Essays on Consumer Demand: Marketing and Policy Implications from Primary and Secondary Data

Xueying Ma

doi:10.7273/000006293

This dissertation focuses on analyzing consumer demand for agricultural products and explores its marketing and policy implications by utilizing both primary and secondary data. The first chapter delves into estimating consumers' willingness to pay for attributes such as sugar content, CRISPR technology, and cranberry flavor intensity in two cranberry products, each presented with different health-related information treatments. Respondents expressed a preference for reduced sugar content over regular sugar products, for conventional over CRISPR breeding methods, and for full/intense cranberry flavor over weak/bland flavor. Interestingly, information emphasizing cranberries' health benefits and sugar intake recommendations amplified the reject to reduced sugar content, surpassing the reject to CRISPR. In the second paper, a Basket-Based Choice Experiment was employed to identify sensory and hedonic quality descriptors of fresh blueberries that could potentially increase the likelihood of purchase. The findings revealed that blueberry packages with a "Stay Fresh" descriptor had a lower price elasticity compared to packages without descriptors or those with "Sweety" and "Crunchy" descriptors. This suggests that consumers are less responsive to price fluctuations when blueberry packages include language indicating an extended shelf life. Moreover, the study indicated that strawberries, blackberries, raspberries, and blueberries are more likely to be purchased together rather than considered substitutes. Additionally, the research identified specific demographic and behavioral factors associated with a higher likelihood of choosing blueberries from a selection of commonly consumed fruits. In the third paper, weekly shipment data from nine apple varieties in Washington state were analyzed to assess the predictive capabilities of various time series models. While some traditional time series models, specifically the Seasonal Autoregressive Integrated Moving Average, excelled in predictive accuracy and capturing variability, this study recommends the use of a machine learning model, specifically Facebook Prophet, due to its computational efficiency and strong predictive accuracy. Additionally, the research explored the impact of introducing a new apple variety, Cosmic Crisp®, on the shipments of existing apple varieties using an interrupted time series analysis. The influence of this introduction varied among different apple cultivars, with some experiencing no changes while others experiencing a decline in subsequent shipment levels. Notably, the overall apple shipments in Washington State remained unaffected by the introduction of Cosmic Crisp®. We also found that the shipments of Cosmic Crisp® stimulates the supply of specific apple varieties and the overall apple supply.

Essays on Consumer Demand: Marketing and Policy Implications from Primary and Secondary Data

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