Do pop-up ads in online videogames influence children’s inspired-to behavior?
Bibliographic record
Abstract
Purpose Advertising through the videogame has become one of the most effective and prevalent channels of advertisement, especially via pop-up ads – appearing on the screen that interrupts children’s gaming activity. Despite its importance, the effectiveness of pop-up ads and its advertising value in online videogames (O-VGs) to predict children’s inspired-to behavior remains scant. This study aims to investigate the underlying factors that explain the relationship between the four dimensions of pop-up ads and perceived advertising value, which further predicts children’s inspired-to behavior. Design/methodology/approach Data from 196 parents who observed their children while playing O-VGs, were analyzed using Smart-PLS. As the respondents are parents, the authors took extra precautions to ensure that the findings are valid. Findings Results showed that perceived irritation and incentives of pop-up ads do not affect children’s advertising value, whereas perceived informativeness and entertainment of pop-up ads positively impact perceived advertising value among children. Besides, children’s perceived advertising value of pop-up ads in O-VGs predict their inspired-to behavior. Originality/value This study contributes to children’s inspired-to behavior via empirically studying the perceived advertising value as a potential deriving source of inspiration. Finally, the study provides information for developers/advertisers about why and under what circumstances children perceived advertising value affect inspired-to behavior.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".