A framed field experiment combined with a latent class econometric approach was used to investigate how prestige-seeking behaviour influences food choices. We propose a theoretical framework to test conspicuous consumption of specialty food products. We test the hypothesis empirically by categorizing individuals into unobserved latent classes according to their general prestige-seeking behaviour. We find evidence of food consumption driven by prestige to the point of becoming a symbol of social status. The prestige-seeking behaviour seems to be motivated by invidious comparison or higher-class individuals seeking to differentiate themselves from lower-class individuals; and pecuniary emulation, or lower-class individuals buying prestigious goods in order to be perceived as members of a higher class. Findings from this study revealed that the effects of differentiating labelling attributes had a higher impact for individuals classified into classes with prestige-seeking behaviour to attain an elevated social status.

The primary objective of this article was to determine key factors affecting the frequency of consumer visits to farmers’ markets. Data were obtained from in-person surveys administered in two farmers’ markets locations in Texas, and used to determine consumer factors, market factors, and socio-demographic characteristics of shoppers influencing frequency of visits. The results of the model showed that travel distance, number of adults in the household, market promotional activities such as entertainment and education, food events, and, to a lesser degree, some demographic variables were all determinants of frequency of visits to farmers’ markets. The negative effect of the distance to the market highlights the importance of farmers’ market location and influences the demographic profile of buyers as most visitors tend to shop in nearby markets. Thus, results also suggest that demographics characteristics are a weak predictor of farmers’ markets interest.

This article investigates the effectiveness of lottery incentive schemes for eliciting consumer valuations in large‐scale online experiments. We implement a fully incentivized condition within a geographically dispersed sample of consumers in which bids for a Criollo steak elicited by a Becker‐DeGroot‐Marschak mechanism are realized with certainty and the products are priority shipped in dry‐ice coolers. The fully incentivized condition is compared to between‐subject random incentivized schemes, in which only a fraction of subjects realize their choices. We tested two treatments with a 10% probability framed as a percentage or an absolute number of subjects, one treatment with a 1% probability, and a purely hypothetical reference condition. The results reveal that between‐subject random incentivized schemes with 10% and 1% payment probabilities are effective in eliciting valuations that are statistically indistinguishable from the fully incentivized scheme. In addition to finding insignificant statistical differences between 10% and 1% and the fully incentivized scheme, all incentivized conditions mitigate hypothetical bias, resulting in lower product valuations than the purely hypothetical condition. We contribute a novel methodological framework for conducting large‐scale experiments with geographically diverse and representative subjects, increasing the external validity and producing reliable valuations while significantly reducing financial and logistic constraints.

The use of neuro-physiological data in models of consumer choice is gaining popularity. This article presents some of the benefits of using psycho-physiological data in analyzing consumer valuation and choice. Eye-tracking, facial expressions, and electroencephalography (EEG) data were used to construct three non-conventional choice models, namely, eye-tracking, emotion and brain model. The predictive performance of the non-conventional models was compared to a baseline model, which was based entirely on conventional data. While the emotion and brain models proved to be as good as conventional data in explaining and predicting consumer choice, the eye-tracking model generated superior predictions. Moreover, we document a significant increase in predictive power when biometric data from different sources were combined into a mixed model. Finally, we utilize a machine learning technique to sparse the data and enhance out-of-sample prediction, thus showcasing the compatibility of biometric data with well-established statistical and econometric methods.