Journal of Econometrics, Elsevier, vol. 233(1), pages 184-208.
    4. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    5. Hans-Bernd Schaefer & Rok Spruk, 2024. "Islamic Law, Western European Law and the Roots of Middle East's Long Divergence: a Comparative Empirical Investigation (800-1600)," Papers 2401.14435, arXiv.org, revised Mar 2024.
    6. Melnik, Walter & Smyth, Andrew, 2024. "R&D tax credits and innovation," Journal of Public Economics, Elsevier, vol. 236(C).
    7. Mounu Prem & Juan Vargas & Miguel E. Purroy, 2021. "Landmines: The Local Effects of Demining," Empirical Studies of Conflict Project (ESOC) Working Papers 28, Empirical Studies of Conflict Project.
    8. Leon Bremer & Konstantin Sommer, 2022. "Competitiveness and investments under emissions trading," Tinbergen Institute Discussion Papers 22-061/V, Tinbergen Institute.
    9. Andrea Albanese & Adrián Nieto & Konstantinos Tatsiramos, 2022. "Job Location Decisions and the Effect of Children on the Employment Gender Gap," CESifo Working Paper Series 9792, CESifo.
    10. Lin, Lihua & Zhang, Zhengyu, 2022. "Interpreting the coefficients in dynamic two-way fixed effects regressions with time-varying covariates," Economics Letters, Elsevier, vol. 216(C).
    11. Perilla, Sergio & Prem, Mounu & Purroy, Miguel E. & Vargas, Juan F., 2024. "How peace saves lives: Evidence from Colombia," World Development, Elsevier, vol. 176(C).
    12. Ozge Demirci & Jonas Hannane & Xinrong Zhu, 2024. "Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms," CESifo Working Paper Series 11276, CESifo.
    13. Qiu, Jun & Lyu, Ping & Tian, Min, 2024. "Do talent housing policies foster regional innovation? An analysis based on labor force heterogeneity," Economic Analysis and Policy, Elsevier, vol. 83(C), pages 150-164.
    14. Kyunghoon Ban & D'esir'e K'edagni, 2022. "Robust Difference-in-differences Models," Papers 2211.06710, arXiv.org, revised Aug 2023.
    15. Jung, Suhyun & Rogers, Martha, 2024. "Mobile phone adoption, deforestation, and agricultural land use in Uganda," World Development, Elsevier, vol. 179(C).
    16. Christian Alemán-Pericón & Alexander Ludwig & Christopher Busch & Raül Santaeulà lia-Llopis, 2022. "A Stage-Based Identification of Policy Effects," Working Papers 1369, Barcelona School of Economics.
    17. Dmitry Arkhangelsky & Aleksei Samkov, 2024. "Sequential Synthetic Difference in Differences," Papers 2404.00164, arXiv.org.
    18. Jordi J. Teixidó & F. Javier Palencia-González & José M. Labeaga & Xavier Labandeira, 2024. "Carbon Leakage from Fuel Taxes: Evidence from a Natural Experiment," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(12), pages 3235-3270, December.
    19. Dmitry Arkhangelsky & David Hirshberg, 2023. "Large-Sample Properties of the Synthetic Control Method under Selection on Unobservables," Papers 2311.13575, arXiv.org, revised Dec 2023.
    20. Ridwan Ah Sheikh & Sunil Kanwar, 2024. "Revisiting the Impact of TRIPS on IPR-intensive Export Flows: Evidence from Staggered Difference-in-Differences," Working papers 351, Centre for Development Economics, Delhi School of Economics.

    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:reihed:v:66:y:2025:i:1:d:10.1007_s11162-024-09822-6. 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.