Economics and Management Empowered by Data Science: An Empirical Investigation of Data-Driven Decision-Making, Organizational Performance, and Sustainable Competitive Advantage

The recent advancement of Industry 4.0, digital transformation, artificial intelligence (AI), big data, and cloud computing has fundamentally reshaped contemporary economic systems and managerial practices. Consequently, organizations increasingly rely on data science capabilities to improve decision-making, enhance organizational agility, stimulate innovation, and achieve sustainable competitive advantage. This study empirically examines the influence of Data Science Capability (DSC) on Managerial Decision Quality (MDQ), Organizational Agility (OA), Innovation Capability (IC), and Organizational Performance (OP) by integrating the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), and the Technology Acceptance Model (TAM) into a unified conceptual framework. A quantitative cross-sectional survey design grounded in the positivist research philosophy was adopted. Data were collected from 520 managers, Chief Executive Officers (CEOs), economists, business analysts, data scientists, and Information Technology (IT) managers drawn from manufacturing firms, financial institutions, healthcare organizations, telecommunications companies, public sector institutions, and digital service enterprises. Stratified random sampling was employed to ensure adequate representation of the study population. The collected data were analyzed using IBM SPSS Statistics 29 and SmartPLS 4, employing descriptive statistics, Pearson correlation, reliability and validity assessment, Partial Least Squares Structural Equation Modelling (PLS-SEM), bootstrapping, Multi-Group Analysis (MGA), and Importance–Performance Map Analysis (IPMA). The empirical findings reveal that data science capability exerts a significant positive influence on managerial decision quality, organizational agility, innovation capability, and organizational performance, with all hypothesized relationships supported (p < 0.001). The structural model demonstrated substantial explanatory power, particularly for organizational performance (R² = 0.68), confirming the strategic value of data science in organizational success. The study extends RBV by conceptualizing data science capability as a strategic organizational resource, validates Dynamic Capabilities Theory in digitally transformed organizations, and develops a novel empirical model explaining the mechanisms through which data science generates competitive advantage. The findings offer important theoretical contributions and practical implications for organizational leaders, policymakers, and researchers seeking to leverage data science for enhanced economic productivity, managerial effectiveness, innovation, and sustainable organizational performance in the digital economy.