Effects of vividness, information and aesthetic design on the appeal of pay-per-click ads
Bibliographic record
Abstract
Purpose Few studies illustrate how contextual effects (e.g. assimilation and contrast) in pay-per-click ad design may impact consumers' attitudes and purchase intention. To fill this research gap, the authors provide theoretical predictions and empirical evidence on how ad design may prompt an assimilation and/or a contrast effect that may influence consumers' attitudes toward the ad and the brand and purchase intention. They also investigate whether the impact of contextual effects on consumers' decisions depends on the level of vividness in the ad. Design/methodology/approach A 2 (vividness: dynamic motion vs. static page) × 2 (information design: assimilation vs. contrast) × 2 (aesthetic design: assimilation vs. contrast) between-subjects experimental design is used to examine the effects of vividness, information design and aesthetic design. Conditional process analysis is used to assess the mediating role of attitudes toward the ad and the brand in the relationship between contextual effects and purchase intention. Findings For dynamic ads (i.e. high vividness) but not for static ads (i.e. low vividness), combined information contrast and aesthetic contrast designs generate a more favorable attitude toward the brand and a higher purchase intention than do combined information assimilation and aesthetic assimilation designs. Notably, combined information contrast and aesthetic contrast designs have the strongest effects than any other combination of assimilation and contrast designs of information and aesthetics. Attitudes toward the ad and the brand are significant mediators between contextual factors and intention to purchase. Research limitations/implications The study examines the effectiveness of online ads from a new theoretical angle based on the attributes of pay-per-click ads. Practical implications The results suggest that when advertisers decide to use dynamic ads, they should adopt a contrast design for both the ad information and its aesthetics. Originality/value This study fills a research gap in the contextual effects literature, including providing evidence of an underlying process in the relationship between certain contextual effects and purchase intent. It also extends previous findings of assimilation/contrast in information design to aesthetics design and advances the literature on vividness by examining a moderation effect of vividness.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".