Adopting eco-friendly technologies, such as converting lawns to alternative low-input grass species, can reduce household expenditures and mitigate negative environmental impacts at the same time. However, the rate of adoption of these technologies has not been as high as expected. This study develops a behavioral framework to identify barriers to new technology adoption by incorporating both prospect theory and present bias. We apply the framework in a choice experiment to investigate the relative importance of several factors that shape decisions associated with adoption of low-input turfgrass. We find that loss aversion plays a significant role. Though consumers exhibit present bias, long-term benefits still matter to them. Insights from the behavior model suggest that marketing and government programs that promote cost–benefit-efficient technologies should focus on eliminating or reducing potential losses caused by product failure.

Machine learning (ML) is becoming one of the most anticipated methods in predicting consumer demand. However, it is still uncertain how ML methods perform relative to traditional econometric methods under different dataset scales. This study estimates and compares the out-of-sample predictive accuracy of household budget share for organic fresh produce using two parametric models and six ML methods under regular and large sample sizes. Results show that ML method, particularly Logistic Elastic Net, performs better than econometric models under regular sample size. Contrarily, when dealing with big data, econometric models reach to same accuracy level as ML methods whereas random forest presents a possible overfitting problem. This study illustrates the competence of ML methods in demand prediction, but choosing the optimal method needs to consider product specifics, sample sizes, and observable features.

Farm animal welfare (FAW) issues are becoming increasingly political in many countries, as evidenced by the increased use of regulations, legislation, and ballot initiatives. Available empirical evidence however, indicates that consumer valuation of improved animal welfare is low, although positive. As a result of the sensitive nature of FAW issues, public preferences for improved FAW standards can be susceptible to social desirability bias leading to disparities between regulatory standards and the public’s “true” preferences. Given the potential negative impacts of high mandated FAW standards on food costs and the associated consumer and producer welfare losses, this study examined the issue of effective public preference elicitation in animal welfare ballot initiatives. Specifically, we examined social desirability, the tendency to conform to the social norms, and its role in generating overenthusiasm in the support for FAW issues and policy instruments. We used data from an opt-in survey of respondents and compared results of a List Experiments (LE) to a conventional (direct) survey format. Our results show that public support for the FAW issues examined was consistently overestimated when elicited with the conventional survey format. We discuss the implications of these outcomes for animal welfare policy and offer suggestions to researchers and practitioners eliciting preferences in other sensitive food policy contexts.

This study estimates distributions of consumer willingness to pay (WTP) for organic and animal welfare product attributes using the store scanner data and compares the results to existing experiment-based findings. We find that the WTP premium estimated for organic eggs is consistent with experimental results, while estimated WTP premiums for animal welfare attributes are significantly lower than experimental findings. The results suggest the importance of considering biases when estimating the price premium for animal welfare attributes in experiments. In addition, consumers are not always willing to pay premiums for organic products. Our results also show that WTP premiums are heterogeneous across store brands.