A Data-Driven Framework for Assessing Sustainability-Oriented Research Models in Higher Education Institutions

Sustainability has become a strategic priority for Higher Education Institutions (HEIs), particularly in the context of the Sustainable Development Goals, where university research plays a key role in addressing environmental, social, economic, and institutional challenges. However, the evaluation of sustainability-oriented research models remains limited by fragmented indicators, descriptive approaches, and the absence of robust, data-driven assessment frameworks. This study proposes a comprehensive framework for assessing the sustainability orientation of university research models, integrating validated measurement instruments with advanced analytical and predictive techniques to support evidence-based decision-making in higher education governance. The framework is based on a multidimensional instrument comprising 26 indicators across environmental, social, economic, and institutional dimensions, developed through expert judgment using the Delphi method and statistically validated by Confirmatory Factor Analysis (CFA). The instrument was applied to 260 researchers from four public HEIs located in the Colombia–Ecuador border region, and perceived performance was contrasted with actual institutional indicators, revealing significant non-linear discrepancies. To address this complexity, an artificial neural network model was developed to estimate real sustainability performance based on survey data, achieving a predictive accuracy of 90.92%. Beyond institutional diagnosis, the proposed framework functions as a decision-support tool that enables HEIs to identify critical gaps, prioritize interventions, and guide continuous improvement strategies in research management. Due to its methodological rigor, scalability, and transferability, the framework can be adapted to different higher education contexts, contributing to the advancement of sustainability assessment methods and governance practices in universities.