主题(计算)
数据科学
管理科学
区域科学
计算机科学
工程类
知识管理
地理
万维网
作者
Baijun Liu,Longhui Pan,Jiao Li,Hongmei Yuan
标识
DOI:10.1080/10429247.2025.2542670
摘要
In today's rapidly changing technological environment, companies must accurately understand the development process of technology from conceptualization to market application. This understanding is crucial for companies in research and development (R&D) strategic planning and decision-making processes for new product launches, as it helps predict future innovation trends and changes, and adjust strategic directions accordingly. Technology Roadmap (TRM) serves as an effective tool for exploring innovation paths, providing an organized mapping method that outlines the evolution of technology and reveals the interrelationships between technology, products, and markets. By visualizing the complex process of technological innovation, TRM assists companies in identifying key technological milestones and market opportunities. However, traditional TRM methods often rely on expert opinions to construct relationships between markets, products, and technologies, which is not only time-consuming and costly but also introduces subjective bias. In light of this, many companies and government agencies are increasingly focused on maintaining objectivity and reducing costs during the development of TRM. To address these challenges, this study proposes a framework combining Structural Topic Modeling (STM) and Subject-Action-Object (SAO) semantic analysis. STM deepens decision-makers' understanding of market demands and technological trends by identifying and integrating key topics and concepts, thereby enhancing the precision of product development decisions. SAO analysis is used to reveal semantic relationships between components, achieving a comprehensive semantic understanding of the relationships between technology, products, and markets. By constructing technology roadmaps with the combination of STM and SAO analysis, the bias associated with intuition-based expert opinions can be significantly reduced, providing companies with deeper and more comprehensive market insights. This approach not only improves the quality and efficiency of decision-making but also makes the new product development process more scientific and systematic, laying a solid foundation for successful product launches. Although this study discusses genetically engineered vaccines as an example, the proposed framework has broad applicability and can be applied across industries. This provides a powerful tool for companies in various fields to navigate the ever-changing landscape of technological innovation, ensuring that their R&D activities remain aligned with market demands and technological advancements.
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