Florenta Teodoridis

General purpose technologies (GPTs) push out the production possibility frontier and are of strategic importance to managers and policymakers. While theoretical models that explain the characteristics, benefits, and approaches to create and capture value from GPTs have advanced significantly, empirical methods to identify GPTs are lagging. The handful of available attempts are typically context specific and rely on hindsight. For managers deciding on technology strategy, it means that the classification, when available, comes too late. We propose a more universal approach of assessing the GPT likelihood of emerging technologies using data from online job postings. We benchmark our approach against prevailing empirical GPT methods that exploit patent data and provide an application on a set of emerging technologies. Our application exercise suggests that a cluster of technologies comprised of machine learning and related data science technologies is relatively likely to be GPT.