Machine Learning-Enabled Business Intelligence Dashboards for Strategic Decision Support

Abstract

This study investigated the impact of machine learning-enabled business intelligence dashboards on strategic decision support and organizational performance using a quantitative quasi-experimental design. Data were collected from a sample of 400 professionals, with 38.5% from finance, 24.3% from healthcare, 21.7% from retail, and 15.5% from manufacturing sectors. Among the participants, 57.4% reported using ML-enabled dashboards, while 42.6% relied on traditional BI systems. Descriptive analysis indicated an average professional experience of 6.8 years and a mean dashboard session duration of 18.6 minutes. Regression analysis revealed significant positive relationships between dashboard usage and decision accuracy (β = 0.46, t = 8.72, p < 0.001), decision speed (β = 0.39, t = 7.11, p < 0.001), and perceived effectiveness (β = 0.51, t = 9.34, p < 0.001), with R² values ranging from 0.52 to 0.61. Analysis of variance showed that high-exposure users achieved a mean decision performance score of 4.35 compared to 3.62 for low-exposure users (F = 27.48, η² = 0.29, p < 0.001). Correlation results indicated that dashboard usage frequency was positively associated with decision quality (r = 0.48, p < 0.001) and operational efficiency (r = 0.41, p < 0.001). Structural equation modeling confirmed that perceived usefulness (β = 0.42) and user engagement (β = 0.37) significantly influenced decision quality. Reliability testing showed strong internal consistency with Cronbach’s alpha values between 0.82 and 0.91. These findings demonstrated that ML-enabled dashboards significantly improved decision-making performance and organizational outcomes.