Consumer-acceptance of artificial intelligence-driven delivery systems in sub-Saharan Africa: A systematic review of contextual factors with implications for South Africa

Abstract

Background: Artificial intelligence (AI)-driven delivery systems may improve access to goods and services in sub-Saharan Africa, but acceptance depends on infrastructure, affordability, trust, digital capability and institutional conditions.
Objectives: This review examines the contextual factors shaping consumer acceptance of AI-driven delivery systems in sub-Saharan Africa and their implications for information management in South Africa.
Method: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020, six databases were searched and 42 studies were included. Thematic synthesis was combined with quantitative content analysis of construct frequency, directional valence, co-occurrence, geographic concentration and inter-rater reliability. Heterogeneous statistical measures precluded formal meta-analysis.
Results: Perceived usefulness or performance expectancy and relative advantage were the most consistent positive factors. Inadequate infrastructure, cost and absent trust were recurrent inhibitors. The Herfindahl-Hirschman Index was 0.200, indicating moderate geographic concentration. Median model-level R-squared values were 0.49 for Technology Acceptance Model-only studies and 0.73–0.78 for studies using three or more frameworks; this descriptive association does not establish causation.
Conclusion: Consumer acceptance in South Africa cannot be explained by individual perceptions alone. Infrastructure instability, digital inequality, affordability, data governance and aviation regulation influence whether favourable intentions translate into use.
Contribution: The review offers a multi-framework synthesis of consumer, technological and institutional factors, identifies evidence gaps and proposes evidence-informed actions for South African information management practice.