Publication Details
Title:
The Artificial Intelligence Nexus in LMIC Marketing and Entrepreneurship: A Systematic Synthesis and Contextual Performance Framework
Details:
Introduction: Artificial intelligence (AI) is fundamentally reshaping global commerce; however, its application within the unique institutional landscapes of low- and middle-income countries (LMICs) remains fragmented. This study addresses this gap by providing the first comprehensive synthesis of the marketing–entrepreneurship nexus in LMIC AI research.
Methods: Following PRISMA 2020 guidelines, we conducted a robust systematic review of 120 peer-reviewed studies (2016–2026) sourced from five databases using an automated Python 3.12 script and eleven manual repositories. Study quality was rigorously evaluated using the Mixed Methods Appraisal Tool (MMAT) and CASP checklists to ensure evidence reliability.
Results: Findings reveal a technological landscape dominated by General AI/Automation (47.5%) and Natural Language Processing (20.0%), with Sub-Saharan Africa (29.2%) and South Asia (19.2%) emerging as primary research hubs. Our analysis identifies a critical "rigor gap," as 44.2% of studies rely on unspecified empirical designs, with a near-absence (7.5%) of longitudinal or experimental evidence.
Discussion/Originality:The study’s primary novelty lies in the development of the Contextual AI–Business Performance Framework. By integrating Resource-Based View (RBV), Technology Acceptance Model (TAM), and Institutional Theory, we move beyond universalistic adoption models to position digital infrastructure, regulation, and informality as essential boundary conditions.
Conclusion: This review contributes a novel theoretical synthesis that bridges the gap between academic rigor and practical implementation. It provides a strategic roadmap for policymakers and operational decision-makers to leverage AI for inclusive growth, while establishing a future research agenda prioritized toward causal identification and geographic diversification in under-researched LMIC regions.