Advertising in a Competitive Market: The Role of Product Standards, Customer Learning, and Switching Costs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Standard models of competition predict that firms will sell less when competitors target their customers with advertising. This is particularly true in mature markets with many competitors that sell relatively undifferentiated products. However, the authors present findings from a large-scale randomized field experiment that contrast sharply with this prediction. The field experiment measures the impact of competitors' advertising on sales at a private label apparel retailer. Surprisingly, for a substantial segment of customers, the competitors' advertisements increased sales at this retailer. This robust effect was obtained through experimental manipulation and by measuring actual purchases from large samples of randomly assigned customers. The effect size is also large, with customers ordering more than 4% more items in some categories in the treatment condition (vs. the control). The authors examine how these positive spillovers vary across product categories to illustrate the importance of product standards, customer learning, and switching costs. The findings have the potential to change our understanding of competition in mature markets.
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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 | 0.031 | 0.008 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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