Título An assessment of the barriers to the consumers' uptake of genetically modified foods: a neural network analysis
Publicación externa No
Medio J. Sci. Food Agric.
Alcance Article
Naturaleza Científica
Cuartil JCR 1
Cuartil SJR 1
Impacto JCR 2.46300
Impacto SJR 0.90600
Ámbito Internacional
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959567776&doi=10.1002%2fjsfa.7247&partnerID=40&md5=b8756cd7aea422ed5d88d6d35a413f1b
Fecha de publicacion 30/03/2016
ISI 000372316700017
Scopus Id 2-s2.0-84959567776
DOI 10.1002/jsfa.7247
Abstract BACKGROUND: This paper studies which of the attitudinal, cognitive and socio-economic factors determine the willingness to purchase genetically modified (GM) food, enabling the forecasting of consumers' behaviour in Andalusia, southern Spain. This classification has been made by a standard multilayer perceptron neural network trained with extreme learning machine. Later, an ordered logistic regression was applied to determine whether the neural network can outperform this traditional econometric approach. RESULTS: The results show that the highest relative contributions lie in the variables related to perceived risks of GM food, while the perceived benefits have a lower influence. In addition, an innovative attitude towards food presents a strong link, as does the perception of food safety. The variables with the least relative contribution are subjective knowledge about GM food and the consumers' age. The neural network approach outperforms the correct classification percentage from the ordered logistic regression. CONCLUSION: The perceived risks must be considered as a critical factor. A strategy to improve the GM food acceptance is to develop a transparent and balanced information framework that makes the potential risk understandable by society, and make them aware of the risk assessments for GM food in the EU. For its success, it is essential to improve the trust in EU institutions and scientific regulatory authorities. (C) 2015 Society of Chemical Industry
Palabras clave genetically modified food; consumer behaviour; neural network; ordered logistic regression
Miembros de la Universidad Loyola

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