Rodríguez-Entrena Macario, Salazar-Ordóñez Melania, Becerra-Alonso David
Institute of Agricultural Research and Training (IFAPA), Department of Agricultural Economics and Rural Studies, Avda. Menéndez Pidal, 14080, - Córdoba, Spain.
Universidad Loyola Andalucía, Department of Economics, C/ Escritor Castilla Aguayo n° 4, 14004 -, Córdoba, Spain.
J Sci Food Agric. 2016 Mar 30;96(5):1548-55. doi: 10.1002/jsfa.7247. Epub 2015 Jun 9.
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.
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.
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.
本文研究了态度、认知和社会经济因素中的哪些因素决定了购买转基因食品的意愿，从而能够预测西班牙南部安达卢西亚消费者的行为。这种分类是通过使用极限学习机训练的标准多层感知器神经网络进行的。随后，应用有序逻辑回归来确定神经网络是否能优于这种传统的计量经济学方法。
结果表明，相对贡献最大的是与转基因食品感知风险相关的变量，而感知利益的影响较小。此外，对食品的创新态度以及对食品安全的认知也存在紧密联系。相对贡献最小的变量是关于转基因食品的主观知识和消费者年龄。神经网络方法的正确分类百分比优于有序逻辑回归。
必须将感知风险视为关键因素。提高转基因食品接受度的一项策略是建立一个透明且平衡的信息框架，使社会能够理解潜在风险，并让他们了解欧盟对转基因食品的风险评估。为了取得成功，提高对欧盟机构和科学监管当局的信任至关重要。
