Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation

Lee, Myunghwan, Victor Cui, Gene Moo Lee. “Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation”, AOM 2023

Given the importance of exploratory innovation in fostering firms’ sustainable competitive advantages, firms often depend on technological assets or inter-firm relationships to pursue exploration. Regarded as a general-purpose technology, deep learning (DL)-based artificial intelligence (AI) can be an exploratory innovation-seeking instrument for firms in searching unexplored resources and thereby broadening their boundary. Drawing on the theories of organizational learning and path dependence, we hypothesize the impact of a firm’s DL capabilities on exploratory innovation and how DL capabilities interact with conventional pathbreaking activities such as technical assets and inter-firm relationships. Our empirical investigations, based on a novel DL capabilities measure constructed from comprehensive datasets on AI conferences and patents, show that DL capabilities have positive impacts on exploratory innovation. The results also show that extant technological assets (i.e., structured data management capabilities) and inter-firm relationships remedy the constraints on a firm’s innovation-seeking behaviors and that these path-breaking activities negatively moderate the positive impact of DL capabilities on exploratory innovation. To our knowledge, this is the first large-scale empirical study to investigate how DL affects exploratory innovation, contributing to the emerging literature on AI and innovation.