Employee and Customer Reactions to a Healthy In-Store Marketing Intervention in Supermarkets
Bibliographic record
Abstract
Supermarkets are a primary source of food for American households, and increased presence in low-income, high-minority neighborhoods present opportunities to increase access to healthy foods. It is important to assess store manager and customer reactions to in-store marketing interventions. The objective was to evaluate manager and customer reactions to stealth, low-cost, sustainable in-store marketing strategies to promote healthier purchases in five product categories and gain insight into shopping habits and willingness to change behaviors. Surveys were collected as part of the evaluation of a cluster-randomized controlled trial conducted from 2011-2012 in eight urban supermarkets in low-income, high-minority neighborhoods. Store manager (n=16) and customer intercept surveys (n=100) were administered at intervention stores in May-July 2012 and August 2012, respectively. Demographics, shopping habits, and impact were calculated using frequency distributions, cross-tabulation, and analyses of variance. Correlations were calculated using Pearson’s R or one-sided Fisher’s Exact Test. Most managers reported the project had a positive impact on stocking, ordering, staffing, and interaction with other employees. Most customers did not notice new marketing strategies, although they were intentionally stealth. A large number of customers reported making impulse purchases regularly. Opportunities to positively affect purchasing may exist.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".