Informing, Transforming, and Persuading: Disentangling the Multiple Effects of Advertising on Brand Choice Decisions
Why this work is in the frame
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Bibliographic record
Abstract
Prior behavioral research has suggested that advertising can influence a consumer's quality evaluation through informative and transformative effects. The informative effect acts directly to inform a consumer of product attributes and hence shapes her evaluations of brand quality. The transformative effect affects the consumer's evaluation of brand quality by enhancing her assessment of her subsequent consumption experience. In addition, advertising may influence a consumer's utility directly, even without providing any explicit information—this is the persuasive effect. In this paper, we propose a framework that formally models the processes through which all three effects of advertisements impact consumers' brand evaluations and their subsequent brand choice decisions. In particular, we model source credibility, confirmatory bias, and bounded rationality on the part of consumers, by appropriately modifying the standard Bayesian learning approach. Our model conforms closely to prior behavioral literature and the experimental findings therein. In our empirical analysis, we get significant estimates of both informative and transformative effects across brands. We find interesting temporal patterns across the effects; for instance, the importance of transformative effects seem to grow over time, while that of informative effects diminishes. Finally, we conduct policy experiments to examine the impact of increased ad intensity on advertising effects, as well as the role played by consumption ambiguity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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 it