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论文编号：	15536
作者编号：	1120211183
上传时间：	2025/12/3 17:36:38
中文题目：	制造型企业人机协同、知识整合能力构建及其对创新绩效的影响研究
英文题目：	Research on Human-AI Collaboration and Knowledge Integration Capability Construction and Its Influence on Innovation Performance in Manufacturing Enterprises
指导老师：	许晖
中文关键字：	人机协同；知识整合能力；企业创新绩效；人工智能；知识共享氛围；互补性资源约束
英文关键字：	Human-AI collaboration; Knowledge integration ability; Enterprise innovation performance; Artificial intelligence; Knowledge sharing atmosphere; Complementary resource constraints
中文摘要：	人工智能的快速发展正深刻重构全球制造业的技术路径与竞争格局，不仅变革企业运营模式，也影响社会结构与经济形态。我国高度重视人工智能与制造业融合，相继出台《中国制造2025》、《新一代人工智能发展规划》等政策，明确推动AI在制造领域的深度应用，以智能制造引领高质量发展。在此进程中，人机协同逐渐取代传统自动化，成为驱动转型升级的核心范式。相较于机器对人力的替代，人机协同强调人类创造力、情境判断与AI计算能力、模式识别之间的互补融合，通过任务重构与知识交互，优化生产流程、提升资源效率并增强创新响应能力。它不仅是传统制造迈向智能制造的关键桥梁，更是重塑生产组织方式与产业价值链的核心动力。通过整合人类隐性经验与AI显性知识，人机协同开辟了新型生产范式，为企业应对复杂环境提供持续动能。然而，在智能化转型过程中，人类与AI在知识表征、更新机制与认知逻辑上的差异，导致知识整合与协同转化面临深层矛盾。如何构建有效的知识整合机制，实现多主体知识资源的协同演化与创新转化，已成为制造企业在动态竞争中构筑可持续竞争优势的战略关键。 针对制造型企业数智化转型行动、知识资源整合策略，以及创新绩效影响因素的相关研究，为理解人工智能时代下制造型企业人机协同实践提供了有益的理论洞见。然而，制造型企业在人工智能时代下呈现出的异质性路径亟需进一步的探讨。一方面，人工智能时代下制造型企业人机协同微观机理及其作用机制有待揭示。尽管现有文献对人机协同研究做出了有益探索，但主要聚焦于社会伦理、概念论述等主题，缺乏深入理论探讨，对于企业人机协同内在机制仍不清楚，人与智能系统之间的动态关系尚未形成系统化的理论框架；另一方面，人工智能时代下制造型企业知识整合能力构建机制有待挖掘。现有关于知识整合机制的研究大多停留在宏观层面，缺乏对企业内部知识整合路径、关键影响因素及其动态演化过程的系统性剖析，对于制造型企业如何通过构建知识整合能力实现人与AI等多主体知识资源深度耦合并作用于企业创新绩效的“黑箱”仍未得到有益的解释，亟需结合智能制造场景，深入挖掘人工智能环境下制造型企业知识整合能力的形成机制，为其实现从技术优势向能力优势的转化提供理论支持和实践指导。 基于此，本文针对人工智能时代下制造型企业知识整合能力构建模式的独特性，提出并检验“人机协同——知识整合能力构建——企业创新绩效”的理论框架，揭示了企业在智能化转型升级过程中人与AI等多主体间知识资源创新转化的过程机制，主要得出以下结论：（1）人机协同是制造型企业应对人工智能时代下新兴技术革新挑战的关键行动，为制造型企业提升创新绩效提供了必要的价值创造路径，机械型人机协同、思考型人机协同与情感型人机协同对于创新绩效不同维度的作用具有差异性；（2）知识整合能力是制造型企业应对人工智能时代下人与AI等多主体知识资源融合转化的关键要素，基于过程视角，将知识整合能力构建过程划分为标准化解码、模块化集成与迭代化更新三个维度，在人机协同与企业创新绩效之间起着重要的中介作用；（3）知识共享氛围是制造型企业数智化战略选择的制度规范性情境因素，在人机协同与知识整合能力之间具有重要的调节效应且呈现出较大的差异性；（4）互补性资源约束是制造型企业创新表现与市场竞争不可忽视的结构约束性情境因素，在知识整合能力与企业创新绩效之间具有重要的调节效应且呈现出较大的差异性。 本文的理论贡献主要体现在以下四个方面：（1）通过对制造企业智能化转型中机械型、思考型与情感型人机协同行为模式的识别与解析，揭示了人机协同的多维构成及其作用机理，拓展了人机交互在组织知识创造过程中的微观理论边界，为理解人工智能嵌入背景下人类与智能体之间的协同逻辑提供了新的理论视角；（2）基于动态过程观，构建了知识整合能力形成的三阶段分析框架——标准化解码、模块化集成与迭代化更新，突破了传统知识整合研究局限于静态资源配置的局限，推动该理论向人机共融的复杂系统情境延伸，阐明了组织如何通过阶段性知识转化机制实现能力跃迁；（3）系统揭示了人机协同经由知识整合能力影响创新绩效的作用路径，将企业创新绩效的生成机制置于智能技术深度融合的现实情境之中，丰富了智能化转型背景下创新驱动因素的理论体系，并回应了关于人机协同效能转化机制的学术关注；（4）引入知识共享氛围作为制度性情境条件与互补性资源约束作为结构性情境条件，揭示了二者在关键路径关系中的差异化调节效应，通过将宏观转型背景具象化为可操作的企业层面变量，弥补了既有研究对情境嵌入性关注不足所导致的理论解释偏差。
英文摘要：	The rapid development of artificial intelligence is profoundly reshaping the technological path and competitive landscape of the global manufacturing industry. It not only transforms the operation mode of enterprises but also influences the social structure and economic form. China attaches great importance to the integration of artificial intelligence and manufacturing. It has successively introduced policies such as "Made in China 2025" and the "Next Generation Artificial Intelligence Development Plan", clearly promoting the in-depth application of AI in the manufacturing field and leading high-quality development with intelligent manufacturing. In this process, human-machine collaboration has gradually replaced traditional automation and become the core paradigm driving transformation and upgrading. Compared with the replacement of human labor by machines, human-machine collaboration emphasizes the complementary integration of human creativity, situational judgment, AI computing power and pattern recognition. Through task reconstruction and knowledge interaction, it optimizes production processes, improves resource efficiency and enhances innovation response capabilities. It is not only a key bridge for traditional manufacturing to move towards intelligent manufacturing, but also the core driving force for reshaping the production organization mode and the industrial value chain. By integrating human implicit experience with AI explicit knowledge, human-machine collaboration has opened up a new production paradigm, providing continuous impetus for enterprises to cope with complex environments. However, during the process of intelligent transformation, the differences between humans and AI in knowledge representation, update mechanisms, and cognitive logic have led to deep-seated contradictions in knowledge integration and collaborative transformation. How to establish an effective knowledge integration mechanism to achieve the collaborative evolution and innovative transformation of multi-subject knowledge resources has become a strategic key for enterprises to build sustainable competitive advantages. Research on the digital and intelligent transformation actions of manufacturing enterprises, knowledge resource integration strategies, and the influencing factors of innovation performance provides beneficial theoretical insights for understanding the human-machine collaborative practices of manufacturing enterprises in the era of artificial intelligence. However, the heterogeneous paths presented by manufacturing enterprises in the era of artificial intelligence urgently need further exploration. On the one hand, the micro-mechanism and action mechanism of human-machine collaboration in manufacturing enterprises in the era of artificial intelligence remain to be revealed. Although the existing literature has made beneficial explorations in the research of human-machine collaboration, it mainly focuses on themes such as social ethics and conceptual discourse, lacking in-depth theoretical discussions. The internal mechanism of human-machine collaboration in enterprises remains unclear, and a systematic theoretical framework for the dynamic relationship between humans and intelligent systems has not yet been formed. On the other hand, the mechanism for building knowledge integration capabilities of manufacturing enterprises in the era of artificial intelligence remains to be explored. Most of the existing research on the knowledge integration mechanism remains at the macro level, lacking a systematic analysis of the internal knowledge integration paths, key influencing factors and their dynamic evolution processes of enterprises. There is still no beneficial explanation for how manufacturing enterprises can achieve the deep coupling of multi-subject knowledge resources such as humans and AI through the construction of knowledge integration capabilities and apply them to the "black box" of enterprise innovation performance. It is urgently necessary to combine the scenarios of intelligent manufacturing and deeply explore the formation mechanism of knowledge integration capabilities of manufacturing enterprises in the artificial intelligence environment, providing theoretical support and practical guidance for their transformation from technological advantages to capability advantages. Based on this, this paper, in view of the uniqueness of the knowledge integration capability construction model of manufacturing enterprises in the era of artificial intelligence, proposes and tests the theoretical framework of "human-machine collaboration - knowledge integration capability construction - enterprise innovation performance", reveals the process mechanism of knowledge resource innovation and transformation among multiple subjects such as humans and AI in the process of intelligent transformation and upgrading of enterprises, and mainly draws the following conclusions: (1) Human-machine collaboration is a key action for manufacturing enterprises to address the challenges of emerging technological innovations in the era of artificial intelligence, providing a necessary value creation path for manufacturing enterprises to enhance their innovation performance. The effects of mechanical human-machine collaboration, thinking human-machine collaboration, and emotional human-machine collaboration on different dimensions of innovation performance are different. (2) Knowledge integration capability is a key element for manufacturing enterprises to cope with the integration and transformation of multi-subject knowledge resources such as human and AI in the era of artificial intelligence. From a process perspective, the construction process of knowledge integration capability is divided into three dimensions: standardized decoding, modular integration, and iterative update, which plays an important mediating role between human-machine collaboration and enterprise innovation performance. (3) The atmosphere of knowledge sharing is a normative situational factor for the digital and intelligent strategic choices of manufacturing enterprises. It has an important moderating effect and shows significant differences between human-machine collaboration and knowledge integration capabilities. (4) Complementary resource constraints are structural and restrictive situational factors that cannot be ignored in the innovation performance and market competition of manufacturing enterprises. They have an important moderating effect between knowledge integration ability and enterprise innovation performance and show significant differences. The theoretical contributions of this article are mainly reflected in the following four aspects: (1) Through the identification and analysis of mechanical, thinking and emotional human-machine collaborative behavior patterns in the intelligent transformation of manufacturing enterprises, the multi-dimensional composition and mechanism of human-machine collaboration are revealed, and the micro-theoretical boundaries of human-machine interaction in the process of organizational knowledge creation are expanded. It provides a new theoretical perspective for understanding the collaborative logic between humans and agents in the context of artificial intelligence embedding. (2) Based on the dynamic process perspective, a three-stage analysis framework for the formation of knowledge integration capabilities - standardized decoding, modular integration, and iterative update - was constructed. This breaks through the limitation of traditional knowledge integration research being confined to static resource allocation, promotes the extension of this theory to the complex system context of human-machine integration, and clarifies how organizations can achieve capability leap through phased knowledge transformation mechanisms. (3) The system reveals the path through which human-machine collaboration influences innovation performance through knowledge integration capabilities, places the generation mechanism of enterprise innovation performance in the realistic context of the deep integration of intelligent technologies, enriches the theoretical system of innovation-driven factors in the context of intelligent transformation, and responds to academic concerns regarding the transformation mechanism of human-machine collaboration efficiency. (4) By introducing the knowledge-sharing atmosphere as an institutional situational condition and complementary resource constraints as a structural situational condition, the differentiated regulatory effects of the two in the critical path relationship are revealed. By concretizing the macro transformation background into operational enterprise-level variables, the theoretical interpretation deviation caused by the insufficient attention to situational embedding in existing studies is remedied.
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