Open Access

Neslihan Zülal Ankara

• In the contemporary business landscape characterized by global economic uncertainty and rapid technological advancements, companies face increasing complexity in meeting their innovation targets. To address this challenge, established firms are engaging in collaborative partnerships with external entities, particularly startups, to leverage their knowledge and capabilities to gain a competitive advantage. Despite the growing popularity of corporate-startup collaboration (CSC), there remains a noticeable gap in the literature regarding the analysis of the dynamics and key success factors that are relevant to enhancing the collaborative process between these asymmetric entities. This study aims to fill this research space by investigating the dynamics, motives, objectives, and challenges of CSC, with a specific focus on identifying the success factors crucial for achieving favorable outcomes. Drawing upon insights from eight expert managers using a mixed-method approach, the study highlights the significance of addressing internal barriers and prioritizing key success factors to ensure successful outcomes in CSC. The research methodology involves a literature review, a pre-stage questionnaire, and semi-structured interviews to gather comprehensive data. The researchers have synthesized the findings to enhance an existing Co-Canvas model in a way that it is specifically tailored for CSC, thereby offering managers a practical framework to effectively navigate the complexities of CSC. The findings of this research hold implications for scholars, corporate leaders, and entrepreneurs, enabling them to evaluate past collaboration initiatives, identify potential sources of errors, and devise strategies to enhance their collaboration performance. By shedding light on the dynamics and success factors of CSC, this study contributes to the existing body of knowledge and offers valuable insights for strategically enhancing collaboration performance. However, the study refrains from making broad generalizations of the model due to the small sample size of experts.