Mechanistic analysis results.

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.

本研究创新性地采用大语言模型（Large Language Model, LLM）技术构建公共数字采购（Public Digital Procurement, PDP）指数，并借助2015-2023年中国A股上市公司的微观数据，系统考察了公共数字采购对企业全要素生产率（Total Factor Productivity, TFP）的影响及其作用机制。研究结果显示，公共数字采购对企业全要素生产率具有显著的正向促进作用，该结论在解决内生性顾虑并开展多项稳健性检验后依然稳健。机制分析表明，公共数字采购主要通过三条路径显著提升企业全要素生产率：一是增强数字技术创新能力，二是缓解企业融资约束，三是改善企业信息披露质量。进一步来看，在产学研（Industry-University-Research, IUR）合作场景下，公共数字采购对企业全要素生产率的正向促进作用更为突出。补充分析还发现，公共数字采购与企业数字化转型存在协同效应：数字化转型水平更高的企业能够更高效地借助公共数字采购实现全要素生产率增长。本研究为依托公共数字采购提升企业全要素生产率提供了关键的理论与实证依据，同时也为政府部门优化公共数字采购政策设计、提升政策实施效果提供了重要参考。