A Contrastive Study of Persuasive Appeals in Online Advertising: Investigating Persuasive Appeals in Jordanian and Egyptian Telecommunication Advertisements During Ramadan
Bibliographic record
Abstract
This research analyzes the persuasive appeals employed by telecommunication companies in Jordan and Egypt during Ramadan, aiming to decipher the interplay of cultural dynamics on advertising strategies. The study investigates the frequencies and types of persuasive appeals utilized by these companies, focusing on the most influential appeals, cross-cultural consistencies, and areas of divergence. The research methodology employs a mixed-method approach. The analysis encompasses a diverse range of appeals, with "Appeal for Price," "Rational," and "Social" emerging prominently. Cultural variations surface, highlighting distinctions in explicit information usage, appeal preferences, and humor utilization. The study underscores the strategic significance of appeals like "Rational" and "Appeal for Price" and the impact of explicit information dominance in Jordanian advertisements. Moreover, it sheds light on the shared reliance on rational appeals across both cultures and explores the infrequent use of humorous and card stacking appeals. The findings hold implications for advertising effectiveness during Ramadan, emphasizing cultural sensitivity, strategic appeal deployment, and continuous adaptation. Acknowledging limitations in sample size and temporal specificity, the study recommends a balance of explicit and implicit information, exploration of humor, and collaborative research initiatives for industry growth. This research lays the groundwork for future investigations into culture and evolving dynamics in telecommunication advertising during Ramadan.
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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 | 0.005 |
| 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".