Effect of Metal Can Labels on Consumer Attention through Eye Tracking Methodology
Bibliographic record
Abstract
In today’s market there are a growing number of packaged goods on the shelves that consumers have to sift through in order to make purchasing decision. To stand out from the competition, companies often times change a product’s packaging to revolutionize the product or add important information to the package. Changing the package design can be risky for repeated customers because they become conditioned to the old package design. A private canning company worked with our researchers to conduct an eye tracking study in CUshop™ at PackExpo (tradeshow) 2014 in Chicago, Il to examine the effect of newly added labels on canned creole. Through a collaborative study at this trade show, quantitative and qualitative data was collected on three different canned creole packaging. A total of 272 participants took place in this study to evaluate if adding “can facts” to the package label and litho printing the ends of the cans had an effect on consumer attention compared to the control can. Three eye tracking metrics were tested and statistical analysis yielded significant results for the can facts and litho ends compared to the control for the Total Fixation Duration (TFD) metric. Participants viewed the can fact cans and litho end cans significantly longer than the control. Survey findings found that participants preferred the litho ends 75% compared to the control and the can facts 53% compared to the control.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".