Craving towards a Personalized Advertisement: Identifying Preferences and Attitudes of Saudi Consumers toward Its Effectiveness
Bibliographic record
Abstract
The preferences of the study related to Saudi customers regarding personalized advertisements have not been investigated, since the rapid penetration of social media marketing among consumers. So the present study aims to determine the attitudes of consumers related to the personalized advertisement. An online questionnaire was used for collecting data from 512 Saudi consumers, who were active on social media. The questionnaire items were developed based on the previous literature and the collected data was analyzed through structural equation modelling and path analysis. The results showed that credibility (0.244, p < 0.001) and lack of irritation (0.536, p < 0.001) significantly impact the preferences of the consumer regarding personalized advertisements. An increase in credibility and lack of irritation is likely to improve the preference of consumers. Moreover, informativeness (0.571, p < 0.001) and entertainment (0.493, p < 0.001) positively influence the preferences of the consumer regarding personalized advertisements. The study holds significant importance as the first study in Saudi Arabia investigating the attitudes of consumers regarding personalized advertisements on social media in penetration, and it deals with the elements closely related to personalizing advertisements. This will help expand the theory about the attitudes of consumers regarding personalized advertisements that replace the traditional way of advertising. Practically, this study provides guidelines about following personalized advertisements on social media sites for marketers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".