Why Do Superstores Fail in Africa? Market and Social Orientation Perspectives
Bibliographic record
Abstract
Abstract Most superstores in Africa either fail or struggle to survive. One of the reasons for low performance is the replication of the Western model and the lack of social orientation. Superstores are designed to depend on high volume and low prices; the opposite is true in Africa, leading to superstore failure. Using the resource-based view and social exchange theories, we argue that superstores in Africa need to be equally market and social-oriented for superior performance. In this context, employing the literature review method, we identify four market orientation factors – consumer behaviour, distribution, location and merchandizing – that can lead to a superior superstore performance and four macro factors – inflation, interest rate, foreign exchange and security – that can moderate the relationship between the market orientation and superstore performance. We also argue for superstores to serve the low-income segment and engage in community development by supporting local schools, farmers and infrastructure development projects. Further, our findings indicate that customers in African superstores spend only 28 minutes per visit compared to 42 minutes in the West, suggesting unattractiveness of superstores. We offer a few managerial implications for superstores to be attractive to both customers and the community. We call the market- and social-oriented-based model – Bottom of the Pyramid Superstores (BOPS) – profitable, sustainable and relatable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".