Determination of key resource variables for a model of technological capability management in the alternative electricity generation industry of Colombia and Mexico, through a decision tree.

The study focuses on identifying key variables to develop a model for managing technological capabilities in the alternative energy generation industry in Colombia and Mexico. The objective is to identify critical factors that influence strategic decisions in this sector. Researchers MsC Neider Duan Barbosa Castro, PhD PD Fabiola Sáenz Blanco, Dr. Francisco Zorrilla Briones, and MsC Evy Fernanda Tapias Forero contribute their expertise in technological capability management and university-industry collaboration. Advanced data mining techniques were used, integrating simulations and algorithms such as decision trees, to extract pertinent information and determine the most influential variables. Detailed Likert scale surveys were conducted in various power generation organizations in Colombia and Mexico, providing a comprehensive data set for analysis. The paper discusses the need for effective management of technological capabilities. It emphasizes strategic collaboration between internal and external stakeholders. The importance of adopting innovative strategies and using a multi-level analytical approach to managing technology in the power generation industry is highlighted. This iterative and cyclical approach enables dynamic adaptability to technological and market changes. It continuously identifies areas for improvement and new opportunities. The study's conclusions highlight the critical importance of innovation and market adaptation, as well as university-industry collaboration in the management of technological capabilities. The need for strategic and dynamic management to maintain a sustainable competitive advantage in the energy sector is evident. The integration of emerging technologies and operational flexibility are emphasized. In addition, the utility of data mining techniques in identifying and prioritizing key variables is highlighted. This provides a solid foundation for informed decision making in managing technological capabilities.