USC Marshall

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.

Prior research on collaboration and creativity often assumes that individuals choose to collaborate to improve the quality of their output. Given the growing role of collaboration and autonomous teams in creative work, the validity of this assumption has important implications for organizations. We argue that in the presence of a collaboration credit premium—when the sum of fractional credit allocated to each collaborator exceeds 100%— individuals may choose to work together even when the project output is of low quality or when its prospects are diminished by collaborating. We test our argument on a sample of economists in academia using the norm of alphabetical ordering of authors’ surnames on academic articles as an instrument for selection into collaboration. This norm means that economists whose family name begins with a letter from the beginning of the alphabet receive systematically more credit for collaborative work than economists whose family name begins with a letter from the end of the alphabet. We show that, in the presence of a credit premium, individuals may choose to collaborate even if this choice decreases output quality. Thus, collaboration can create a misalignment between the incentives of creative workers and the prospects of the project.