Pop-up Ads and Behaviour Patterns: A Quantitative Analysis Involving Perception of Saudi Users
Bibliographic record
Abstract
The study aimed to investigate consumer behaviour towards pop-up ads. The study is quantitative in nature and carries out a survey questionnaire. The study sample consisted of 100 active users of social media (i.e., Snapchat, Instagram, Twitter, Tik Tok and gaming application). The data collected were analysed using Statistical Package of Social Sciences (SPSS) version 23.0. Moreover, the study used descriptive statistical analyses, a t-test was used to check the different impact of independent variables and finally ANOVA test was used to find the impact of more than one independent variable on the dependent one. The results of the study showed that Snapchat (30.47%) was the widely used application among the participants and an average user consumes social media more than 4 hours a day which makes it 40% of the participants. The study also found that participants disagreed that they always look for pop-up ads (M = 1.71, Std = 0.92). Also, the study found no significant difference in perceptions of respondents towards pop-up ads with regard to gender. The ANOVA test revealed that educational level (0.627) didn’t show any significant difference towards the opinions of participants about pop-up ads whereas, age level (0.50) and monthly income (0.001) showed significant difference towards the opinions of participants about pop-up ads. The study concluded that pop-up ads do not actually impact consumer behaviour positively and are not the affective means of attracting consumers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".