Search

AbstractThis study uses a choice experiment to characterize market segments (consumer preferences heterogeneity) based on three attributes of seafood (mussels) that are affected by ocean acidification: shell appearance, meat color, and nutritional composition. Using a sample of 1,257 individuals from two main cities in Chile, we estimate both the Mixed Logit model and the Latent Class model. We use the individual-specific posterior (ISP) parameters’ distribution to categorize consumers’ heterogeneity based on the signs and intensity (i.e., like or dislike dimension) of these ISPs. We compare the pattern of preferences and whether people are classified within the same preference pattern in both models. In general, we observed that the models identify a different number of segments with various patterns of preferences. Moreover, the models classify the same people into different groups. Since the segmentation is sensitive to the chosen model, we discuss strengths, inconsistencies, biases, and best practices regarding methodological approaches to establishing market segments in choice experiments and future ocean acidification conditions.

We conducted a discrete choice experiment to explore whether providing information about a lesser-known issue related to climate change, as it is ocean acidification (OA), affects consumers’ preferences for seafood products in a middle-income country in the southern hemisphere. Our objective was to determine whether OA information affects consumer preferences for seafood using stated preference (SP) techniques. Seafood attributes include shell size and appearance, meat color, texture, taste, nutritional composition, product assortments, and price. We applied a split-sample approach to test for information effects, with one sample receiving information about OA while the other did not. We analyze the differences between samples using visual instruments and statistical tests. The results demonstrate that although the statistical test does not identify a difference between models, we did find that OA information increases the precision of ’consumers’ responses. Moreover, using visual instruments, we found differences in specific parameters – not detected in the statistical analysis - which can lead to substantial differences in the willingness to pay for seafood attributes. The article is relevant as understanding these matters is essential when generating more effective communicational strategies regarding the impacts of climate change.