Digital Marketing in Emerging Economies: A Comparative Study of Consumer Engagement Strategies in Nigeria and South Africa
Bibliographic record
Abstract
This article used a comparative systematic review to examine the key consumer engagement (CE) strategies within the distinct DM landscapes of Nigeria and South Africa (SA). It also investigated the cultural, economic, and technological influences affecting the strategies in both countries. The study involved systematic research of Google Scholar, ResearchGate, Scopus, other internet-based data and publications, and secondary data from grey literature. Boolean string search protocols were used to address the research questions raised in the study. Inclusion and exclusion criteria were applied to ensure rigour and comprehensiveness and to reduce publication bias. Data from twenty-five (25) primary studies (13 for Nigeria and 12 for South Africa) indicated that social media is a primary engagement tool; mobile and email marketing are also used, although not as prominent as social media. Similarities and differences in both country’s engagement strategies were examined. Additionally, the study identifies the cultural differences, economic gap, and technological divide in both countries as major factors that influence engagement. The findings provide valuable implications for marketers that aim to optimise their strategies in emerging markets. These highlight the need to implement culturally adaptive strategies, leverage mobile penetration opportunities, consider economic sensitivities, bridge the digital divide, foster technological acceptance, and adopt ethical and sustainable marketing practices.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".