AI-Driven Optimization for Startup Partnership Matching

This is the authors' abstract. We don't add key points for this paper.

The process of startup partner matching (SPM) is complex and time-consuming, requiring consideration of multiple factors and preferences from both parties. This study aims to address these challenges by leveraging Artificial Intelligence (AI) to enhance the effectiveness and efficiency of SPM. The primary objective is to develop an AI-based system that utilizes Machine Learning (ML) algorithms to analyze profile data and preferences from startups and potential partners, providing recommendations based on suitability and relevance. This research employs the Unified Theory of Acceptance and Use of Technology (UTAUT2) model to investigate factors influencing startups’ adoption of the AI-based SPM system. Data was collected from 600 respondents and analyzed using SmartPLS software with the Partial Least Squares-Structural Equation Modeling (PLS-SEM) method. The analysis revealed that performance expectations, facilitating conditions, and hedonic motivation significantly influence startups’ intentions to adopt AI technology for partner matching. The R-squared value for behavioral intention to use AI technology was 0.65, indicating that the perceived capabilities of AI explain 65% of the variance in behavioral intention. The study concludes that AI technology can significantly enhance the SPM process by improving the accuracy and efficiency of matching and that startups are more likely to adopt such systems if they perceive them as beneficial, well-supported, and enjoyable to use. These findings provide valuable insights for developing effective AI-based solutions to support startup development and successful partnerships.