🎓AI-Driven Information System for Knowledge and Technology Transfer: A Design Science Approach for Technology Transfer Offices

Knowledge and technology transfer (KTT) is essential for transforming academic research into real-world innovations. Technology transfer offices (TTOs) help facilitate this process, however, they often struggle with inefficiencies due to various factors. Among these, networking challenges between researchers and TTOs are identified as one of the most critical barriers. This dissertation explores how artificial intelligence (AI) can help improve these relationships. Using Principal-Agent Theory (PAT) as a theoretical framework, the research investigates how AI can augment KTT by addressing TTO-researcher relationship challenges. The study follows a design science approach to develop and evaluate an AI-based system for KTT. This system will be built using pre-trained large language models, adapted with domain-specific data, and tested through both quantitative and qualitative evaluation methods. Ultimately, the research aims to improve KTT processes for the benefit of academia and industry. Additionally, it contributes to theory by extending PAT to incorporate AI-driven dynamics.