MARKETING PRACTICES AND THE DARK SIDE OF INEQUITY: A QUALITATIVE RESEARCH IN AFRICAN PRIVATE COMPANIES
Bibliographic record
Abstract
Business ethics is capital for both customer satisfaction and growth in a world where competition is a distinctive constituent for companies’ unrelenting existence. The purpose of this research is to examine how ethical and fair practices affect customer satisfaction of companies in very low income countries. The research is centered on a constructivist design model with a qualitative approach that is inspired by the researcher’s experience of the context. Data collection is implemented in two (2) private companies, using a digital recorder and some semi-structured interviews. One company in the field of professional training, and one in the field of IT and printing. The data is transcribed by hand, and then a content analysis is made. The results show that equity has some effects on customer satisfaction, and thus on the overall growth of companies. The recommendation is that companies in the context should offer nondiscriminatory products and reasonable services to upturn customer satisfaction. Keywords: Business ethics, Equity, Customer satisfaction, Marketing, Burkina Faso.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".