Date of Award

3-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Information Systems (PhDIS)

First Advisor

Dr. Omar El-Gayar

Second Advisor

Dr. Abeer Alkhwaldi

Third Advisor

Dr. Daniel Talley

Abstract

Federal agencies invest over $115 billion annually in information technology, yet persistent questions remain about whether these substantial investments translate into improved operational efficiency and mission outcomes. Despite transparency requirements established by the Federal IT Acquisition Reform Act (FITARA) of 2014 and the creation of the federal IT Dashboard, agency leaders lack systematic frameworks for evaluating optimal IT investment levels and allocation patterns across different technology asset types.

This dissertation examines the relationship between information technology investment clusters and operational efficiency across 24 federal government agencies subject to the CFO Act. Using comprehensive data from the federal IT Dashboard spanning fiscal years 2021-2024, this research employs Data Envelopment Analysis (DEA) to assess the relative efficiency of agencies in converting IT investments into mission-critical outcomes. The study addresses the primary research question: How effectively do federal agencies convert information technology investments into mission outcomes and operational efficiency?

The research design utilizes a two-stage DEA approach with second-stage ANOVA implemented through output-oriented BCC (Banker-Charnes-Cooper) models with variable returns to scale. The DEA model incorporates three IT spending categories as inputs: infrastructure IT (data centers, networks, cloud services), administrative IT (financial management, human resources, procurement systems), and mission IT (agency-specific service delivery systems), with agency performance metrics including primary mission transactions, customer interactions, and customer satisfaction scores serving as outputs. Environmental variables, including budget quartiles, agency age categories, and fiscal years, provide groupings for contextual analysis. The second-stage analysis employs ANOVA to test whether efficiency differs significantly across these environmental categories, with particular attention to scale-dependent patterns through quartile-based sub-group analysis.

Analysis of four years of data (fiscal years 2021 through 2024) reveals significant efficiency variations across agencies and budget size categories. Findings indicate that efficient agencies allocate higher proportions of spending to infrastructure IT (48.6% versus 34.7% for inefficient agencies) and maintain lower total IT budgets on average. The analysis identifies differential impacts of IT asset clusters based on agency size, with infrastructure IT investments demonstrating positive efficiency impacts for smaller agencies while mission-specific IT shows stronger returns for larger agencies.

This research makes several important contributions. Theoretically, it extends healthcare IT efficiency methodologies to the federal government domain, providing the first systematic assessment of IT investment efficiency using mandated federal reporting data. Practically, the study offers agency leaders and policymakers a quantitative framework for evidence-based resource allocation decisions, identifies benchmark agencies operating at the relative efficiency frontier within this sample, and provides indicative improvement targets for underperforming agencies based on relative benchmarking comparisons. These contributions support more effective implementation of FITARA provisions and ultimately enable improved government service delivery within existing budget constraints.

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