The effects of multiple‐ads and multiple‐brands on consumer attitude and purchase behavior
Bibliographic record
Abstract
Purpose The purpose of this research is to show how the dual mediation model has been used to explain consumer responses toward an ad and a brand. This study attempts to incorporate ad affect and competition into the framework and examine the effects of advertising on consumers' attitudes and purchase intentions in multiple‐ad and multiple‐brand environments. Design/methodology/approach A total of 165 usable data (54 percent female, mean age=36.2) were collected from an experiment conducted in North America. Findings The findings revealed that the higher level of affective responses to a focal ad significantly leads to a higher evaluation of that ad. Our findings also indicated that information about a competing ad and brand is processed comparatively and that evaluations of the competing ad and brand negatively influence evaluations of a focal ad and brand. Originality/value Important theoretical contributions of this study are that ad affect is an important determinant in the formation of ad attitude and it can be incorporated into the dual mediation model to explain the effects of advertising on consumer behavior. Our research also challenges the dual mediation model by incorporating competition into the model. Managerial implications of these results were discussed.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".