Whether or not to use a quick response code in the ad
Bibliographic record
Abstract
QR codes have a multitude of benefits for both the scanning consumers and advertisers. We empirically examine print ads in Fortune magazine to explore the factors behind a company's decision on whether or not to use QR codes in its print ads. In our model, we focus on the role of a company's past decisions as well as its competitors' past decisions. We adopt a binary logit model with multiple explanatory variables to control for advertiser type, past behaviour, and past competitive behaviour. We find that companies are likely to be influenced by their own past behaviour in their decision to use QR codes in their print ads. We also find that companies are more likely to start adopting QR codes when their competitors have done so in the past. To the best of our knowledge, this is the first attempt to examine QR codes in a descriptive, objective, multivariate, scientific study. Although the incidence of QR codes is currently low, we expect increased overall usage of QR codes in the future because of strong inertia and mimicry effects we find in our empirical investigation.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".