Mapping technology diffusion with AI: A web-based approach for tracking additive manufacturing adoption

Understanding the diffusion of emerging technologies is essential for capturing the benefits of innovation. Yet, traditional science, technology, and innovation (ST&I) indicators are often limited in measuring technology adoption. This study investigates the potential of analyzing corporate websites through web mining and machine learning to measure the adoption of additive manufacturing (AM) technologies. Furthermore, it examines how regional ST&I indicators — specifically patents and publications — shape AM adoption patterns. Despite still being niche, AM adoption in Germany doubled from 0.37% (2022) to 0.74% (2023) of firms. Regional web-based adoption hot spots largely align with patent and publication activity. In addition, our regression analyses reveal a positive and statistically significant relationship between these indicators and AM diffusion based on our AI-based web indicator. These results underline the potential of WebAI methods to complement traditional ST&I indicators. • Measuring additive manufacturing adoption via large-scale web scraping and NLP. • Web indicators offer a broader view of adoption than patents and publications. • AM adoption in Germany doubled from 2022 to 2023, reaching 0.74% of firms. • Regional adoption hotspots largely align with patent and publication activity.