The Role of Micro-Influencers in Niche Digital Marketing Strategies in Africa: Impact on Consumer Engagement and Brand Loyalty
Bibliographic record
Abstract
Globally, the growth of digital marketing has transformed consumer engagement strategies, with micro-influencers emerging as an essential force in niche markets. Influencers’ growing usage and effectiveness in digital marketing have captivated academic researchers and industry professionals. In Africa, a continent characterised by increasing digital penetration and fragmented consumer preferences, the role of micro-influencers in shaping engagement and loyalty necessitates a critical exploration. To elucidate the underpinnings and evolving patterns of this contemporary phenomenon, this paper employs a systematic literature review methodology to map out and synthesise key findings from existing studies where influencer marketing, consumer engagement, and brand loyalty are intertwined. By analysing 15 articles published between 2011 and 2024 in journals indexed by Google Scholar, Scopus, ResearchGate, and Web of Science, this study deconstructs the existing literature on influencer marketing by exploring various journals, articles, theories, methodologies, and themes. The study examines how micro-influencers contribute to the success of niche digital marketing strategies, specifically focusing on their impact on consumer engagement and brand loyalty. The findings underscore the potential of micro-influencers as strategic assets in digital marketing, who possess unique capacity to promote trust and interactions crucial in driving engagement within niche markets. However, African brands face challenges such as fragmentation and regional differences, discrimination, and lack of accessibility to data and analytics. Thus, the study recommends that African brands consider micro-influencers that fit their brand identity and market features while adopting data analytics tools. Additionally, brands should strive to build continuous relationships with micro-influencers to promote long-term brand loyalty while ensuring equitable and inclusive marketing approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".