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

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.

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Publication Details

Journal
Frontiers in Nutrition
Published
2026-01-30
DOI
https://doi.org/10.3389/fnut.2026.1729484
Primary Topic
Consumer Attitudes and Food Labeling
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Tina Bartelmeß, Eric Nyarko
Frontiers in Nutrition
Consumer Attitudes and Food Labeling
article

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

Tina Bartelmeß, Eric Nyarko
article en

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.

Frontiers in NutritionVol. 13
University of Ghana (GH), Institute of Mathematical Statistics (US), University of Bayreuth (DE)
Deutsche Forschungsgemeinschaft
Gender equality
Openalex Percentile: Top 73%
Consumer Attitudes and Food Labeling
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