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数智化转型对农产品加工企业创新边界的影响——基于我国上市公司的实证分析

The impact of digital and intelligent transformation on the innovation boundaries of agricultural product processing enterprises: an empirical analysis based on listed companies in China

  • 摘要: 推进农产品加工业的数智化转型是实现中国式农业现代化和深入实施农业强国战略的重要举措。本文利用2016—2023年210家农产品加工业上市公司的面板数据,分析了数智化转型对农产品加工企业创新边界的影响及其作用机制。研究发现,数智化转型显著促进了农产品加工企业创新边界的拓展。进一步的异质性分析表明,数智化转型对不同类型企业的创新效应存在明显差异:与中小型或非国有企业相比,数智化转型对大型或国有企业的创新边界影响更为显著。作用机制分析结果表明,数智化转型主要通过缓解融资约束和促进人力资本积累,推动企业创新边界的不断扩展。研究结论不仅丰富了企业数智化转型与创新边界领域的理论研究,也为推动农产品加工业的数智化转型和高质量发展提供了实证依据与政策建议,具有重要的现实意义。

     

    Abstract: Promoting the digital and intelligent transformation of the agricultural product processing industry is an important measure for achieving Chinese-style agricultural modernization and for deeply implementing the strategy of building a strong agricultural country. This study analyzes the impact and mechanism of digital and intelligent transformation on the innovation boundaries of agricultural product processing enterprises, based on panel data from 210 listed companies between 2016 and 2023. The findings reveal that digital and intelligent transformation significantly promotes the expansion of innovation boundaries in agricultural product processing enterprises. Further heterogeneity analysis demonstrates that the innovation effects of digital and intelligent transformation vary significantly across different types of enterprises: compared to small and medium-sized or non-state-owned enterprises, digital and intelligent transformation has a more pronounced impact on the innovation boundaries of large-scale or state-owned enterprises. Mechanism analysis suggests that digital and intelligent transformation primarily drives the continuous expansion of enterprise innovation boundaries by alleviating financing constraints and promoting human capital accumulation. The research conclusions not only enrich the theoretical understanding of the relationship between digital-intelligent transformation and enterprise innovation boundaries but also provide empirical evidence and policy recommendations for promoting the digital-intelligent transformation and high-quality development of the agricultural product processing industry, with important practical significance.

     

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