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产品创新度与创新效率的矛盾是新产品开发中长期存在的难题，生成式人工智能为解决该难题提供了新的机会和条件。目前，鲜有研究将新产品开发实践与生成式人工智能创新相结合，探讨基于生成式人工智能的新产品开发人智融合机制，导致对生成式人工智能情境下新产品开发如何形成新突破的理解有限。本文界定了新产品开发人智融合的概念，总结提炼出人智融合区别于人机协同的两个主要特征。基于行动者网络理论，本文阐述了基于生成式人工智能的新产品开发人智融合机制，即开发者通过提示词工程引导、大数据筛选工程微调生成式人工智能，以及生成式人工智能通过知识发现工程形成对开发者的反馈。本文的研究结论深化了新产品开发中的人智融合理论研究，为企业通过生成式人工智能解决产品创新度与创新效率的矛盾提供了实践启示。
Abstract：The tension between product innovativeness and innovation efficiency has been one of the central challenges in new product development(NPD) and a key factor contributing to the accumulation of inventory in China's manufacturing industry. The application of generative artificial intelligence(GenAI) in NPD, combined with management innovation, not only transforms developers' cognitive processes but also extends their capability boundaries. Research on GenAI has primarily focused on topics related to strategy, marketing, and human resource management. In contrast, studies on NPD have predominantly examined issues such as agile development, integrated development, user and supplier involvement, or marketing-manufacturing integration. Overall, there has been limited research integrating NPD practices with GenAI innovation to explore the mechanisms of human-AI integration in GenAI-driven NPD. This gap has resulted in a limited understanding of how breakthrough activities in NPD can emerge within the context of GenAI.This study defines the concept of human-AI integration in NPD and identifies two distinctive characteristics that differentiate it from human-machine collaboration. Drawing on the actor-network theory, this study elaborates on the mechanisms of human-AI integration, including how developers use prompt engineering to guide, big data screening engineering to fine-tune generative AI models, and knowledge discovery engineering to leverage AI feedback for further development. The findings contribute to the theoretical discourse on human-AI integration in NPD and provide practical insights for enterprises to leverage generative AI to reconcile the tension between product innovativeness and innovation efficiency.The theoretical implications for NPD research are as follows. First, existing NPD research lacks an in-depth analysis of how GenAI innovation interacts with NPD practices. This study addresses this gap by defining the connotation and extension of human-AI integration in NPD and identifying the key characteristics that differentiate human-AI integration from conventional human-machine collaboration. By analyzing the new patterns of human-AI integration in the GenAI era,this study fills an important theoretical gap in the field. Second, in the field of business management, research on GenAI has primarily concentrated on topics related to strategy, marketing, and human resource management, with limited focus on the mechanisms through which GenAI interacts with developers in the context of product innovation. This study constructs a theoretical model of human-AI integration mechanisms in GenAI-driven NPD, offering an initial exploration into the “black box” of interactions between developers and GenAI, and opening up new avenues and topics for research on human-AI integration in GenAI-based NPD.
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基本信息:
DOI：10.19654/j.cnki.cjwtyj.2025.05.002
中图分类号:TP18;F273.2
引用信息:
[1]谢康,卢鹏,夏正豪.基于生成式人工智能的新产品开发人智融合机制[J].财经问题研究,2025,No.498(05):15-26.DOI:10.19654/j.cnki.cjwtyj.2025.05.002.
基金信息:
国家自然科学基金专项项目“基于生成式人工智能的新产品开发人智融合研究”（72442012）;国家自然科学基金重点项目“制造企业数字化转型与管理适应性变革研究”（72032009）; 广东省自然科学基金面上项目“情境化视角下人工智能驱动产品创新的价值与机制研究”（2025A1515011150）