Abstract: From a knowledge perspective, a product constitutes a knowledge set. For a long time, academic research has conflated knowledge production in the product R&D phase with that in the product usage phase. Focusing on the issue of knowledge production during new product usage, this paper selects three cases—Comet airliner, lithography machine, and intelligent driving vehicles—and through comparative research, finds that: First, from a temporal dimension, the knowledge set of a new product comprises laboratory knowledge from the R&D phase and user knowledge from the usage phase. User knowledge, historically overlooked, refers to new knowledge generated during the usage phase that extends beyond the scope of laboratory R&D. This new knowledge arises from unforeseen actual usage scenarios and the discrepancies between designed and real-world scenarios. The production of user knowledge embodies the technological iteration of new products: in the iterative process during the usage phase, each instance of user knowledge produced serves as a knowledge reference for the next round of laboratory-based product R&D. User knowledge and laboratory knowledge are non-substitutable and complementary. Second, unlike laboratory knowledge production—where both “problem identification” and “problem solving” are undertaken by product manufacturers—user knowledge production follows the technological iteration logic of lead users raise problems—manufacturers solve problems. As new products may expose multiple issues during use, a small subset of users (i.e., lead users) bear the uncertainties, risks, and costs associated with new product adoption. Once a new product completes user knowledge production, it matures, and the broad base of ordinary users then becomes the foundation supporting the evolution of the new product into a new industry. Third, generative artificial intelligence (AI) has transformed the mode of user knowledge production. Manufacturers and users can now synchronously perceive the usage environment and user behavior, enabling manufacturers to achieve vertically integrated user knowledge production. Generative AI narrows and blurs the boundary between ordinary users and lead users by “injecting the wisdom of predecessors into users’ minds,” reducing users’ uncertainties, risks, and costs, and empowering ordinary users to become lead users involved in user knowledge production. This assists manufacturers in producing user knowledge more rapidly and with higher quality, generating “intelligent solutions” for technological iteration. Based on these findings, three implications are proposed. Firstly, a lead user supply mechanism must be established. Without lead users’ environmental perception and problem identification, manufacturers cannot complete technological iteration of new products, thereby impeding the formation of new development trajectories and industries. Secondly, the development of generative AI must be accelerated. Generative AI-empowered new products are capable of perceiving usage scenarios and collecting user behavior data more comprehensively, accurately, and rapidly. This capability enables broader participation of ordinary users in user knowledge production while promoting exponential improvements in corporate efficiency for user knowledge generation. Thirdly, the data asset management system must be rapidly improved. User knowledge production for new products—whether or not generative AI is employed—involves the use of user data. Accelerating the improvement of user data legislation and establishing a sound data property rights system will facilitate the advancement of user knowledge production.

摘要： 从知识视角看，一个产品就是一个知识集。长期以来，学术研究把产品研发阶段的知识生产和产品进入使用阶段的知识生产混为一体。本文针对新产品使用期间的知识生产问题，通过比较“彗星客机”“光刻机”和“智能驾驶汽车”案例，发现：第一，从时间维度看，新产品知识集由研发阶段的实验室知识和使用阶段的用户知识构成，过去被忽视的用户知识是在使用阶段出现的超出实验室知识范围的知识，生产用户知识就是新产品的技术迭代。第二，传统的用户知识生产遵循“领先用户提出问题－制造商解决问题”的技术迭代逻辑。第三，生成式人工智能改变了传统的用户知识生产方式，其令制造商和用户同步感知使用环境和用户行为、且让普通用户参与用户知识生产，推动制造商更快、更高质量地生产用户知识，生成技术迭代的“智慧方案”。本研究为制造商建立用户知识生产机制、加快新产品技术迭代提出了新的实践方式，对我国自主研发重大产品加速技术迭代、发展新赛道新产业提供了重要的理论价值。