In this research we develop and test a theoretical framework of how alliance success shapes firms’ future collaborative strategies. Specifically, we explore how companies manage the size and quality of their alliance portfolios after achieving a significant positive collaborative outcome. Drawing on sensemaking and attribution theories, we argue that the effects of alliance success on collaborative opportunities and firms’ motivation to pursue them vary based on the firms’ contribution to alliance success. Using advanced machine learning techniques, we analyze 3,328 late-stage collaborative clinical trials conducted between 2005 and 2020 to identify alliance success. Our results reveal a divergence in post-success strategies: lead-firms in successful Phase III clinical trials reduce their collaborative drug development efforts when compared to non-lead firms. Although both lead and non-lead firms tend to partner with higher-status firms following alliance success, our findings reveal no significant difference in the overall quality of their alliance portfolios post-success. These findings contribute to our understanding of alliance portfolio dynamics and provide practical insights for firms navigating collaborative strategies in capital-intensive, R&D-driven industries.
