Bibliographic record
Abstract
This research examines the impacts of exposure duration and banner ad complexity on advertising persuasion in a web advertising environment. Processing fluency is used to explain the underlying process that occurs among consumers during exposure to advertisements, and refers to the ease of stimulus encoding and processing that is facilitated by prior exposure to a banner ad. Based on previous studies (e.g. Reber et al. 1998), this research used a priming phase and a testing phase, in which respondents viewed two banner ads for the same brand. A banner ad presented in the priming phase facilitates viewer processing of a target banner ad in the testing phase due to processing fluency. The findings show that, when a banner ad is difficult to process in the priming phase, increasing the duration of exposure to the ad in the priming phase causes a linear increase in respondent attitudes towards the target ad and brand in the testing phase. When the priming banner ad is moderately difficult to process, increasing the exposure duration in the priming phase first increases, and then decreases, respondent attitudes towards the target ad and brand (an inverted-U pattern) in the testing phase. When the priming banner ad is easy to process, increasing the exposure duration in the priming phase first decreases, and then increases, respondent attitudes towards the target ad and brand (a U pattern) in the testing phase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".