Advertising and Brand Attitudes: Evidence from 575 Brands over Five\n Years
Bibliographic record
Abstract
Little is known about how different types of advertising affect brand\nattitudes. We investigate the relationships between three brand attitude\nvariables (perceived quality, perceived value and recent satisfaction) and\nthree types of advertising (national traditional, local traditional and\ndigital). The data represent ten million brand attitude surveys and $264\nbillion spent on ads by 575 regular advertisers over a five-year period,\napproximately 37% of all ad spend measured between 2008 and 2012. Inclusion of\nbrand/quarter fixed effects and industry/week fixed effects brings parameter\nestimates closer to expectations without major reductions in estimation\nprecision. The findings indicate that (i) national traditional ads increase\nperceived quality, perceived value, and recent satisfaction; (ii) local\ntraditional ads increase perceived quality and perceived value; (iii) digital\nads increase perceived value; and (iv) competitor ad effects are generally\nnegative.\n
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".