Getting along with frenemies: enhancing multi-competitor coopetition governance through artificial intelligence and blockchain

Abstract

ABSTRACT Collaborating with one competitor is difficult but collaborating with several competitors is a monumental challenge. However, multi-competitor coopetition, or cooperation between multiple competitors, is increasing. This study examines how recent advancements in artificial intelligence (AI) and blockchain can support multi-competitor coopetition by enhancing governance. Examining two coopetitive R&D consortia in pharmaceuticals and medical imaging, we find that a nascent form of AI called federated learning can address key coopetition concerns such proprietary and confidential data protection, knowledge leakage, data sovereignty and silos thereby maintaining organisational boundaries and autonomy. The use of federated learning and blockchain increases transparency and accountability, which reduces information asymmetries and power differential inequities. Together, these technologies decentralise governance and authority, reducing the tension between collective value creation and individual value appropriation inherent in coopetition, particularly those with multiple competitors. Finally, this study illustrates how emerging technologies challenge traditional assumptions about organisational boundaries, distributed innovation, and coopetition.