# Socio-Demographic Correlates of Basic Food Needs: A Maslow’s Hierarchy Analysis

## TL;DR

This study explores how basic food needs influence purchasing behaviors and identifies vulnerable consumer groups based on socio-demographic factors.

## Contribution

The study introduces multinomial logistic regression models to predict food purchasing behaviors linked to fundamental needs.

## Key findings

- Gender, age, and education significantly influence food purchases driven by food security.
- Marital status is a significant factor only for survival-related purchases.
- The developed models offer actionable insights for marketing strategies and product optimization.

## Abstract

Nutrition is a fundamental aspect of consumer behavior, closely linked to the satisfaction of basic household needs and strategies for purchasing food products. This study aimed to examine how fundamental food needs—specifically survival (daily food) and food security (food stocks)—shape purchasing behaviors, enabling the identification of vulnerable consumer segments and the delineation of patterns useful for producers and retailers. Data were collected through a cross-sectional survey (N = 1060) and analyzed using the Rao & Scott-adjusted Pearson chi-square test (R, version 4.4.3), considering key socio-demographic factors including gender, age, educational level, marital status, residence, and income. Results indicate that gender, age, and education significantly influence food purchases driven by the need for food security, whereas marital status is a significant factor only for survival-related purchases. Differences observed in other contexts were not statistically significant. Additionally, two multinomial logistic regression models were developed to predict consumer food purchases driven by fundamental needs, demonstrating high explanatory power. Each socio-demographic factor emerged as a significant predictor for at least one response category on the Likert scale, and the relative influence of each predictor was quantified. These models provide actionable insights for marketing strategies, including the identification of optimal store locations and the adjustment, diversification, or optimization of product ranges based on the characteristics of specific consumer segments and geographic areas.