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DIGITAL INFRASTRUCTURE GOVERNANCE IN U.S. HIGHER EDUCATION: A PRACTITIONER FRAMEWORK FOR SYSTEM SELECTION

Aug 2026 · International Journal of Science and Research Archive · 0 citations

TL;DR

The article proposes the Digital Infrastructure Governance and Selection (DIGS) framework, which combines seven decision domains with six stage gates spanning problem definition, mandatory assurance, comparative assessment, controlled piloting, contracting and implementation, and lifecycle review and is a transparent decision aid rather than a statistically validated prediction model.

Abstract

U.S. colleges and universities increasingly depend on cloud platforms, identity services, integration layers, data repositories, and administrative applications to conduct admissions, registration, finance, human resources, advising, and student support. Yet technology selection is often organized as a procurement event even though the resulting decision redistributes institutional risk for a decade or more. This survey integrates information-systems research, higher-education studies, and current U.S. standards to identify recurring selection and governance failures and translate them into an auditable practitioner method. The analysis finds that weaknesses commonly arise from fragmented ownership, premature product comparison, opaque lifecycle costs, insufficient interoperability and exit planning, inconsistent vendor assurance, and failure to connect implementation with measurable benefit realization. In response, the article proposes the Digital Infrastructure Governance and Selection (DIGS) framework. DIGS combines seven decision domains with six stage gates spanning problem definition, mandatory assurance, comparative assessment, controlled piloting, contracting and implementation, and lifecycle review. It distinguishes noncompensatory requirements from weighted preferences, records evidence confidence, and treats portability, accessibility, privacy, cybersecurity, and operational resilience as governance obligations rather than optional features. The framework is a transparent decision aid rather than a statistically validated prediction model. Its contribution is a practical bridge between sociotechnical research and institutional artifacts including requirements, scorecards, risk registers, contracts, pilots, and post-implementation reviews.

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