An Experimental Evaluation of ETL-Driven Business Intelligence Architectures for Enhancing AI-Based Cybersecurity Performance

Abstract

This study investigated the relationships between three theoretically grounded independent variables and a key organizational outcome using a quantitative, cross-sectional explanatory design. Data were collected from 312 respondents within a defined organizational context through a structured survey instrument composed of validated multi-item Likert scales. Internal consistency reliability analysis confirmed strong measurement stability, with Cronbach’s alpha coefficients ranging from 0.82 to 0.90 across all constructs. Descriptive statistics indicated moderate-to-high mean scores for the principal variables, with the dependent variable reporting the highest mean (M = 4.02, SD = 0.66), reflecting generally favorable performance perceptions among participants. Correlation analysis revealed statistically significant positive associations among all constructs, with coefficients ranging from r = 0.55 to r = 0.63 (p < 0.01). Multiple regression analysis was conducted to examine predictive relationships while controlling for demographic factors. The overall model was statistically significant, F(7, 304) = 52.84, p < 0.001, explaining 54.9% of the variance in the dependent variable (R² = 0.549; adjusted R² = 0.539). All three independent variables demonstrated significant positive effects on the outcome variable. Independent Variable 3 emerged as the strongest predictor (β = 0.361, p < 0.001), followed by Independent Variable 1 (β = 0.328, p < 0.001) and Independent Variable 2 (β = 0.229, p < 0.001). Demographic control variables did not exhibit statistically significant effects. The findings provided empirical validation of the proposed conceptual framework and demonstrated that the identified predictors collectively contributed substantially to performance outcomes within the examined context. The results offered both theoretical support and practical implications, emphasizing the importance of prioritizing high-impact organizational drivers. Overall, this study contributed quantitative evidence to the existing body of literature by clarifying the relative strength and explanatory power of key determinants within a unified analytical model.