An integrated GenAI-driven method for automating ideation with user-generated content

作者：Xingchen Chen, Hao Liu, Libo Liu, Kristijan Mirkovski, Marta Indulska, Katja Hölttä‐Otto · 发表于：Decision Support Systems · 年份：2025 · DOI：10.1016/j.dss.2025.114554 · 被引用次数：3 · 研究领域：Data Visualization and Analytics、Advanced Text Analysis Techniques、Open Source Software Innovations

Customer-driven innovation relies on leveraging customer insights to develop or improve products that meet evolving customer needs and preferences. Central to this innovation is the ideation process that involves two key stages: identifying customer needs and generating new ideas. While user-generated content offers a rich source of consumer insights, existing approaches for automating the ideation process—including unsupervised learning, supervised learning, deep learning, text summarization and GenAI—face limitations that restrict their scalability and practical utility. Moreover, these approaches often address only isolated stages of the ideation process. Based on a design science methodology and grounded in the user innovation theory, this paper develops and evaluates an integrated GenAI-driven method that automates the ideation process. The method consists of two stages: (1) customer opinion knowledgebase construction and (2) GenAI-based idea generation. The proposed GenAI-driven method offers an adaptable, scalable, and comprehensive solution for advancing customer-driven innovation. • Develop an integrated GenAI-driven method that automates the ideation process.. • Customer opinion knowledgebase construction and GenAI-based idea generation • A more comprehensive solution for advancing customer-driven innovation.