Artificial Intelligence and Big Data Adoption in MSMEs: A Systematic Literature Review - Ledyana Ayu Pratiwi; Subandi Subandi; Yuda Padriyadi; Eka Travilta Oktaria (Penerbit Goodwood, 2026)
Purpose: This study systematically reviews the literature on Artificial Intelligence (AI) and Big Data Analytics (BDA) adoption in Micro, Small, and Medium Enterprises (MSMEs), focusing on adoption drivers, organizational and dynamic capabilities, and performance outcomes.Research Methodology: A Systematic Literature Review (SLR) following PRISMA guidelines was conducted. Data were sourced from Scopus and Semantic Scholar for peer-reviewed journal articles published between 2015 and 2025. Based on predefined criteria, 33 articles were selected and analyzed using thematic analysis to identify the dominant themes, theoretical perspectives, and research gaps.Results: The review shows that MSMEs’ AI and BDA adoption is driven by technological readiness, managerial support, and environmental pressures. Crucially, neither AI nor BDA directly enhances firm performance. Their performance impact is indirect and occurs through the development of dynamic capabilities, particularly data-driven decision-making, organizational learning, and adaptability. These capabilities enable MSMEs to convert technology adoption into meaningful performance outcomes. However, prior studies are fragmented, often treating AI and BDA separately and focusing primarily on short-term performance effects.Conclusions: This review confirms that AI and BDA influence MSME performance solely through dynamic capabilities. Thus, integrating technology adoption theories with the dynamic capability perspective is essential to explain their role in sustainable performance and in digital transformation.Limitations: This study was limited to English-language journal articles indexed in selected databases.Contribution: This study offers a holistic framework that integrates technology adoption and dynamic capability perspectives for AI and BDA adoption in MSMEs.