Linking business analytics to firm performance: A mixed-method analysis of capabilities, decision quality, and firm size

Despite growing recognition that business analytics (BA) capabilities enhance organizational performance, empirical evidence on the specific mechanisms and boundary conditions through which analytics create value remains fragmented and heavily skewed toward large firms in data-rich Northern economies. Grounded in the Resource-Based View and Dynamic Capabilities theory, this study investigates how technical, human and contextual BA resources affect decision quality and, in turn, firm performance. Using partial least squares structural-equation modelling (PLS-SEM) complemented by fuzzy-set Qualitative Comparative Analysis (fsQCA), the results show that the success of business analytics depends not on isolated investments but on complementary bundles of capabilities that meet quality thresholds and fit the organizational context. Decision quality emerges as the central mechanism translating BA capabilities into performance, while firm size acts as a key boundary condition. Smaller firms benefit most from lean, integrated data-to-decision systems, whereas larger firms achieve comparable outcomes through scale and accumulated business knowledge.