Leveraging big data-driven dynamic capabilities for enhanced decision making: a moderated-mediation approach

Purpose Grounded in the dynamic capabilities view and organisational learning theory, this study examines the relative association of big data analytics-enabled dynamic capabilities (BDAEDC) on the quality of the decisions made in organisations through the mediating roles of exploitative and explorative learning and through the moderating role of top-management support for learning (TMSL).Design/Methodology/Approach Using cross-sectional survey data collected from 330 software firms in Pakistan, the study tests a moderated mediation model via partial least squares structural equation modelling (PLS-SEM) in WarpPLS 8.0.Findings It has been found that BDAEDC is a significant source of improved exploitative and exploratory learning, which are responsible for improved decision-making quality. Explorative learning ends up being a more influential mechanism. Bootstrapped mediation analysis confirms both learning orientations as key pathways. Top-management support of learning has a negative moderating effect on both the associations.Originality/Value By combining theory on dynamic capabilities with theory on organizational learning, digital capabilities for analytics integrate technological and organizational perspectives and create a new understanding of the mechanisms and boundary conditions of the effect of analytics-enabled capabilities on decision-making quality.