Online from publisher website. 5 pages., Describes a new Food Trust Consortium , run by IBM, using blockchain technologies to improve food traceability.
16 pages, via online journal, Buying local food has become an increasingly popular way for consumers to engage with those who grow their food; however, research has shown a specific audience tends to buy local food due to individual-specific barriers. To better understand what motivates consumers to buy local food the Theory of Planned Behavior was used as a way to potentially predict consumers’ behavior toward buying locally grown blueberries. Also, the variables of past experience and self-identity/moral obligation toward buying local food were introduced to the model since both variables may increase the predictability of the Theory of Planned Behavior model in certain food-related behaviors. This study used an online survey completed by 1,122 respondents, from a sample frame of all consumers in Florida. The study used a non-probability sample of an opt-in panel, and weighting procedures were applied to the data to account for coverage errors associated with using a non-probability sample. The interaction of consumers’ past experience and self-identity/moral obligation was found to help explain consumers’ intention to buy locally grown blueberries when added to the Theory of Planned Behavior. The Theory of Planned Behavior was shown to predict consumers’ intention to buy locally grown blueberries; however, their intent was better explained with the inclusion of the variables of past experience and self-identity/moral obligation toward buying local blueberries. Recommendations for future research and application include future research into other variables that may impact buying behavior and inclusion of these variables in future communication efforts focused on consumers buying and engaging with local food.
20 pages., Via online from the University of Illinois website., Authors' review provided an overview of the data sources, computational methods, and applications of text data in the food industry. Applications of text data analysis were illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food systems.
