科研管理 ›› 2025, Vol. 46 ›› Issue (8): 38-46.DOI: 10.19571/j.cnki.1000-2995.2025.08.004

• 论文 • 上一篇    下一篇

人工智能应用对探索式创新节奏的非线性作用

肖睿聪,吴伟伟   

  1. 哈尔滨工业大学商学院,黑龙江 哈尔滨150001
  • 收稿日期:2024-07-18 修回日期:2025-03-19 出版日期:2025-08-20 发布日期:2025-08-14
  • 通讯作者: 吴伟伟
  • 基金资助:
    国家自然科学基金项目:“人工智能驱动企业创新链产业链深度融合的路径机制与治理模式”(72472039, 2025.01—2028.12);国家自然科学基金项目:“大数据能力驱动的突破性技术创新行为触发路径与演化机制”(72072047, 2021.01—2024.12);黑龙江省高等教育学会高等教育研究课题:“基于创新创业的教育链、创新链与产业链协同发展研究”(23GJYBC011, 2023.08—2026.08);中央高校基本科研业务费专项资金项目:“面向新动能塑造的技术管理能力对突破性创新行为的作用机制研究”(HIT-HSS-ESD202310, 2023.01—2025.12);哈尔滨工业大学研究生教育教学改革研究项目:“‘创新强基’导向的技术经济及管理研究生成长规律与培养模式”(23MS011, 2023.07—2025.07)。

Nonlinear effect of artificial intelligence application on exploratory innovation rhythm

Xiao Ruicong, Wu Weiwei   

  1. Business School, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
  • Received:2024-07-18 Revised:2025-03-19 Online:2025-08-20 Published:2025-08-14

摘要:    适当的探索式创新节奏是提升企业创新效率和创新成功率的关键,对企业抓住创新时机,实现创新驱动发展战略具有重要意义。数智时代人工智能技术的发展和应用为新一轮技术革命和产业转型带来了诸多机遇,而现有研究在企业层面对人工智能应用与探索式创新节奏关系的考察尚有不足。本文基于组织信息处理理论和2011至2020年中国A股上市公司的数据,对人工智能应用与探索式创新节奏的关系进行了实证检验。研究表明:(1)人工智能应用对探索式创新节奏产生倒U型影响;(2)高管团队调节定向对人工智能应用与探索式创新节奏间关系发挥调节作用;(3)高管团队促进定向和预防定向对人工智能应用与探索式创新节奏间关系具有异质性的调节机制;(4)人工智能应用对探索式创新节奏的倒U型影响在非国有企业、成熟企业、低技术行业和低市场竞争度环境中的企业中更强。本文加深了对微观企业层面应用人工智能影响探索式创新节奏的更全面认识,为高管团队决策偏好差异对企业组织信息处理及其匹配过程的影响机制提供了深刻洞察。

关键词: 人工智能应用, 探索式创新, 创新节奏, 高管团队调节定向, 组织信息处理

Abstract:    An appropriate exploratory innovation pace is crucial for enhancing corporate innovation efficiency and success rates, and it is of great significance for seizing innovation opportunities and realizing innovation-driven development. In the era of digital intelligence, the development and application of artificial intelligence (AI) technology have brought many opportunities for a new round of technological revolution and industrial transformation. However, existing research is insufficient in examining the relationship between the AI application and exploratory innovation pace at the firm level. Based on the organizational information processing theory and the data from Chinese A-share listed companies from 2011 to 2020, this paper conducted an empirical analysis on the relationship between AI application and exploratory innovation pace. The results showed that: (1) AI application has an inverted U-shaped impact on exploratory innovation pace; (2) Top management team regulatory focus moderates the relationship between AI application and exploratory innovation pace; (3) TMT promotion focus and prevention focus have heterogeneous regulatory effects on the relationship between AI application and exploratory innovation pace; and (4) The inverted U-shaped impact of AI application on exploratory innovation pace is stronger in non-state-owned enterprises, mature enterprises, low-tech industries, and enterprises in low-competition market environments. By doing so, this paper has deepened the understanding of the impact of AI application on exploratory innovation pace at the micro-corporate level and it will provide profound insights into the impact mechanism of top management team preference differences on corporate information processing and matching processes.

Key words: artificial intelligence application, exploratory innovation, innovation rhythm, TMT regulatory focus, organizational information processing