Measures of Online Advertising Effectiveness for Market Penetration: The Case of Orange Juice Consumers
Bibliographic record
Abstract
Traditionally, research on the impacts of generic advertising on food demand has focused on television advertising and in‐store promotions. However, with the an increased use of advertising using social media, such as online banner advertisements, there is a need for investigation to determine whether online advertising has a similar impact as television advertising. We investigate the factors related to both the awareness and impact of online and television advertisements on the frequency of orange juice consumption. A negative time trend is found with regard to awareness of both online and television advertising. The relationship between awareness of orange juice advertisements and frequency of consumption is somewhat surprising. The major surprise is the lack of relationship between online advertising and consumption frequency. Only awareness of online Facebook advertisements is positively related to the number of days per week orange juice is consumed. The lack of relationship with awareness of television advertisements, which at first may be surprising is reflected in a recent decision by the Florida Department of Citrus, which is responsible for the funding and placement of orange juice advertisements, to no longer invest in television advertising. Additional research on whether or not online advertisements are proving ineffective is needed.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".