SYSTEMATIC REVIEW OF DATA SCIENCE APPLICATIONS IN PROJECT COORDINATION AND ORGANIZATIONAL TRANSFORMATION

Abstract

This study investigates the quantitative relationships between data-science capability, coordination efficiency, and organizational transformation, emphasizing how analytics-driven decision systems enhance operational and strategic performance. Using a cross-sectoral dataset of 210 organizational observations, the analysis employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the predictive influence of data-science capability and the mediating roles of data literacy and leadership alignment. The results reveal that data-science capability significantly predicts both coordination efficiency (β = 0.68, p < .001) and organizational transformation (β = 0.55, p < .001), explaining 46% and 59% of the respective variances. When mediators are included, the full model explains 67% of total variance, demonstrating that transformation outcomes depend on both technological and human enablers. Strong model-fit indices (CFI = 0.97, RMSEA = 0.037, SRMR = 0.033) confirm the reliability of the analytical framework. The study also establishes that data literacy (β_indirect = 0.23) and leadership alignment (β_indirect = 0.18) significantly strengthen the indirect pathways between analytics capability and transformation outcomes, indicating that human and managerial dimensions are integral to successful digital change. The findings highlight that coordination efficiency functions as a mechanism translating analytics maturity into process agility and transformation readiness. Overall, the study concludes that sustainable organizational transformation arises from the integration of advanced data-science capability, analytical culture, and leadership alignment. These results contribute to the growing empirical evidence that data-driven ecosystems serve as the foundation for strategic adaptability, operational precision, and continuous innovation in contemporary organizations.