Abstract: The paper employ literature review to address theoretical questions related to user innovation under the paradigm of artificial intelligence (AI). It begins by defining AI and user innovation and then conducts a literature search, using specific keywords and inclusion criteria to identify relevant studies. The search strategy is designed to be comprehensive yet accurate, focusing on peer-reviewed studies published in English from 1991 to 2023. The authors use the Gioia methodology for data analysis. This methodology involves three stages: first-order categories, second-order themes, and overall dimensions, moving from a strict focus on empirical data to developing theoretical insights. The purpose of this review is to bridge the gap between AI and user innovation research by identifying key themes and constructing an integrated framework that delineates the reciprocal impact of AI on user innovation and vice versa. The review aims to answer research questions such as "What impact does AI have on user innovation? Why and how can users improve or even innovate AI?" and to pave the way for future research focusing on the interplay of AI, enterprises, users, and multi-actor engagement. The study identifies eight key themes and constructs a comprehensive research framework that provides a systematic understanding of AI and user innovation. The study found that the paths through which AI acts on user innovation include: ①Enterprises identify user innovations through AI; ② AI-driven innovation toolkits; ③AI as a medium reshaping enterprise-user co-creation relationships; ④AI empowering innovation communities; ⑤AI directly participating in innovation as an actor. The paths through which user innovation acts on AI include: ①Consumer users through participatory design. ②Optimizing AI systems through the "human-in-the-loop" framework; ③Enterprise users driving AI innovation through data assets, scenario applications, and multiple roles. The paper enriches existing research results and promotes further research on the bidirectional impact of artificial intelligence and user innovation. It also identifies gaps in the literature and proposes a research agenda to advance innovation in AI users, contributing to the broader literature on user innovation management and providing a foundation for managing AI effectively in enterprises and user innovation processes. We synthesise these findings into an integrative framework that depicts AI and user innovation as a mutually constitutive system. Building on this synthesis, we propose a future research agenda that addresses five under-explored areas: At the firm level, future inquiry should shift from simply using machine learning to identify user insights toward investigating how retrieval-augmented generation and other generative-AI techniques can actively search for, synthesise and integrate user-generated knowledge and solutions. This includes clarifying the new boundaries and governance mechanisms that distinguish AI-native innovation communities from traditional digital ones, and critically examining data-acquisition M&A strategies—such as large firms buying hospitals to secure private health data—by unpacking their strategic motives and the attendant ethical risks of commodifying sensitive patient information. At the user level, in-depth case studies and experiments are needed to tease out the contextual contingencies of AI-driven innovation toolkits, moving AI from a passive “resource” to a co-evolving network actor. Particular attention should be paid to micro-level mechanisms through which (1) employees undergoing digital transformation tinker with or augment AI systems in situ, and (2) individual contributors in open AI communities iteratively refine algorithms via pull requests, issue tracking and human-in-the-loop feedback loops. At the multi-actor ecosystem level, research must go beyond marketing-oriented value co-creation to explore how AI infrastructures mobilise cross-domain collective intelligence in real time. Empirical studies should examine how heterogeneous actors dynamically orchestrate AI capabilities to sense, seize and scale transient digital opportunities within complex, rapidly changing environments.

摘要： 尽管人工智能与用户创新的研究日益增多，但相关研究较为零散，且理论体系尚未形成。为弥合这一差距，对来自Web of Science数据库的75项研究进行了系统回顾，确定了2个聚合维度，8个关键主题，并构建了一个综合框架，阐明了人工智能与用户创新的双向影响。研究发现人工智能对用户创新作用路径包括：①企业通过人工智能识别用户创新；②人工智能驱动的创新工具箱；③人工智能作为媒介重塑企业-用户共创关系；④人工智能赋能创新社区。⑤人工智能以行动者身份直接参与创新。用户创新对人工智能的作用路径包括：①消费者用户通过参与式设计；②通过“人机回环”框架优化人工智能系统；③企业用户凭借数据资产、场景应用与多元角色驱动人工智能创新。未来研究需关注生成式智能驱动的创新搜索机制、人工智能创新社区治理、用户企业并购驱动的数据获取行为、人机协作中的角色动态，以及多主体生态系统下的人工智能赋能共创。