Willingness to purchase Genetically Modified food: an analysis applying artificial Neural Networks

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dc.creatorSalazar-Ordóñez, M.
dc.creatorRodríguez-Entrena, M.
dc.creatorBecerra-Alonso, D.
dc.date2017-04-01T17:10:16Z
dc.date.accessioned2026-07-09T08:20:48Z
dc.descriptionFindings about consumer decision-making process regarding GM food purchase remain mixed and are inconclusive. This paper offers a model which classifies willingness to purchase GM food, using data from 399 surveys in Southern Spain. Willingness to purchase has been measured using three dichotomous questions and classification, based on attitudinal, cognitive and socio-demographic factors, has been made by an artificial neural network model. The results show 74% accuracy to forecast the willingness to purchase. The highest relative contributions lie in the variables related to beliefs, especially those link to perceived risks; while the variables with the least relative contribution are age and knowledge on GMO.
dc.identifierdoi:10.22004/ag.econ.182940
dc.identifierhttps://ageconsearch.umn.edu/record/182940/files/14th_EAAE_Congress_-_Poster_Salazar-Ord__ez.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/182940
dc.identifier.urihttp://hdl.handle.net/123456789/598093
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/182940
dc.titleWillingness to purchase Genetically Modified food: an analysis applying artificial Neural Networks
dc.typeText

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University of Minnesota