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How does public digital procurement enhance corporate total factor productivity? The role of industry–university–research collaboration

Author

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  • Kan Jia
  • Jinqi Qin
  • Yang Li

Abstract

This study innovatively employs large language model (LLM) technology to construct a public digital procurement (PDP) index and, by utilizing microlevel data from Chinese A-share listed companies (2015–2023), systematically examines the impact of PDP on corporate total factor productivity (TFP) and its underlying mechanisms. The results demonstrate that PDP has a significantly positive effect on corporate TFP, and this conclusion remains robust after endogeneity concerns are addressed and multiple robustness tests are conducted. Mechanism analysis reveals that PDP significantly increases corporate TFP primarily through three pathways: enhancing digital technology innovation, alleviating financial constraints, and improving corporate information disclosure. Furthermore, in the context of industry–university–research (IUR) collaboration, PDP has a more pronounced positive effect on corporate TFP. Additional analysis indicates a synergistic effect between PDP and corporate digital transformation, meaning that firms with a higher level of digital transformation can more effectively leverage PDP to achieve TFP growth. This study provides critical theoretical and empirical evidence for leveraging PDP to improve corporate TFP and offers important references for government departments in optimizing the design of PDP policies and enhancing their implementation effectiveness.

Suggested Citation

  • Kan Jia & Jinqi Qin & Yang Li, 2025. "How does public digital procurement enhance corporate total factor productivity? The role of industry–university–research collaboration," PLOS ONE, Public Library of Science, vol. 20(8), pages 1-22, August.
    • Handle: RePEc:plo:pone00:0330160
    DOI: 10.1371/journal.pone.0330160
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    References listed on IDEAS

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    1. Chen, Chen-Wen & Liu, Victor W., 2013. "Corporate governance under asymmetric information: Theory and evidence," Economic Modelling, Elsevier, vol. 33(C), pages 280-291.
    2. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
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