Persuasive Advertising in a Vertically Differentiated Competitive Marketplace
Bibliographic record
Abstract
Abstract Despite the widely acknowledged existence in practice, the theoretical literature on persuasive advertising is generally vague about exactly how such advertising could affect consumer preferences, except for the general assumption that persuasive advertising affects consumer willingness to pay or simply “shifts demand.” This paper proposes a theoretical framework for characterizing different ways that persuasive advertising may affect consumer utility in a vertically differentiated marketplace. Firstly, persuasive advertising could simply raise consumers’ reservation price for the product category. Secondly, persuasive advertising could enhance consumers’ perception about the product quality offered by the advertising firm. Thirdly, persuasive advertising could increase consumers’ willingness to pay for quality increment. Preliminary evidences from lab studies are presented to support the existences of the proposed effects. Using a game-theoretic approach, we study two firms’ decision in the adoption of persuasive advertising of a particular effect and the associated price competition. Findings from the theoretical model analyses indicate that factors influencing a firm’s decision in persuasive advertising include consumer heterogeneity, degree of product differentiation, the effectiveness and the cost of such advertising. In a vertically differentiated competitive marketplace, persuasive adverting is a more desirable strategic tool for firms of higher-quality products to further establish a competitive advantage.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".