AI-analytics capabilities and knowledge assimilation: driving innovation in SMEs

Purpose This study aims to investigate how AI-assisted big data analytics capability influences product innovation performance through digital knowledge assimilation in manufacturing SMEs in Viet Nam – an emerging market. Design/methodology/approach This study used a mixed-methods research design incorporating both qualitative and quantitative methodologies. A time-lagged, multi-stage approach was used to test a research model examining direct and indirect relationships between constructs. Empirical data were gathered from 292 Vietnamese manufacturing SMEs to validate the hypothesized relationships within the proposed framework. Findings AI-assisted big data analytics capability directly influences both incremental and radical product innovation performance, with a stronger effect on incremental innovation. Digital knowledge assimilation serves as a significant mediating mechanism, with stronger effects on radical innovation than incremental innovation. Practical implications The findings provide actionable insights for SME managers to strategically invest in AI-assisted analytics capabilities while developing robust knowledge assimilation processes to maximize innovation returns. Policymakers should create targeted programs enhancing SMEs’ access to AI technologies and building analytics capabilities to address digital divides, while educational institutions should integrate AI training with knowledge management competencies in manufacturing curricula. Originality/value The findings extend dynamic capabilities view by elucidating how AI-enhanced analytical capabilities function as distinct dynamic capabilities that drive diverse innovation outcomes through knowledge assimilation processes.