Mapping Tech Hotspots Using WebAI: The Case of 3D Printing

The diffusion of new technologies is crucial for the realization of social and economic returns to innovation. Tracking and mapping technology diffusion is, however, typically limited by the extent to which we can observe technology adoption. A new study by Technical University of Munich, University of Mannheim, Universität Salzburg, Justus Liebig University Giessen, ZEW – Leibniz Centre for European Economic Research and ISTARI.AI investigates the diffusion of 3D printing using web mining and deep learning methods. It uses website texts to train a multilingual language model ensemble to map technology diffusion for the case of 3D printing. The study identifies relevant actors and their roles in the diffusion process. This novel approach extends traditional innovation measures such as patent data and company surveys. It provides new insights for technology diffusion.

“Being able to observe innovation activities across the board is not only important for innovation research, but also for companies and economic policy. Current methods for evaluating websites open up new possibilities to measure technology adoption and diffusion in real time. In this project, we demonstrate the potential of applying such web AI-based indicators to 3D printing technology. We can thus identify different types of actors and identify geographic hotspots. These indicators also feed into other research projects on the innovation performance of companies and regions”, explains Prof. Hanna Hottenrott, Professor for Economics of Innovation at the TUM School of Management and co-author of the study.

Together with Julian Schwierzy, researcher at TUM School of Management, Prof. Hottenrott summarizes the results, which show that besides manufacturers, service provider, retailers, and information providers play an important role. The geographic distribution of adoption intensity suggests that regional 3D-printing intensity is driven by experienced lead users and the presence of technical universities. The overall adoption intensity varies by sector and firm size. These patterns indicate that the approach of using webAI provides a useful and novel tool for technology mapping which adds to existing measures based on patents or survey data.


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