Value of parsimonious nutritional information, consumer-oriented foods cluster, and predicting food price
Abstract
This dissertation focuses on three topics that relate to consumer behavior and the food industry. The first chapter investigates consumers' beliefs about the tastiness and healthfulness of 173 food items in a framed field experiment. Using data collected from 129 food shoppers in Grenoble France, demand models are estimated to determine how choices change with the provision of objective health information. We elicit and convey health information using simple nutritional indices meant to lower search and cognitive processing costs. The results indicate that consumers are willing to pay for tastier foods and for healthier foods, particularly if the consumers have objective information on nutrient content. The estimates suggest that the value of the type of nutritional information provided in the experiment is 0.98 euro per day. The second chapter investigates USA, China, and Korea consumers' perceptions about the health, taste, and price of 60 different food items to determine country-specific food clusters before and after the provision of objective health information. Subsequent analysis relates cluster characteristics to purchase intentions. For Hedonic and Taste-oriented cluster products, Koreans' purchase intentions rise if the products are perceived as expensive before the provision of information; however the purchase intention of Americans and Chinese is not affected by beliefs about affordability. These results could help retailers in each country identify appropriate food groupings, from the consumers' perspective, to improve category management, marketing, and pricing. The last chapter explores whether unconventional consumer-oriented variables might be useful in predicting the Bureau of Labor Statistics (BLS) Food and Beverages Consumer Price Index (CPI). We determine the ability of an Internet search-based index related to food prices (the Google trends index) and a survey-based consumer sentiment index to predict changes in food-related BLS prices from January 2004 to July 2015. A vector autoregression (VAR) model has the best predictive performance with the moving window structure and a vector error correction model (VECM) performs best with the expanding window structure. Encompassing tests reveal that our model out-predicts USDA Economic Research Service food-related CPI forecasts.
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- OSU Dissertations [11221]