From analytics capability to performance: Data-driven decision making in U.S. FMCG small and medium enterprises

Abstract

Small and medium enterprises (SMEs) in the United States fast-moving consumer goods (FMCG) sector face volatile demand and tight resources, yet little is known about how predictive analytics capability translates into organizational performance. Drawing on dynamic capabilities theory and organizational information processing theory, this study examines the effects of four capability dimensions (data quality and accessibility, analytics infrastructure, analytical skills and expertise, and predictive modelling capability) on financial forecasting accuracy, risk management effectiveness and operational efficiency. It also tests data-driven decision-making as a mediator and organizational analytics culture as a moderator. A face-to-face survey of 400 managers and decision makers in FMCG SMEs was analysed using partial least squares structural equation modelling, complemented by fuzzy set qualitative comparative analysis (fsQCA). All four capability dimensions had significant positive effects on the three outcomes (β from 0.108 to 0.239). Data driven decision making mediated the link between capability and performance (indirect β = 0.067, p = 0.003), while the direct effect was not significant. Analytics culture had a direct positive effect on performance (β = 0.201) but did not moderate the relationship between capability and performance. The fsQCA results showed that no single capability was necessary and that several combinations of capability, decision processes and culture were associated with high performance. The study offers evidence from an under-researched sector and proposes a conceptual framework to guide analytics adoption in FMCG SMEs.