Technology Mapping Using WebAI: The Case of 3D Printing

TL;DR

This paper presents a novel webAI-based approach to map 3D printing technology diffusion by analyzing website texts, revealing key actors, geographic patterns, and sectoral differences, thus enhancing traditional measurement methods.

Contribution

The study introduces a new webAI methodology for mapping technology diffusion using online texts, providing insights into regional and sectoral adoption patterns of 3D printing.

Findings

Manufacturers, service providers, retailers, and information providers are key actors.

Regional adoption is driven by lead users and technical universities.

Adoption varies by sector and firm size.

Abstract

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.