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

The growing demand for sustainable development has exposed significant limitations in how Higher Education Institutions (HEIs) evaluate sustainability-oriented research, particularly due to fragmented indicators, descriptive approaches, and the absence of robust data-driven assessment frameworks. This study proposes a comprehensive framework for evaluating 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 through 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 nonlinear 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 diverse higher education contexts, contributing to the advancement of sustainability assessment methods and governance practices in universities.