The Love of Money, pressure to Perform and Unethical Marketing Behavior in the Cosmetic Industry in Uganda
Bibliographic record
Abstract
The purpose of the study was to examine the relationship between love of money, pressure to perform and unethical marketing behavior in the cosmetic industry in Uganda. The methodology was cross-sectional and correlational. A questionnaire was administered to collect data on a sample of 169 marketers selected randomly from five cosmetic companies in Uganda. Results indicate that if the salespersons are willing to perform unprofessional assignments for monetary gain or if they have a burning desire for success regardless of how they should succeed, this is bound to result into unethical marketing behavior. Furthermore, the present study reveals that as pressure to perform increases through the achievement of targets and deadlines, unethical behavior increases and moves in the same direction as a result of the effect. Unrealistic targets combined with fixed deadlines promote and strengthen unethical marketing behavior. Thus love of money through its components, Success, Motivator, Evil, Budget and Equity can be moderated by management control - as management control improves, unethical marketing behavior is minimized. Even if the cosmetics industry in Uganda is very much in its infancy with only five manufacturers and this may limit generalizability, this study argues that companies should employ staff with good working experience in the marketing profession and there should be continuous staff screening of their behaviors over the years. Company image should be a top priority and management should design targets that are realistic to avoid continuous reported unethical behaviors among their staff.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".