Evidence map›Paper›PMID 41693947›Full record

ArticleFrontiers in nutrition2026

Factors influencing urban Ghanaian consumers' preferences for meals/products from multinational food corporations and gender subgroups: a supervised machine learning MaxDiff designs study.

Eric Nyarko, Tina Bartelmeß

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Eric NyarkoDepartment of Statistics and Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Accra, Ghana.
Tina BartelmeßFaculty of Life Sciences, Food, Nutrition and Health, University of Bayreuth, Kulmbach, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The decision-making process of consumers when choosing meals or products from multinational food corporations is influenced by various factors related to food, personal preferences, and the environment. This study combines five machine learning (ML) models and quantitative MaxDiff designs to predict the factors that influence urban Ghanaian consumers' preferences for meals/products from these corporations, alongside gender-specific differences in consumer preferences. We utilized data collected in March/April 2023 from a random sample of 200 consumers in the Greater Accra region. All ML models demonstrated similar levels of goodness of fit, but there were slight differences in predictive performance. The Ridge regression model distinguished itself with superior predictive capabilities, although it required a longer fitting time. For all respondents, food quality/packaging emerged as the most critical factor in choosing products or meals, followed by healthiness, taste/flavor, and nutritional value. The subgroup results indicated notable gender-specific differences in consumer food preferences. While female respondents placed greater emphasis on attributes such as aroma/smell, followed by affordability, convenience, accessibility, and familiarity with a meal, male respondents prioritized factors like being high in fiber/roughage, followed by aroma/smell, affordability, and convenience. This finding is crucial as it suggests that dietary interventions could benefit from being tailored to specific gender groups. By integrating multiple ML models and MaxDiff designs, we identified additional significant predictors compared to traditional statistical methods, offering policymakers a deeper understanding of the factors influencing urban Ghanaian consumers' food preferences for products from multinational food corporations. This understanding supports the development of healthier food policies in Ghana's evolving food landscape.

Indexed as

consumersGhanamachine learning modelsMaxDiff designsmultinational food corporations

Identifiers

PMID41693947
PMCPMC12900749

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.