The Impact of Interactive Dashboard Visualization on Data-Driven Decision Making in Financial Services: A Qualitative Evaluation

Abstract

This study examines the problem that financial-service organizations increasingly invest in interactive dashboards and business intelligence systems, yet the availability of sophisticated visualization technology does not necessarily ensure accurate, timely, evidence-based, and analytically informed decision making. The purpose of the research is to quantitatively assess how specific interactive dashboard visualization capabilities influence Data-Driven Decision-Making Performance in financial-service organizations and to determine their individual and combined predictive contributions. Although the paper title refers to a qualitative evaluation, the implemented methodology is explicitly quantitative, cross-sectional, and case-study-based, focusing on selected enterprise financial-service cases, including banks, insurance companies, investment firms, lending institutions, and fintech organizations using interactive dashboards for operational, risk-related, analytical, and managerial decisions. A purposive sample of financial managers, business and data analysts, risk and credit professionals, operations and compliance personnel, and BI, IT, and analytics specialists was surveyed using a five-point Likert-scale instrument. Of 320 questionnaires distributed, 298 were returned and 284 usable responses were retained, producing an 88.8% usable response rate. The key independent variables were Dashboard Interactivity, Visual Information Quality, Dashboard Usability and Accessibility, and Real-Time Analytical Capability, while Data-Driven Decision-Making Performance served as the dependent variable. Data analysis incorporated descriptive statistics, Cronbach's alpha, Pearson correlation, multiple regression, ANOVA, standardized beta coefficients, R², adjusted R², VIF, tolerance, and Durbin-Watson diagnostics. The findings showed that Data-Driven Decision-Making Performance achieved M = 4.21, SD = 0.52, while Visual Information Quality Recorded M = 4.18, SD = 0.54. All predictors were positively and significantly associated with decision performance, with Visual Information Quality showing the strongest correlation, r = .72, p < .001, followed by Real-Time Analytical Capability, r = .70, Dashboard Interactivity, r = .68, and Dashboard Usability and Accessibility, r = .61. The regression model produced R = .834, R² = .696, adjusted R² = .692, F (4, 279) = 159.69, p < .001, explaining 69.6% of the variance. Visual Information Quality was the strongest predictor, β = .30, followed by Real-Time Analytical Capability, β = .28, Dashboard Interactivity, β = .24, and Dashboard Usability and Accessibility, β = .17. All five hypotheses were supported. These results imply that financial enterprises should prioritize high-quality visual information, timely analytics, meaningful interactivity, and accessible dashboard design to strengthen evidence-based financial decision performance.