ARTIFICIAL INTELLIGENCE ADOPTION AND FIRM PERFORMANCE IN SMES A SYSTEMATIC LITERATURE REVIEW AND FUTURE RESEARCH AGENDA
The relationship between artificial intelligence (AI) adoption and firm performance in small and medium-sized enterprises(SMEs) has attracted growing scholarly attention, yet the literature remains theoretically fragmented and dominated by techno-optimistic assumptions that portray AI as a direct pathway to superior performance. This study addresses two central research questions: why do AI adoption outcomes remain heterogeneous across SMEs, and through what organizational mechanisms does AI create business value? A systematic search of the Scopus database, followed by PRISMA-guided screening, yielded 29peer-reviewed articles analyzed through VOS viewer bibliometric mapping and qualitative content analysis. Four thematic clusters were identified: AI Adoption and SME Sustainable Performance; AI Investment and Firm-Level Heterogeneity; Green Intellectual Capital and Innovation; and AI Application and Service Productivity in China. Drawing on these clusters, an integrative Antecedents-Mediators-Moderators-Outcomes (AMMO) framework is proposed. Findings demonstrate that AI adoption outcomes are contingent upon complementary organizational capabilities, knowledge management infrastructure, human capital quality, and institutional context rather than technology deployment alone. Five value-creating mechanism categories are identified: innovation and transformation, human capital and knowledge, operational and productivity, strategic and behavioral, and financial and investment mechanisms. The study contributes by challenging techno-deterministic assumptions, advancing the AMMO framework as a conceptual architecture for future inquiry, and positioning green intellectual capital as an emerging theoretical frontier. Implications for managers, policymakers, and future researchers are discussed.