Predicting responsiveness to information: consumer acceptance of biotechnology in animal products

We propose a novel framework using individual choice data and Bayesian updating to predict which consumers are most responsive to information—namely those consumers whose pre-information choices reveal a high level of uncertainty surrounding their preferences. We apply our method to the study of consumer acceptance of genetically modified animal products, which prior research has revealed is a particularly polarising subject. Utilising conditional willingness-to-pay estimates from mixed logit models, we find that individuals with higher preference uncertainty prior to receiving information are most responsive. Implications of our results are discussed in the context of recent breakthroughs in biotechnology.