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

Title data

Nyarko, Eric ; Bartelmeß, Tina:
Factors influencing urban Ghanaian consumers' preferences for meals/products from multinational food corporations and gender subgroups : a supervised machine learning MaxDiff designs study.
In: Frontiers in Nutrition. Vol. 13 (2026) . - pp. 1-11.
ISSN 2296-861X
DOI: https://doi.org/10.3389/fnut.2026.1729484

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Abstract in another language

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.