欧洲联盟
营销
一致性(知识库)
排名(信息检索)
新奇的食物
粮食安全
食物选择
人工智能
健康食品
分类器(UML)
机器学习
透视图(图形)
心理学
持续性
可持续发展
随机森林
业务
舆论
可持续农业
公共政策
消费者行为
文化上适当的
食品
粮食政策
作者
Pedro Antonio Martín-Cervantes,Pablo de Frutos Madrazo,Parisa Ziarati
标识
DOI:10.1108/bfj-03-2025-0351
摘要
Purpose This paper investigates how consumers across the European Union perceive and accept insect-based foods, aiming to determine which factors most significantly influence their willingness to adopt this sustainable dietary alternative. Design/methodology/approach A machine learning approach – specifically the Random Forest algorithm – was employed to analyze survey responses collected in six EU countries. The model's performance was assessed through classification metrics and the ranking of variable importance. Findings The classifier reached a strong predictive accuracy of 97.83%. Among the predictors, age stood out as the most impactful, followed by considerations related to price, health benefits and environmental motivations. The analysis also revealed a notable level of cultural consistency in attitudes toward entomophagy across countries. Research limitations/implications As the analysis relies on secondary data and non-random sampling, the ability to generalize the findings may be limited. Practical implications The results offer guidance for both industry stakeholders and public policy, highlighting the consumer segments most receptive to insect-based foods and identifying key themes for communication strategies. Social implications Understanding public attitudes toward edible insects can support the development of sustainable dietary norms across Europe. As food security and environmental concerns grow, this research may help normalize alternative protein sources and reduce cultural resistance. Enhancing awareness of health and ecological benefits could shift consumer mindsets and support broader social acceptance of entomophagy as a viable future food practice. Originality/value This research introduces a data-driven methodological perspective rarely seen in food acceptance studies by integrating machine learning into consumer research. It also provides fresh insights into how demographic and psychological variables interact in shaping European acceptance of insect consumption.
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