Linking business analytics to firm performance: A mixed-method analysis of capabilities, decision quality, and firm size
通过PLS-SEM和fsQCA混合方法，发现商业分析能力通过决策质量影响企业绩效，且企业规模是关键边界条件，小企业从精简的数据到决策系统中获益最大。
• Combination of PLS-SEM and fsQCA reveals multiple pathways to firm performance. • Decision quality mediates business analytics capabilities and performance. • Business analytics success depends on complementary bundles of capabilities. • Firm size is a key boundary shaping business analytics value creation. • Smaller firms gain most from lean data-to-decision systems. 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.