A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment
The aquaculture industry has expanded to fill the gap between plateauing wild seafood supply and growing consumer seafood demand. The use of genetic modification (GM) technology has been proposed to address sustainability concerns associated with current aquaculture practices, but GM seafood has proved controversial among both industry stakeholders and producers, especially with forthcoming GM disclosure requirements for food products in the United States. We conduct a choice experiment eliciting willingness-to-pay for salmon fillets with varying characteristics, including GM technology and GM feed. We then develop a predictive model of consumer choice using LASSO (least absolute shrinkage and selection operator)-regularization applied to a mixed logit, incorporating risk perception, ambiguity preference, and other behavioral measures as potential predictors. Our findings show that health and environmental risk perceptions, confidence and concern about potential health and environmental risks, subjective knowledge, and ambiguity aversion in the domain of GM foods are all significant predictors of salmon fillet choice. These results have important implications for marketing of foods utilizing novel food technologies. In particular, people familiar with GM technology are more likely to be open to consuming GM seafood or GM-fed seafood, and effective information interventions for consumers will include details about health and environmental risks associated with GM seafood.
Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment
The aquaculture industry has expanded to fill the gap between plateauing wild seafood supply and growing consumer seafood demand. The use of genetic modification (GM) technology has been proposed to address sustainability concerns associated with current aquaculture practices, but GM seafood has proved controversial among both industry stakeholders and producers, especially with forthcoming GM disclosure requirements for food products in the United States. We conduct a choice experiment eliciting willingness-to-pay for salmon fillets with varying characteristics, including GM technology and GM feed. We then develop a predictive model of consumer choice using LASSO (least absolute shrinkage and selection operator)-regularization applied to a mixed logit, incorporating risk perception, ambiguity preference, and other behavioral measures as potential predictors. Our findings show that health and environmental risk perceptions, confidence and concern about potential health and environmental risks, subjective knowledge, and ambiguity aversion in the domain of GM foods are all significant predictors of salmon fillet choice. These results have important implications for marketing of foods utilizing novel food technologies. In particular, people familiar with GM technology are more likely to be open to consuming GM seafood or GM-fed seafood, and effective information interventions for consumers will include details about health and environmental risks associated with GM seafood.
Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment
Michael J. Weir (author) / Thomas W. Sproul (author)
2019
Article (Journal)
Electronic Resource
Unknown
food labeling , machine learning , seafood , genetic modification , consumer preferences , risk perceptions , subjective knowledge , ambiguity aversion , choice experiment , Environmental effects of industries and plants , TD194-195 , Renewable energy sources , TJ807-830 , Environmental sciences , GE1-350
Metadata by DOAJ is licensed under CC BY-SA 1.0
Individual and Population Learning in Route Choice Behavior of Drivers: Experiment and Model
| British Library Conference Proceedings | 2009
| British Library Online Contents | 2013
| Taylor & Francis Verlag | 2020
Road geometry and drivers' speed choice
| British Library Online Contents | 2007