Assessing the influence of mobile direct social media advertising on consumer attitudes: a study of Kuwaiti consumers
Bibliographic record
Abstract
With the growing number of mobile device users and the rise of engaging social media platforms, the mobile advertising industry is evolving rapidly, presenting marketers with new challenges in reaching customers while maintaining positive attitudes toward advertising.in this paper, we investigated the impact of personalizing advertisements on consumers' attitudes and intentions toward mobile advertising in kuwait.additionally, we explored the influence of other factors, such as informativeness, entertainment, credibility, and irritation, which have been reported to affect consumer attitudes.Our study analyzed a sample of 162 usable questionnaire responses using Partial least squares.the analysis was carried out in two steps, first examining the measurement model followed by the structural model.this research reveals that entertainment, informativeness, and personalization are the three most crucial attributes affecting consumer attitudes toward mobile advertising in kuwait, while credibility and irritation have a less significant impact.interestingly, our results differ from similar studies in different cultures and geographical locations, as consumers in kuwait display a relatively positive attitude toward receiving mobile advertising.Furthermore, consumers who perceive advertisements as personalized exhibit more favorable attitudes and intentions toward mobile advertising, finding the ads less irritating and more informative and entertaining.notably, our study highlights that credibility was not a significant factor influencing consumers' attitudes in kuwait.this could indicate that consumers in an open market, similar to kuwait, may be more receptive to new brands and products, with lower credibility expectations impacting their attitudes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".