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Record W4392057615 · doi:10.1177/00222437241237483

Using Price Promotions to Drive Children's Healthy Choices in a Developing Economy

2024· article· en· W4392057615 on OpenAlexaff
Szu‐chi Huang, Michal Maimaran, Daniella Kupor

Bibliographic record

VenueJournal of Marketing Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsEconomicsMarketingMicroeconomicsEconomyAdvertisingBusiness

Abstract

fetched live from OpenAlex

This research examines how price discounts—a classic marketing incentive—drive children's healthy choices in the understudied context of a developing economy. The authors partnered with UNICEF to launch three field experiments in Panamá among 2,418 children to examine four pillars of price discount promotions for children: what to discount (product selection), how to discount (message design), whom to target (children's age), and whether to discount again (repetition). They uncovered four previously undocumented insights. First, price discounts alone effectively increase demand among children 6–11 years of age, reconciling conflicting findings in prior literature. Second, product selection based on relative price—a particularly crucial factor in developing regions—drives opposing postpromotion effects: ironically, marketers should not discount expensive healthy options but rather moderately priced ones. Third, different from prior literature's practice of directly communicating final prices, discount messages that require older children to derive final prices are more effective. Fourth, repetition can amplify or undermine discounts’ efficacy depending on message complexity and children's age. This research offers concrete guidelines for researchers and practitioners, uncovering both positive and negative effects of price promotions on children, and shedding light on price promotion interventions that most powerfully nudge children of different ages to act.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.083
GPT teacher head0.437
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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