How to grow new applications out of old research? Evidence from firm cumulative investments in deep learning

Abstract

Firm technological research has the potential to spawn multiple applications. Despite recognizing such potential, past literature disagrees on the process through which firms discover and grow new applications out of their past technological research. I examine this question in the context of deep learning, taking a question‐driven approach. Difference‐in‐difference analysis suggests that firms radically increased cumulative investments in past deep learning research upon signals indicating elevated application potential of deep learning. Furthermore, rather than investing in proprietary efforts, firms disclosed their cumulative development trajectories to engage external innovation efforts from which they learn and build. Grounded in these findings, I propose that the discovery and growth of new applications of past research entails unfolding innovation interdependence which motivates firms to co‐evolve with external innovators. Managerial Summary Firm technological research has the potential to spawn multiple applications. This article examines how firms cumulatively invest in their past technological research to grow new applications in the context of deep learning. Employing a difference‐in‐difference approach, analysis suggests that firms radically increased cumulative investments in deep learning after a shock elevating the application potential of their past deep‐learning research. Furthermore, firms publicly disclosed their cumulative development trajectories to attract innovation efforts from application sectors while actively learning from the attracted efforts to innovate further. These findings suggest that firms engaged, leveraged and co‐evolved with external innovation efforts to discover and grow new applications of their past research.