Unlocking data science adoption in MSMEs: A systematic literature review using the SPAR-4-SLR based systematic review and thematic analysis

Abstract

This study presents a comprehensive Systematic Literature Review (SLR) aimed at identifying and synthesizing the key factors influencing the adoption of data science among Micro, Small, and Medium Enterprises (MSMEs). Drawing upon 192 peer-reviewed articles published between 2018 and 2024, the review adopts the SPAR-4-SLR framework a structured, transparent, and replicable protocol for high-quality literature synthesis alongside a structured thematic classification. The analysis identifies seven core thematic clusters: Technology adoption models, digital transformation, business intelligence and big data analytics, Industry 4.0/5.0, emerging technologies, trust in technology adoption, and methodological tools such as PLS-SEM. The review uncovers significant conceptual, methodological, and contextual gaps in current scholarship, offering actionable insights and future research directions. This work enhances understanding of the dynamics shaping data science adoption in MSMEs and underscores the need for context-sensitive, strategic interventions.