合作请愿
竞争对手分析
业务
透明度(行为)
块链
公司治理
产业组织
竞赛(生物学)
价值(数学)
知识管理
营销
计算机科学
经济
市场经济
计算机安全
生物
机器学习
激励
生态学
财务
标识
DOI:10.1080/13662716.2023.2168519
摘要
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.
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