We investigate the diffusion of 3D printing using web mining and deep learning methods. This novel approach extends traditional innovation measures such as patent data and company surveys. It provides new insights for technology diffusion. Joint project with the University of Mannheim, Universität Salzburg, Justus Liebig University Giessen, ZEW - Leibniz Centre for European Economic Research and @ISTARI.AI.
Full report: https://arxiv.org/abs/2201.01125
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. This study 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. The results 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.