Design of an Economic Decision Intelligence Platform for Resource Optimization in Socially Impactful U.S. Sectors

Abstract

Organizations operating across socially impactful U.S. sectors increasingly depend on data analytics, artificial intelligence, and decision-support technologies, yet fragmented information systems, limited forecasting capability, inefficient resource allocation, delayed decision processes, and competing service demands continue to constrain effective resource optimization. This study aimed to design and quantitatively evaluate an Economic Decision Intelligence Platform capable of integrating organizational data, predictive analytics, AI-supported optimization, and real-time decision support to improve Resource Optimization Performance. A quantitative, cross-sectional, case-study-based research design was employed using organizational cases drawn from healthcare, education, public utilities, energy, infrastructure, social services, and other public-interest environments in the United States. The final analytical sample comprised 306 valid responses from 340 distributed questionnaires, representing a 90.0% usable response rate. The principal variables were Integrated Economic Data Intelligence, Predictive Economic Analytics, AI-Driven Resource Allocation and Optimization, Real-Time Decision Support Intelligence, and Resource Optimization Performance. Analysis included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation analysis, multiple regression, ANOVA, standardized beta coefficients, tolerance, VIF, and Durbin-Watson diagnostics. The reported quantitative findings showed that Resource Optimization Performance achieved the highest overall mean, M = 4.14, SD = 0.55, while AI-Driven Resource Allocation and Optimization recorded the highest predictor mean, M = 4.11, SD = 0.57. All predictors were significantly associated with Resource Optimization Performance, including Integrated Economic Data Intelligence, r = .66, Predictive Economic Analytics, r = .63, AI-Driven Resource Allocation and Optimization, r = .74, and Real-Time Decision Support Intelligence, r = .70, all p < .001. The combined model explained 70.9% of the variance in Resource Optimization Performance, R = .842, R² = .709, adjusted R² = .705, F (4, 301) = 183.34, p < .001. AI-Driven Resource Allocation and Optimization emerged as the strongest independent predictor, β = .31, followed by Real-Time Decision Support Intelligence, β = .27, Predictive Economic Analytics, β = .24, and Integrated Economic Data Intelligence, β = .22. All five hypotheses were supported. The findings imply that socially impactful organizations can improve resource utilization, demand matching, cost control, and allocation efficiency by integrating reliable data, predictive intelligence, AI-supported optimization, and timely managerial decision support within a unified platform.