A PATH THROUGH KNOWLEDGE MANAGEMENT AND ARTIFICIAL INTELLIGENCE MODELING: a focus on Sustainability, Science Park's dynamics, Backcasting on emerging technologies and the implementation of TalTech Industrial Project

The doctoral thesis examines how knowledge management strategically drives innovation, sustainability and competitiveness in the information and data analysis economy. The first chapter examines the fundamental principles and structures of knowledge management (KM) and how they fit into corporate decision-making, highlighting the shift from a static to a dynamic vision of knowledge as a fluid resource necessary for sustainable solutions. The following section examines the challenges and opportunities of Science Parks as an ideal ecosystem for promoting the development and dissemination of knowledge, as well as the complex interaction between business intelligence, organizational learning, innovation and financial performance. This analysis has allowed the positive and non-positive critical relationships to emerge, on which it becomes strategic to focus, and to simulate such an ecosystem realistically, the application of an agent-based model is proposed. Moving on to an innovative proposal for managing the R&D function, in the third chapter, a literature analysis procedure is presented through backcasting, replacing the traditional forecasting approach, within a blockchain system to classify and select emerging technologies, frontier and dominant ones to support the corporate decision-making process on the choice of investments to be made. In the last chapter, the thesis discusses the relationship between KM, innovation, research, new technologies and organizational and economic performance, thanks to the development of the international research project "TalTech Industrial" and the analysis of Global Value Chains in emerging economies. This study and the reinterpretation of indicators such as the Catch-Up Performance Index show that knowledge-based policies have essential public and private economic implications to effectively reduce existing gaps and increase current company and sectoral performance. In conclusion, the transformative capacity of KM as a bridge between theoretical and practical fields is crucial to support economic growth and competitiveness in the contemporary, constantly evolving socio-economic context. The findings contribute to the academic discourse and serve as a guiding framework for organizations aiming to leverage KM for strategic advantage in the ever-evolving business environment.