Healthier food choices for children through menu pricing
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the use of pricing (incentive and deterrent) to shift the purchase decision intent of parents when they order food for their child in a fast food restaurant. Design/methodology/approach A financial incentive and a deterrent pricing tactic was tested using an online quantitative approach with a sample of 400 Canadian parents, representative of the Canadian population based on geography, household income and education level. Findings The financial incentive tactic demonstrated that a strong and clearly articulated monetary discount can shift the stated purchase intent of parents into an increased number choosing a healthier side dish for a child’s fast food meal. A deterrent pricing approach was shown to also shift stated purchase intent, and had a higher consumer impact on a per dollar basis. Younger parents (<35 years old) were more likely to select healthier side dishes for their child; however, parents of all ages could potentially be influenced through motivational pricing approaches. Research limitations/implications This was an exploratory study using online surveys and stated purchase intent among Canadian respondents. Examining “stated” purchase intent only through the use of a questionnaire, and without a consequence of the choice, may not reflect a consumer’s real purchase behaviour. A future study should be conducted on pricing approaches in a restaurant setting, where the parents then have the consequences of interacting with the child and the response of the child to the food decision made on their behalf. Practical implications The use of pricing to shift parental food purchase decisions into ordering healthier food items for their children is a promising option, which with further exploration may lead to easily implementable restaurant-level recommendations that achieve the desired results of children eating healthier. Social implications As the frequency of fast food consumption continues to rise, encouraging healthier fast food choices for children could help to combat the troubling rise of obesity in young children. Originality/value While most historical research has focussed on teen or adult consumers, this paper offers insights to academics, marketers and restaurant industry influencers into the previously unexplored area of using pricing to encourage parents to make healthier food choices for children in a fast food restaurant environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".