Leveraging Machine Learning Models to Enhance Startup Collaboration and Drive Technopreneurship

Abstrak

In the dynamic and competitive realm of startups, identifying and cultivating effective collaborations is crucial for sustained success. This research evaluates how machine learning (ML) technologies can enhance startup collaborations by advancing decision-making processes through the analysis of historical data. Employing the SmartPLS methodology, this study collected data from 220 stakeholders, including 207 actively engaged in startups that are either utilizing or integrating ML technologies. The investigation focuses on understanding ML models, the importance of historical data, and the dimensions of collaboration critical to the success of startups. Through analysis with PLS-SEM, it was found that ML models significantly boost inter-startup synergy and the effectiveness of collaborative efforts. The results provide vital insights for industry practitioners and strategic decision-makers, offering practical strategies to employ ML in optimizing collaboration and ensuring sustainable growth within the technopreneurship arena. This study not only highlights the benefits of ML in fostering cooperative ventures but also aims to refine the strategic frameworks essential to the startup ecosystem.