PI: Yizao Liu, The Pennsylvania State University

Date Awarded: August 2020 (FSRF 1-1)

Abstract (from final report):

This paper examines the quantitative impact of consumer learning from various channels on growing plant-based milk consumption. Using brand-level sales and price data on both plant-based milk from 2006 to 2018 in the United States, this paper formulates a structural random coefficient discrete choice model with a Bayesian learning process in the plant-based milk market. More specifically, we assume that consumers are uncertain about the health quality of plant-based milk and that information from media, advertising, and manufacturers’ product variety strategy affect their choices through a learning process. Consumers learn about the association between health outcomes and plant-based milk consumption from these different channels, update their perceptions of product qualities, and make their beverage choices. We find that consumers can learn about the true quality of each plant-based milk categories over time. Signals from all three channels all help improve the perceived quality with different relative contributions. Among all channels, health news contributes the most, followed by product variety and advertising.

Further Information: Full report can be downloaded here.