Investigating business outcomes of healthy food retail strategies: A systematic scoping review
Bibliographic record
Abstract
Large changes to food retail settings are required to improve population diet. However, limited research has comprehensively considered the business implications of healthy food retail strategies for food retailers. We performed a systematic scoping review to identify types of business outcomes that have been reported in healthy food retail strategy evaluations. Peer-reviewed and grey literature were searched. We identified qualitative or quantitative real-world food or beverage retail strategies designed to improve the healthiness of the consumer nutrition environment (eg, changes to the "marketing mix" of product, price, promotion, and/or placement). Eligible studies reported store- or chain-level outcomes for measures of commercial viability, retailer perspectives, customer perspectives, and/or community outcomes. 11 682 titles and abstracts were screened with 107 studies included for review from 15 countries. Overall item sales, revenue, store patronage, and customer level of satisfaction with strategy were the most frequently examined outcomes. There was a large heterogeneity in outcome measures reported and in favourability for retailers of outcomes across studies. We recommend more consistent reporting of business outcomes and increased development and use of validated and reliable measurement tools. This may help generate more robust research evidence to aid retailers and policymakers to select feasible and sustainable healthy food retail strategies to benefit population health within and across countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".