企业人工智能创新前因组态及其高质量发展效应研究

摘要/Abstract

摘要： 企业人工智能（AI）创新“何以实现，又如何致远”是极具价值但知之甚少的研究问题。本文基于TOE框架，结合fsQCA和PSM方法，以2018—2023年中国上市公司为样本，检验技术条件（技术广度与技术深度）、组织条件（ICT能力、AI存量与AI年龄）、环境条件（地区AI生态环境与行业ICT创新氛围）对企业人工智能创新的组态效应，以及不同组态对企业高质量发展的影响。研究发现：①AI存量是高水平人工智能创新的必要条件。②高水平人工智能创新存在3种驱动路径，即动态学习强创新、自力更生谋创新与内外协同促创新。③增量效应检验表明，相对于7个条件的单独效应，组态路径对人工智能创新有更好的解释力度。④多时段QCA分析结果显示，组态路径的变化分别呈现出“涌现轨迹”“缓冲主导轨迹”和“主导轨迹”3种类型。⑤引致高水平人工智能创新的路径对企业高质量发展存在异质性影响。本文从组态视角拓展了人工智能创新前因的研究，并为AI“现代生产率悖论”提供新的解释视角，为企业如何利用人工智能创新培育和发展新质生产力提供启示。

关键词: TOE框架, 人工智能创新, 模糊集定性比较分析（fsQCA）, 高质量发展

Abstract: A critical gap remains in understanding the valuable issue of how to achieve AI innovation and advance it further. Based on the TOE framework, this study employs fsQCA and PSM methods to analyze Chinese listed firms (2018—2023), examining how configurations of technical factors (technological breadth, technological depth), organizational factors (ICT capabilities, AI stock, AI age), and environmental factors (regional AI ecological environment, industry ICT innovation atmosphere) influence AI innovation and subsequently shape firms’ high-quality development. Our findings reveal that AI stock serves as a necessary condition, and identify three high-level innovation paths: dynamic learning, self striving, and collaboration. The incremental effect test results of the configuration show that compared with the net effect of antecedent conditions, the configuration results have a better explanatory strength for AI innovation. The multi-period QCA results indicate that the changes in the three paths respectively present “emergent trajectories”, “buffer-dominant trajectories”, and “dominant trajectories”. High-level AI innovation triggered by multiple configurations has different effects on the high-quality development. The paper expands the research on the antecedents of AI innovation from a configuration perspective, provides a new explanatory for the “modern productivity paradox” of AI, and provides inspiration for firms on how to leverage AI innovation to foster and develop new qualitative productivity.

Key words: TOE framework, artificial intelligence innovation, fuzzy set qualitative comparative analysis (fsQCA), high quality development

马海燕, 黎玉杰, 池毛毛. 企业人工智能创新前因组态及其高质量发展效应研究[J]. 管理评论, 2025, 37(10): 76-87.

Ma Haiyan, Li Yujie, Chi Maomao. Research on the Antecedent Configuration and High-quality Development Effect of Firm’s Artificial Intelligence Innovation[J]. Management Review, 2025, 37(10): 76-87.

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