title: An assessment of the barriers to the consumers' uptake of genetically modified foods: a neural network analysis.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Institute of Agricultural Research and Training (IFAPA), Department of Agricultural Economics and Rural Studies, Avda. Menéndez Pidal, 14080, - Córdoba, Spain.
authors.forename: Macario
authors.initials: M
authors.lastname: Rodríguez-Entrena
authors.affiliation: Universidad Loyola Andalucía, Department of Economics, C/ Escritor Castilla Aguayo n° 4, 14004 -, Córdoba, Spain.
authors.forename: Melania
authors.initials: M
authors.lastname: Salazar-Ordóñez
authors.affiliation: Universidad Loyola Andalucía, Department of Management and Quantitative Methods, C/Energía solar, 41014 -, Sevilla, Spain.
authors.forename: David
authors.initials: D
authors.lastname: Becerra-Alonso
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D003258:Consumer Behavior; D005260:Female; D059022:Food Safety; D030841:Food, Genetically Modified; D007722:Health Knowledge, Attitudes, Practice; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010465:Perception; D018570:Risk Assessment; D012959:Socioeconomic Factors; D013030:Spain
keywords: consumer behaviour; genetically modified food; neural network; ordered logistic regression