Sustainable Digital Transformation in Public Administration: A Framework for University Enrollment
Abstract
The increasing pressure on public institutions to adopt digital solutions has raised new questions about the actual sustainability of such transformations, particularly in administrative systems that remain resource-intensive despite partial digitalization. While digitalization is widely promoted as a path to greater efficiency and environmental responsibility, its sustainability outcomes are far from guaranteed. This study critically examines these assumptions through the case of a university enrollment system, identifying inefficiencies and exploring how digitalization can optimize resource use and improve performance. The widely assumed link between digitalization and sustainability is not universally valid, as outcomes depend on context, implementation, and infrastructure. The economic, environmental, and social impacts of digital transformation are assessed using process mapping, a conceptual sustainability framework, and estimates of paper and processing efficiency. Digital workflows are shown to reduce administrative workload, lower paper waste, and enhance accessibility, while underscoring the need to avoid unnecessary information technology practices, redundant communication flows, and poorly structured information management that may offset sustainability gains. For example, annual carbon dioxide emissions were reduced from 85,924.68 kilograms to 85,006.73 kilograms when transitioning from a hybrid to a fully digital enrollment process, primarily by eliminating paper-related emissions and reducing electricity use. The study provides a policy-oriented sustainability indicator framework to guide decision-makers in evaluating public sector digitalization efforts, while also offering a structured approach to assess trade-offs and implementation conditions. The research contributes methodologically by combining process modelling with sustainability indicators—a rarely integrated approach in public administration—and lays a foundation for future computational optimization models focused on institutional governance and sustainability-oriented digital transformation.