人工智能应用对企业竞争地位的影响

王梅玲, 沈坤荣

科研管理 ›› 2026, Vol. 47 ›› Issue (7) : 79-87.

PDF(1024 KB)
PDF(1024 KB)
科研管理 ›› 2026, Vol. 47 ›› Issue (7) : 79-87. DOI: 10.19571/j.cnki.1000-2995.2026.07.008  CSTR: 32148.14.kygl.2026.07.008

人工智能应用对企业竞争地位的影响

作者信息 +

The impact of artificial intelligence applications on corporate competitive position

Author information +
文章历史 +

摘要

随着全球市场竞争日益激烈以及技术飞速发展,企业面临着如何快速有效地利用人工智能这一颠覆性技术提升竞争地位的紧迫挑战。本文聚焦于人工智能在企业提升竞争地位中的应用,详细阐释了人工智能对企业竞争地位的影响机理,并采用2013—2023年中国深沪两市A股上市企业的面板数据,通过构建双向固定效应模型和中介效应模型,系统检验了人工智能对企业竞争地位的影响及其多重作用机制。研究结果表明,人工智能的应用显著提升了企业竞争地位,且这一结论在经过一系列稳健性检验和内生性检验后仍然成立。异质性分析发现,人工智能对国有企业、风险承担能力较高的企业、环境不确定性较高的企业、数字产业的企业以及第二、三产业的企业竞争地位提升效果更为显著。机制分析表明,人工智能通过全要素生产率的提升、政府补贴的增加以及分析师关注度的提高等三条主要路径显著影响企业的竞争地位。本文的研究结果不仅为理解人工智能在提升企业竞争地位中的作用机制提供了新视角,也为推动人工智能技术在企业中的应用和优化提供了理论基础和决策参考。

Abstract

With the intensifying competition in the global market and the rapid advancements in technology, companies face the urgent challenge of how to swiftly and effectively leverage artificial intelligence(AI), a disruptive technology, to enhance their competitiveness. This study focuses on the application of AI in improving corporate competitive position, systematically elucidating the mechanisms through which AI affects competitive position. Using panel data from A-share listed companies in the Shanghai and Shenzhen stock markets from 2013 to 2023, we employ two-way fixed effect models and mediating effect models to systematically examine the impact of AI on corporate competitive position and its multiple mechanisms. The results indicate that the application of AI significantly enhances corporate competitive position, and this conclusion remains robust after a series of robustness and endogeneity tests. Heterogeneity analysis reveals that AI has a more pronounced effect on the competitive position of state-owned enterprises, firms with higher risk-taking capacity, those facing greater environmental uncertainty, enterprises in the digital industry, and enterprises in the secondary and tertiary sectors. Mechanism analysis shows that AI significantly influences competitive position through three main pathways, including improvements in total factor productivity, access to government subsidies, and increased analyst attention. The findings of this study not only provide a new perspective for understanding the role of AI in enhancing corporate competitiveness but also offer a theoretical foundation and decision-making reference for promoting the application and optimization of AI technologies in businesses.

关键词

人工智能 / 竞争地位 / 风险承担 / 机制分析

Key words

artificial intelligence / competitive position / risk-taking / mechanism analysis

引用本文

导出引用
王梅玲, 沈坤荣. 人工智能应用对企业竞争地位的影响[J]. 科研管理. 2026, 47(7): 79-87 https://doi.org/10.19571/j.cnki.1000-2995.2026.07.008
Wang Meiling, Shen kunrong. The impact of artificial intelligence applications on corporate competitive position[J]. Science Research Management. 2026, 47(7): 79-87 https://doi.org/10.19571/j.cnki.1000-2995.2026.07.008
中图分类号: F272.5   

参考文献

[1]
Kulkov I, Kulkova J, Rohrbeck R, et al. Artificial intelligence-driven sustainable development:Examining organizational,technical,and processing approaches to achieving global goals[J]. Sustainable Development, 2024, 32(3):2253-2267.
[2]
武亚军. “战略框架式思考”“悖论整合”与企业竞争优势:任正非的认知模式分析及管理启示[J]. 管理世界, 2013(4):150-163.
Wu Yajun. “Strategic framework thinking” “paradox integration” and corporate competitive advantage:An analysis of Ren Zhengfei’s cognitive model and management implications[J]. Journal of Management World, 2013(4):150-163.
[3]
李维安, 韩忠雪. 民营企业金字塔结构与产品市场竞争[J]. 中国工业经济, 2013(1):77-89.
Li Weian, Han Zhongxue. Pyramid structure of private enterprises and competition on product market[J]. China Industrial Economics, 2013(1):77-89.
[4]
Tu Y, Wu W. How does green innovation improve enterprises’ competitive advantage? The role of organizational learning[J]. Sustainable Production and Consumption, 2021, 26:504-516.
[5]
Pan M, Bai M, Ren X. Does internet convergence improve manufacturing enterprises’ competitive advantage? Empirical research based on the mediation effect model[J]. Technology in Society, 2022, 69:101944.
[6]
Wei Y, Zhu R, Tan L. Emission trading scheme,technological innovation,and competitiveness:Evidence from China’s thermal power enterprises[J]. Journal of Environmental Management, 2022, 320:115874.
[7]
Xie M, Ding L, Xia Y, et al. Does artificial intelligence affect the pattern of skill demand? Evidence from Chinese manufacturing firms[J]. Economic Modelling, 2021, 96:295-309.
[8]
Haftor D M, Costa-Climent R, Ribeiro-navarrete S. Firms’ use of predictive artificial intelligence for economic value creation and appropriation[J]. International Journal of Information Management, 2024, 79:102836.
[9]
Babina T, Fedyk A, He A, et al. Artificial intelligence,firm growth,and product innovation[J]. Journal of Financial Economics, 2024, 151:103745.
[10]
宋华, 韩梦玮, 沈凌云. 人工智能在供应链韧性塑造中的作用:基于迈创全球售后供应链管理实践的案例研究[J]. 中国工业经济, 2024(5):174-192.
Song Hua, Han Mengwei, Shen Lingyun. How does AI play a role in shaping supply chain resilience:A case study based on maitrox’s global after-sales supply chain management practice[J]. China Industrial Economics, 2024(5):174-192.
[11]
Wamba S F. Impact of artificial intelligence assimilation on firm performance:The mediating effects of organizational agility and customer agility[J]. International Journal of Information Management, 2022, 67:102544.
[12]
Roberts D L, Candi M. Artificial intelligence and innovation management:Charting the evolving landscape[J]. Technovation, 2024, 136:103081.
[13]
马鸿佳, 林樾, 苏中锋, 等. 人工智能可供性、智能制造平台价值共创与制造企业数字化转型绩效[J]. 中国工业经济, 2024(6):155-173.
Ma Hongjia, Lin Yue, Su Zhongfeng, et al. Artificial intelligence affordance,intelligent manufacturing platform value co-creation and digital transformation performance of manufacturing enterprises[J]. China Industrial Economics, 2024(6):155-173.
[14]
Lui A K, Lee M C, Nga E W. Impact of artificial intelligence investment on firm value[J]. Annals of Operations Research, 2022, 308(1):373-388.
[15]
Mikalef P, Isiam N, Parida V, et al. Artificial intelligence(AI) competencies for organizational performance:A B2B marketing capabilities perspective[J]. Journal of Business Research, 2023, 164:113998.
[16]
邵云飞, 陈燕萍, 吴晓波, 等. 从“研发”到“市场”:链主企业如何实现关键核心技术的商业化?[J]. 管理世界, 2024, 40(12):19-43.
Shao Yunfei, Chen Yanping, Wu Xiaobo, et al. From “R&D” to “Market”:How can leading-chain enterprises commercialize key core technology?[J]. Journal of Management World, 2024, 40(12):19-43.
[17]
Liu J, Chang H, Forrest J Y L, et al. Influence of artificial intelligence on technological innovation:Evidence from the panel data of China’s manufacturing sectors[J]. Technological Forecasting and Social Change, 2020, 158:120142.
[18]
廖高可, 李庭辉. 人工智能在金融领域的应用研究进展[J]. 经济学动态, 2023(3):141-158.
Liao Gaoke, Li Tinghui. Research progress on the applications of artificial intelligence in finance[J]. Economic Perspectives, 2023(3):141-158.
[19]
俞立平, 章美娇, 王作功. 中国地区高技术产业政策评估及影响因素研究[J]. 科学学研究, 2018, 36(1):28-36.
摘要
我国出台了众多产业政策以推动高技术产业发展,但政策执行的效果有待考察,评估并提高技术产业政策绩效应是进一步促进高技术产业发展的关键。文章通过DEA-Malmquist模型测度了高技术产业政策绩效,并运用面板回归和分位数回归模型研究其影响因素。研究发现:我国高技术产业政策绩效整体上呈现时期变迁;市场化进程显著降低高技术产业政策绩效,企业效益、高技术产业发展水平等因素显著提高产业政策绩效;当产业政策绩效处于较低的水平时,应从高技术产业发展水平入手提高政策绩效,而当产业政策绩效已处于较高水平时,政策重点应是高技术企业效益和政策执行力。, 我国出台了众多产业政策以推动高技术产业发展,但政策执行的效果有待考察,评估并提高技术产业政策绩效应是进一步促进高技术产业发展的关键。文章通过DEA-Malmquist模型测度了高技术产业政策绩效,并运用面板回归和分位数回归模型研究其影响因素。研究发现:我国高技术产业政策绩效整体上呈现时期变迁;市场化进程显著降低高技术产业政策绩效,企业效益、高技术产业发展水平等因素显著提高产业政策绩效;当产业政策绩效处于较低的水平时,应从高技术产业发展水平入手提高政策绩效,而当产业政策绩效已处于较高水平时,政策重点应是高技术企业效益和政策执行力。
Yu Liping, Zhang Meijiao, Wang Zuogong. The study of the performance evaluation of regional hi-tech industrial policies and the influencing factors[J]. Studies in Science of Science, 2018, 36(1):28-36.
[20]
顾元媛, 沈坤荣. 地方政府行为与企业研发投入:基于中国省际面板数据的实证分析[J]. 中国工业经济, 2012(10):77-88.
Gu Yuanyuan, Shen Kunrong. The effect of local governments’ behavior on corporate R&D investment:Empirical analysis based on China’s provincial panel data[J]. China Industrial Economics, 2012(10):77-88.
[21]
何筠, 熊孜贤. 人工智能技术应用对制造业企业创新绩效的影响[J]. 科研管理, 2025, 46(5):13-22.
He Yun, Xiong Zixian. The impacts of artificial intelligence technology applications on the innovation performance of manufacturing enterprises[J]. Science Research Management, 2025, 46(5):13-22.
[22]
Tao W, Weng S, Chen X, et al. Artificial intelligence-driven transformations in low-carbon energy structure:Evidence from China[J]. Energy Economics, 2024, 136:107719.
[23]
Cowen A, Groysberg B, Healy P. Which types of analyst firms are more optimistic?[J]. Journal of Accounting and Economics, 2006, 41(1-2):119-146.
[24]
周鹏, 王卓, 谭常春, 等. 数字技术创新的价值:基于并购视角和机器学习方法的分析[J]. 中国工业经济, 2024(2):137-154.
Zhou Peng, Wang Zhuo, Tan Changchun, et al. The value of digital technology innovation:From the M&A perspective and machine learning methods[J]. China Industrial Economics, 2024(2):137-154.
[25]
朱红军, 何贤杰, 陶林. 中国的证券分析师能够提高资本市场的效率吗:基于股价同步性和股价信息含量的经验证据[J]. 金融研究, 2007(2):110-121.
Zhu Hongjun, He Xianjie, Tao Lin. Can securities analysts improve the efficiency of capital market in China:Empirical evidence based on stock price synchronicity and the information content of stock prices[J]. Journal of Financial Research, 2007(2):110-121.
[26]
Harrison J S, Wicks A C. Stakeholder theory,value,and firm performance[J]. Business Ethics Quarterly, 2013, 23(1):97-124.
This paper argues that the notion of value has been overly simplified and narrowed to focus on economic returns. Stakeholder theory provides an appropriate lens for considering a more complex perspective of the value that stakeholders seek as well as new ways to measure it. We develop a four-factor perspective for defining value that includes, but extends beyond, the economic value stakeholders seek. To highlight its distinctiveness, we compare this perspective to three other popular performance perspectives. Recommendations are made regarding performance measurement for both academic researchers and practitioners. The stakeholder perspective on value offered in this paper draws attention to those factors that are most closely associated with building more value for stakeholders, and in so doing, allows academics to better measure it and enhances managerial ability to create it.
[27]
陈志斌, 王诗雨. 产品市场竞争对企业现金流风险影响研究:基于行业竞争程度和企业竞争地位的双重考量[J]. 中国工业经济, 2015(3):96-108.
Chen Zhibin, Wang Shiyu. Impact of product market competition on corporate cash flow risk:Analysis based on the competition degree of industry and the competitive position of enterprise[J]. China Industrial Economics, 2015(3):96-108.
[28]
姚加权, 张锟澎, 郭李鹏, 等. 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[J]. 管理世界, 2024, 40(2):101-116.
Yao Jiaquan, Zhang Kunpeng, Guo Lipeng, et al. How does artificial intelligence improve firm productivity?Based on the perspective of labor skill structure adjustment[J]. Journal of Management World, 2024, 40(2):101-116.
[29]
程翔, 张瑞, 张峰. 科技金融政策是否提升了企业竞争力?:来自高新技术上市公司的证据[J]. 经济与管理研究, 2020, 41(8):131-144.
Cheng Xiang, Zhang Rui, Zhang Feng. Whether scientific and technological financial policies improve corporate competitiveness? Evidence based on high-tech listed companies[J]. Research on Economics and Management, 2020, 41(8):131-144.
[30]
陈仕华, 王雅茹. 企业并购依赖的缘由和后果:基于知识基础理论和成长压力理论的研究[J]. 管理世界, 2022, 38(5):156-175.
Chen Shihua, Wang Yaru. The antecedents and consequences of firms’ M&A dependence:A study based on knowledge-based theory and growth-pressure theory[J]. Journal of Management World, 2022, 38(5):156-175.
[31]
何瑛, 于文蕾, 杨棉之. CEO复合型职业经历、企业风险承担与企业价值[J]. 中国工业经济, 2019(9):155-173.
He Ying, Yu Wenlei, Yang Mianzhi. CEOs with rich career experience,corporate risk-taking and the value of enterprises[J]. China Industrial Economics, 2019(9):155-173.
[32]
周翔, 叶文平, 李新春. 数智化知识编排与组织动态能力演化:基于小米科技的案例研究[J]. 管理世界, 2023, 39(1):138-157.
Zhou Xiang, Ye Wenping, Li Xinchun. Digit-intellectualized knowledge orchestration and the evolution of organizational dynamic capabilities:A case study of Xiaomi technology[J]. Journal of Management World, 2023, 39(1):138-157.
[33]
申慧慧, 于鹏, 吴联生. 国有股权、环境不确定性与投资效率[J]. 经济研究, 2012(7):114-127.
Shen Huihui, Yu Peng, Wu Liansheng. State ownership,environment uncertainty and investment efficiency[J]. Economic Research Journal, 2012(7):114-127.
[34]
曾国安, 苏诗琴, 彭爽. 企业杠杆行为与技术创新[J]. 中国工业经济, 2023(8):155-173.
Zeng Guoan, Su Shiqin, Peng Shuang. Corporate leverage and technological innovation[J]. China Industrial Economics, 2023(8):155-173.
[35]
Lu Z, Wang N, Ma X, et al. Evaluation standards of intelligent technology based on financial alternative data[J]. Journal of Innovation & Knowledge, 2022, 7(4),100229.
[36]
江艇. 因果推断经验研究中的中介效应与调节效应[J]. 中国工业经济, 2022(5):100-120.
Jiang Ting. Mediating effects and moderating effects in causal inference[J]. China Industrial Economics, 2022(5):100-120.

基金

国家社会科学基金重大项目:“推动经济实现质的有效提升和量的合理增长研究”(24&ZD044,2024.12—2028.12)
国家自然科学基金青年基金项目:“政府补贴对制造业绿色创新韧性的影响机理、效果评估与政策优化研究”(72304141,2024.01—2026.12)
江苏省社会科学基金一般项目:“数字化赋能江苏制造业绿色创新韧性的机制与路径研究”(23EYB002,2023.07—2025.12)

PDF(1024 KB)

Accesses

Citation

Detail

段落导航
相关文章

/