Evaluating the Effectiveness of Negative Appeals Used in Emotional Marketing in Relation to Smoking Phenomenon in Egypt
Bibliographic record
Abstract
Negative emotional appeals are used frequently to change behaviours and direct them to serve the purposes of individuals or societies. Certain studies have shown that negative emotional appeals, which include guilt and fear, have the ability to change the behaviour of individuals. On the other hand, some argue for using positive emotion appeals to steer consumer behaviour instead of negative emotional appeals amidst continued debates weighing the effectiveness of warning messages that some government agencies or departments might compel producers to put on product packaging, which usually use fear or threat to positively change and alter consumer behaviour and raise their awareness of consumption risks. This article studies the effectiveness of certain warning messages that the Ministry of Health compels producers to write on cigarette packs; and reviews the effects of negative emotional appeals on a smoker’s behaviour on both the short and the long term. The study concludes that reading these warning messages only managed to affect or change the behaviour of a limited percentage of 14.7% of the total number of smokers who have actually read them. The study also uncovered a negative correlation between smoking and both education level and income level; when levels of education and/or income increase, this brings about a relative decrease in smoking and a stronger desire to quit. It was also found that the male participants showed a particular interest in smoking imported cigarettes on a daily basis while the female participants showed no such interest in smoking a certain type of cigarettes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".