Host: 上海立信会计金融学院

  • ISSN 2096-9554
  • CN 31-2074/F
Volume 39 Issue 5
Oct.  2025
Turn off MathJax
Article Contents
Zhang Qingyan, Yang Xingquan. 2025. How Does Artificial Intelligence Promote State-owned Enterprises’ Focus on Core Business? —A Quasi-natural Experiment Based on Artificial Intelligence Pilot Zones. Accounting and Economic Research, 39(5): 38-62.
Citation: Zhang Qingyan, Yang Xingquan. 2025. How Does Artificial Intelligence Promote State-owned Enterprises’ Focus on Core Business? —A Quasi-natural Experiment Based on Artificial Intelligence Pilot Zones. Accounting and Economic Research, 39(5): 38-62.

How Does Artificial Intelligence Promote State-owned Enterprises’ Focus on Core Business? —A Quasi-natural Experiment Based on Artificial Intelligence Pilot Zones

  • Received Date: 2025-05-02
    Available Online: 2026-05-09
  • Publish Date: 2025-10-20
  • This study takes the establishment of the National New-generation Artificial Intelligence Innovation Pilot Zones as a quasi-natural experiment to examine the impact and underlying mechanism of artificial intelligence policies on core business concentration of state-owned enterprises. The empirical results reveal that the policy of artificial intelligence pilot zones significantly promotes core business concentration among state-owned enterprises. Mechanism analysis indicates that the policy functions through three channels: supervision and governance, resource allocation, and human capital. Heterogeneity analysis shows that the positive effect is more pronounced for firms in high-tech industries, located in cities with advanced digital infrastructure, and in regions with a higher degree of marketization. Further analysis suggests that the policy of artificial intelligence pilot zones enhances the core competitiveness of state-owned enterprises while empowering them to focus on their core business. This study provides theoretical insights into how AI-related policies influence corporate strategic behaviors, and offers practical implications for the government to further advance the “AI+” initiative and guide state-owned enterprises to focus on their core business.

     

  • loading
  • [1]
    蔡庆丰, 刘昊, 舒少文. 2024. 政府产业引导基金与域内企业创新: 引导效应还是挤出效应? 金融研究 (3): 75−93.
    [2]
    曹玉平, 刘竟威. 2025. 新一代人工智能发展促进了城市产业结构优化吗? ——效率提升与协调发展的双元视角分析. 管理评论(网络首发).
    [3]
    曹越, 孙丽. 2021. 国有控制权转让对内部控制质量的影响: 监督还是掏空? 会计研究 (10): 126−151.
    [4]
    陈东, 秦子洋. 2022. 人工智能与包容性增长——来自全球工业机器人使用的证据. 经济研究 (4): 85−102.
    [5]
    陈彦斌, 林晨, 陈小亮. 2019. 人工智能、老龄化与经济增长. 经济研究 (7): 47−63.
    [6]
    陈志勇, 陈思霞. 2014. 制度环境、地方政府投资冲动与财政预算软约束. 经济研究 (3): 76−87.
    [7]
    崔世娟, 孙利, 蓝海林. 2009. 中国企业归核化战略绩效研究. 科学学与科学技术管理 (7): 164−172.
    [8]
    郭凯明. 2019. 人工智能发展、产业结构转型升级与劳动收入份额变动. 管理世界 (7): 60−77. doi: 10.3969/j.issn.1002-5502.2019.07.007
    [9]
    洪娇, 牛彪, 王建新. 2025. 政府引导基金能促进企业聚焦主业吗? ——来自创业板上市公司的经验证据. 现代财经(天津财经大学学报) (6): 95−110.
    [10]
    李小荣, 张瑞君. 2014. 股权激励影响风险承担: 代理成本还是风险规避? 会计研究 (1): 57−63.
    [11]
    李玉花, 林雨昕, 李丹丹. 2024. 人工智能技术应用如何影响企业创新. 中国工业经济 (10): 155−173. doi: 10.3969/j.issn.1672-2272.202510008
    [12]
    林晨, 陈小亮, 陈伟泽, 陈彦斌. 2020. 人工智能、经济增长与居民消费改善: 资本结构优化的视角. 中国工业经济 (2): 61−83.
    [13]
    刘备, 邱兆轩, 任保平. 2025. 人工智能与企业高质量发展: 来自国家新一代人工智能创新发展试验区的经验证据. 中国软科学 (6): 166−176. doi: 10.3724/1005-0566.20250616
    [14]
    刘华珂, 李旭超, 聂禾, 宋敏. 2024. AI时代: 城市数智化转型与企业创新. 中国软科学 (2): 38−54. doi: 10.3969/j.issn.1002-9753.2024.02.005
    [15]
    潘越, 汤旭东, 宁博, 杨玲玲. 2020. 连锁股东与企业投资效率: 治理协同还是竞争合谋. 中国工业经济 (2): 136−164.
    [16]
    彭睿, 綦好东, 亚琨. 2020. 国有企业归核化与风险承担. 会计研究 (7): 104−118. doi: 10.3969/j.issn.1003-2886.2020.07.009
    [17]
    任广乾, 李俊超, 田一迪. 2023. 混合所有制改革何以影响国有企业投资效率? ——基于公司治理视角的研究. 南京审计大学学报 (5): 51−60.
    [18]
    孙早, 侯玉琳. 2019. 工业智能化如何重塑劳动力就业结构. 中国工业经济 (5): 61−79.
    [19]
    唐浩丹, 方森辉, 蒋殿春. 2022. 数字化转型的市场绩效: 数字并购能提升制造业企业市场势力吗? 数量经济技术经济研究 (12): 90−110.
    [20]
    王化成, 王芃芃, 孙昌玲, 郝恩琪. 2022. 中国上市公司核心竞争力信息披露: 现状、问题与改进建议. 会计研究 (8): 14−29. doi: 10.3969/j.issn.1003-2886.2022.08.002
    [21]
    巫强, 姚雨秀. 2023. 企业数字化转型与供应链配置: 集中化还是多元化. 中国工业经济 (8): 99−117. doi: 10.3969/j.issn.1006-480X.2023.08.007
    [22]
    辛清泉, 孔东民, 郝颖. 2014. 公司透明度与股价波动性. 金融研究 (10): 193−206.
    [23]
    杨德明, 刘泳文. 2018. “互联网+”为什么加出了业绩. 中国工业经济 (5): 80−98.
    [24]
    杨兴全, 李文聪, 尹兴强. 2019. 多元化经营对企业创新的“双重”影响研究. 财经研究 (8): 58−71. doi: 10.16538/j.cnki.jfe.2019.08.004
    [25]
    杨兴全, 刘颍. 2025. “耐心资本”何以助推国企聚焦主业? ——基于“国家队”持股的研究. 审计与经济研究 (4): 61−73.
    [26]
    杨兴全, 刘颍, 张可欣. 2025a. 产业链“链长制”与国企聚焦主业: “助推器”抑或“绊脚石”. 会计研究 (5): 147−160.
    [27]
    杨兴全, 尹兴强, 孟庆玺. 2018. 谁更趋多元化经营: 产业政策扶持企业抑或非扶持企业? 经济研究 (9): 133−150.
    [28]
    杨兴全, 张记元. 2022. 连锁股东与企业多元化经营: 加速扩张还是聚焦主业. 现代财经(天津财经大学学报) (5): 36−55.
    [29]
    杨兴全, 张清燕, 刘颖. 2025b. 供应链金融能否促进企业聚焦主业. 当代财经 (4): 97−110. doi: 10.13676/j.cnki.cn36-1030/f.20241112.001
    [30]
    姚加权, 张锟澎, 郭李鹏, 冯绪. 2024. 人工智能如何提升企业生产效率? ——基于劳动力技能结构调整的视角. 管理世界 (2): 101−116, 133. doi: 10.3969/j.issn.1002-5502.2024.02.006
    [31]
    苑泽明, 于翔, 李萌, 刘冠辰. 2025. 数据资产促进了中国企业人力资本水平提升吗——基于文本分析法的经验证据. 南开管理评论 (8): 64−75. doi: 10.3969/j.issn.1008-3448.2025.08.007
    [32]
    张璠, 王竹泉, 于小悦. 2022. 政府扶持与民营中小企业“专精特新”转型——来自省级政策文本量化的经验证据. 财经科学 (1): 116−132. doi: 10.3969/j.issn.1000-8306.2022.01.010
    [33]
    张永珅, 李小波, 邢铭强. 2021. 企业数字化转型与审计定价. 审计研究 (3): 62−71. doi: 10.13939/j.cnki.zgsc.2024.14.029
    [34]
    赵涛, 张智, 梁上坤. 2020. 数字经济、创业活跃度与高质量发展——来自中国城市的经验证据. 管理世界 (10): 65−76.
    [35]
    曾江洪, 赵丹. 2025. 人工智能发展对企业劳动收入份额的影响——来自国家新一代人工智能创新发展试验区的证据. 中南大学学报(社会科学版) (5): 97−110.
    [36]
    郑志刚, 李东旭, 许荣, 林仁韬, 赵锡军. 2012. 国企高管的政治晋升与形象工程——基于N省A公司的案例研究. 管理世界 (10): 146−156. doi: 10.19744/j.cnki.11-1235/f.2012.10.013
    [37]
    周黎安. 2007. 中国地方官员的晋升锦标赛模式研究. 经济研究 (7): 36−50.
    [38]
    祝继高, 朱佳信, 李天时, 宫迪. 2023. 政府会计监督与银行信贷行为研究——基于财政部会计信息质量随机检查的证据. 管理世界 (1): 157−176, 189. doi: 10.19744/j.cnki.11-1235/f.2023.0018
    [39]
    Abaku, E. A., T. E. Edunjob, and A. C. Odimarha. 2024. Theoretical approaches to AI in supply chain optimization: Pathways to efficiency and resilience. International Journal of Science and Technology Research Archive 6(1): 92−107. doi: 10.53771/ijstra.2024.6.1.0033
    [40]
    Acemoglu, D., and D. Autor. 2011. Skills, tasks and technologies: Implications for employment and earnings. Handbook of Labor Economics 4: 1043−1171. doi: 10.3386/w16082
    [41]
    Acemoglu, D., and P. Restrepo. 2018. The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review 108(6): 1488−1542. doi: 10.1257/aer.20160696
    [42]
    Acemoglu, D., and P. Restrepo. 2020. Robots and jobs: Evidence from US labor markets. Journal of Political Economy 128(6): 2188−2244. doi: 10.1086/705716
    [43]
    Berger, P. G., and E. Ofek. 1995. Diversification’s effect on firm value. Journal of Financial Economics 37(1): 39−65. doi: 10.1016/0304-405X(94)00798-6
    [44]
    Briscoe, F., and M. Rogan. 2016. Coordinating complex work: Knowledge networks, partner departures, and client relationship performance in a law firm. Management Science 62(8): 2392−2411. doi: 10.1287/mnsc.2015.2231
    [45]
    Chen, Y. Z., H. Y. Li, and J. Y. Luo. 2022. Artificial intelligence, technological innovation and the upgrading of China’s equipment manufacturing industry. Journal of Asian Research 6(4): 30−45. doi: 10.22158/jar.v6n4p30
    [46]
    Feng, M., C. Li, and S. McVay. 2009. Internal control and management guidance. Journal of Accounting and Economics 48(2-3): 190−209. doi: 10.1016/j.jacceco.2009.09.004
    [47]
    Goodman-Bacon, A. 2021. Difference-in-differences with variation in treatment timing. Journal of Econometrics 225(2): 254−277. doi: 10.1016/j.jeconom.2021.03.014
    [48]
    Huang, Y., S. Liu, J. Gan, B. Liu, and Y. Wu. 2024. How does the construction of new generation of national AI innovative development pilot zones drive enterprise ESG development? Empirical evidence from China. Energy Economics 140, 108011. doi: 10.1016/j.eneco.2024.108011
    [49]
    Li, C., Y. Xu, H. Zheng, Z. Wang, H. Han, and L. Zeng. 2023. Artificial intelligence, resource reallocation, and corporate innovation efficiency: Evidence from China’s listed companies. Resources Policy 81, 103324. doi: 10.1016/j.resourpol.2023.103324
    [50]
    Lou, B., and L. Wu. 2021. AI on drugs: Can artificial intelligence accelerate drug development? Evidence from a large-scale examination of bio-pharma firms. Management Information Systems Quarterly 45(3): 1451−1482. doi: 10.25300/MISQ/2021/16565
    [51]
    Nazarov, Z., and A. Akhmedjonov. 2012. Education, on-the-job training, and innovation in transition economies. Eastern European Economics 50(6): 28−56. doi: 10.2753/EEE0012-8775500602
    [52]
    Pfeffer, J., and G. Salancik. 1978. The external control of organizations: A resource dependence perspective. New York: Harper & Row.
    [53]
    Rammer, C., G. P. Fernández, and D. Czarnitzki. 2022. Artificial intelligence and industrial innovation: Evidence from German firm-level data. Research Policy 51(7), 104555. doi: 10.1016/j.respol.2022.104555
    [54]
    Rickardo, G., and S. Meiriele. 2023. Artificial intelligence: Its impact on employability. World Journal of Advanced Research and Reviews 18(3): 198−203. doi: 10.30574/wjarr.2023.18.3.1056
    [55]
    Romer, P. M. 1990. Endogenous technological change. Journal of Political Economy 98(5, Part2): S71−S102.
    [56]
    Shi, J., Z. Jiang, and Z. Liu. 2023. Digital technology adoption and collaborative innovation in Chinese high-speed rail industry: Does organizational agility matter? IEEE Transactions on Engineering Management 71: 4322−4335.
    [57]
    Shleifer, A., and R. W. Vishny. 1989. Management entrenchment: The case of manager-specific investments. Journal of Financial Economics 25(1): 123−139.
    [58]
    Sun, B., A. Ruan, B. Peng and S. Liu. 2022. Pay disparities within top management teams, marketization and firms’ innovation: Evidence from China. Journal of the Asia Pacific Economy 27(4): 715−735. doi: 10.1080/13547860.2020.1865248
    [59]
    Usai, A., F. Fiano, A. M. Petruzzelli, P. Paoloni, and B. Orlando. 2021. Unveiling the impact of the adoption of digital technologies on firms’ innovation performance. Journal of Business Research 133: 327−336. doi: 10.1016/j.jbusres.2021.04.035
    [60]
    Yu, F. F. 2008. Analyst coverage and earnings management. Journal of Financial Economics 88(2): 245−271. doi: 10.1016/j.jfineco.2007.05.008

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(13)

    Article views (49) PDF downloads(27) Cited by()
    Proportional views
    Related
    Link 

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return