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

Deskripsi

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.