数字技术创新对制造业企业市场价值影响研究

何芸, 杨阳丹

科研管理 ›› 2026, Vol. 47 ›› Issue (7) : 88-96.

PDF(1016 KB)
PDF(1016 KB)
科研管理 ›› 2026, Vol. 47 ›› Issue (7) : 88-96. DOI: 10.19571/j.cnki.1000-2995.2026.07.009  CSTR: 32148.14.kygl.2026.07.009

数字技术创新对制造业企业市场价值影响研究

作者信息 +

Research on the impact of digital technology innovation on the market value of manufacturing enterprises

Author information +
文章历史 +

摘要

推动数字经济与实体经济深度融合正逐渐成为中国新一轮经济增长点。文章以A股上市的制造企业为研究对象,从企业专利文本信息中识别出数字专利,进而构造数字技术创新指标,并考察其对公司市场价值的影响。实证结果表明:数字技术创新对制造企业的市值有明显的促进作用,且这一结论在内生性和稳健性的检验中依然成立。上述提升作用在高科技企业、劳动密集型企业、民营企业以及大公司中更显著。机制检验表明,数字技术创新可以通过降低生产成本、优化劳动力资源结构以及提升创新效率这三重机制影响市场价值。本研究验证了数字技术创新对实体经济价值增长的提升作用,对中国数字技术相关的政策设计具有参考价值,为推动实体企业数字化转型的战略决策提供了经验证据和建议。

Abstract

Promoting the deep integration of the digital economy and the real economy is gradually becoming a new growth driver for China’s economy. This study focuses on A-share listed manufacturing enterprises in China, analyzing corporate patent texts to identify digital patents, thereby constructing a digital technology innovation indicator and examining its impact on corporate market value.The empirical results show that digital technology innovation significantly enhances the market value of manufacturing enterprises, and this conclusion remains valid after endogeneity and robustness tests. The positive effect is more pronounced in high-tech enterprises, labor-intensive enterprises, private enterprises, and large companies. Mechanism tests indicate that digital technology innovation affects market value through three channels: reducing production costs, optimizing labor resource structures, and improving innovation efficiency. This study validates the role of digital technology innovation in boosting the value growth of the real economy, providing reference for policy design related to digital technology in China and offering empirical evidence and recommendations for promoting the digital transformation of real enterprises.

关键词

数字技术 / 市场价值 / 文本分析 / 实体经济 / 数字经济

Key words

digital technology / market value / text analysis / real economy / digital economy

引用本文

导出引用
何芸, 杨阳丹. 数字技术创新对制造业企业市场价值影响研究[J]. 科研管理. 2026, 47(7): 88-96 https://doi.org/10.19571/j.cnki.1000-2995.2026.07.009
He Yun, Yang Yangdan. Research on the impact of digital technology innovation on the market value of manufacturing enterprises[J]. Science Research Management. 2026, 47(7): 88-96 https://doi.org/10.19571/j.cnki.1000-2995.2026.07.009
中图分类号: F49;F270.3   

参考文献

[1]
张叶青, 陆瑶, 李乐芸. 大数据应用对中国企业市场价值的影响:来自中国上市公司年报文本分析的证据[J]. 经济研究, 2021, 56(12):42-59.
Zhang Yeqing, Lu Yao, Li Leyun. Effects ofbig data on firm value in China:Evidence from textual analysis of Chinese listed firms’ annual reports[J]. Economic Research Journal, 2021, 56(12):42-59.
[2]
田秀娟, 李睿. 数字技术赋能实体经济转型发展:基于熊彼特内生增长理论的分析框架[J]. 管理世界, 2022, 38(5):56-74.
Tian Xiujuan, Li Rui. Digital technology empowers the transformation and development of real economy:An analysis framework based on Schumpeter’s endogenous growth theory[J]. Journal of Management World, 2022, 38(5):56-74.
[3]
陈雨露. 数字经济与实体经济融合发展的理论探索[J]. 经济研究, 2023, 58(9):22-30.
Chen Yulu. Theoretical exploration of the integrated development of digital economy and real economy[J]. Economic Research Journal, 2023, 58(9):22-30.
[4]
徐旭初, 杨威, 吴彬. 乡村数字经济赋能农业全要素生产率提升的多元路径:基于浙江省县级数据的组态分析[J]. 中国农村经济, 2024(10):84-103.
Xu Xuchu, Yang Wei, Wu Bin. Multiple pathways for rural digital economy empowering agricultural total factor productivity:A configuration analysis based on county-level data from Zhejiang Province[J]. Chinese Rural Economy, 2024(10):84-103.
[5]
刘平峰, 张旺. 数字技术如何赋能制造业全要素生产率?[J]. 科学学研究, 2021, 39(8):1396-1406.
摘要
数字技术是数字经济核心驱动力,与实体经济深度融合加速优化重构生产要素体系,催生出数字化生产要素。本文从数字技术是生产要素赋能型技术视角,拓展数字技术为资本赋能型技术和劳动赋能型技术,引入CES生产函数中推演TFP增长公式,清晰展现了数字技术赋能路径,并基于1990-2018年中国制造业27个细分行业面板数据进行参数估计。研究发现:数字技术是TFP增长主要驱动力,中国制造业TFP年增长率4.9%,其中数字技术贡献4.1%;数字技术和要素配置均偏向于资本,数字化背景下资本与劳动替代弹性为0.763(互补关系);数字技术偏向和要素配置偏向均已由抑制演变为促进,两者交互项已由促进演变为抑制并呈倒U特征。
Liu Pingfeng, Zhang Wang. How does digital technology empower the totalfactor productivity of the manufacturing sector?[J]. Studies in Science of Science, 2021, 39(8):1396-1406.
Digital technology is the core driving force of the digital economy. Its deep integration with the real economy accelerates the optimization and reconstruction of the production factor system, and gives birth to digital production factors. Taking digital technology as a technology empowering production factors, we expand digital technology to capital-enabling technology and labor-enabling technology. Then we introduce the CES production function to derive the TFP growth formula, which clearly shows the path of digital technology empowerment. Based on the panel data of 27 sub-sectors of China's manufacturing sector from 1990 to 2018, parameter estimation is conducted. It is found that digital technology is the main driving force for the growth of TFP. The annual growth rate of TFP in China’s manufacturing industry is 4.9% while digital technology contributes 4.1%. Digital technology and factor allocation are biased towards capital. The elasticity of substitution of capital to labor under the digital background is 0.763 (complementary relationship). Both the digital technology bias and the element allocation bias have evolved from suppression to promotion. The interactive items between them have evolved from promotion to suppression, showing the characteristics of an inverted U.
[6]
李晓华. 数字技术推动下的服务型制造创新发展[J]. 改革, 2021(10):72-83.
Li Xiaohua. The innovative development of service-oriented manufacturing driven by the digital technologies[J]. Reform, 2021(10):72-83.
[7]
Liu Yang, Dong Jiuyu, Mei Liang, et al. Digital innovation and performance of manufacturing firms:An affordance perspective[J]. Technovation, 2023, 119,102458.
[8]
黄勃, 李海彤, 刘俊岐, 等. 数字技术创新与中国企业高质量发展:来自企业数字专利的证据[J]. 经济研究, 2023, 58(3):97-115.
Huang Bo, Li Haitong, Liu Junqi, et al. Digital technology innovation and the high-quality development of Chinese enterprises:Evidence from enterprise’s digital patents[J]. Economic Research Journal, 2023, 58(3):97-115.
[9]
吴非, 徐斯旸. 人工智能技术应用与上市企业市场价值[J]. 现代经济探讨, 2022(11):77-92.
Wu Fei, Xu Siyang. The application of artificial intelligence and the market value of listed companies[J]. Modern Economic Research, 2022(11):77-92.
[10]
陶锋, 朱盼, 邱楚芝, 等. 数字技术创新对企业市场价值的影响研究[J]. 数量经济技术经济研究, 2023, 40(5):68-91.
Tao Feng, Zhu Pan, Qiu Chuzhi, et al. The impact of digital technology innovation on enterprise market value[J]. Journal of Quantitative & Technological Economics, 2023, 40(5):68-91.
[11]
Cathles A, Nayyar G, Rückert D. Digital technologies and firm performance:Evidence from Europe[J]. EIB Working Papers, 2020.DOI:10.2867/36888.
[12]
刘洋, 董久钰, 魏江. 数字创新管理:理论框架与未来研究[J]. 管理世界, 2020, 36(7):198-217+219.
Liu Yang, Dong Jiuyu, Wei Jiang. Digital innovation management:Theoretical framework and future research[J]. Journal of Management World, 2020, 36(7):198-217+219.
[13]
林东杰, 崔小勇, 龚六堂. 金融摩擦异质性、资源错配与全要素生产率损失[J]. 经济研究, 2022, 57(1):89-106.
Lin Dongjie, Cui Xiaoyong, Gong Liutang. Financial friction heterogeneity,resources misallocation and TFP loss[J]. Economic Research Journal, 2022, 57(1):89-106.
[14]
袁淳, 肖土盛, 耿春晓. 数字化转型与企业分工:专业化还是纵向一体化[J]. 中国工业经济, 2021(9):137-155.
Yuan Chun, Xiao Tusheng, Geng Chunxiao, et al. Digital transformation and division of labor between enterprises:Vertical specialization or vertical integration[J]. China Industrial Economics, 2021(9):137-155.
[15]
Ciarli T, Kenney M, Massini S, et al. Digital technologies,innovation,and skills:Emerging trajectories and challenges[J]. Research Policy, 2021(6):104289.
[16]
Chen N, Sun D, Chen J. Digital transformation,labor share,and industrial heterogeneity[J]. Journal of Innovation & Knowledge, 2022,100173.
[17]
Babina T, Fedyk A, He A X, et al. Artificial intelligence,firm growth,and industry concentration[J]. Social Science Electronic Publishing.DOI:10.2139/ssrn.3651052.
[18]
吴非, 胡慧芷, 林慧妍, 等. 企业数字化转型与资本市场表现:来自股票流动性的经验证据[J]. 管理世界, 2021, 37(7):130-144+10.
Wu Fei, Hu Huizhi, Lin Huiyan, et al. Enterprise digital transformation and capital market performance:Empirical evidence from stock liquidity[J]. Journal of Management World, 2021, 37(7):130-144+10.

基金

教育部人文社会科学研究规划基金项目:“基于多源异构数据融合的碳排放权价格波动风险测度及防控研究”(21YJAZH082,2021.03—2026.03)
中央高校基本科研业务费专项资金资助,合肥工业大学学术新人提升B计划:“融合知识图谱与深度学习的我国系统性金融风险预警研究”(JZ2024HGTB0193,2024.04—2025.12)

PDF(1016 KB)

Accesses

Citation

Detail

段落导航
相关文章

/