Double learning or double blinding: An investigation of vendor private information acquisition and consumer learning via online reviews

• Main Authors: HU, Nan, DOW, Kevin E., CHONG, Alain Yee Loong, LIU, Ling
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Online reviews; Word-of-mouth; Online product reviews; Double-learning; Analyst forecast; Databases and Information Systems; E-Commerce; Operations and Supply Chain Management
• Online Access: https://ink.library.smu.edu.sg/sis_research/8015; https://ink.library.smu.edu.sg/context/sis_research/article/9018/viewcontent/Double_learning_or_double_blinding_av.pdf
• Institution: Singapore Management University

In this paper, building upon information acquisition theory and using portfolio methods and system equations, we made an empirical investigation into how online vendors and consumers are learning from each other, and how online reviews, prices, and sales interact among each other. First, this study shows that vendors acquire information from both private and public channels to learn the quality of their products to make price adjustment. Second, for the more popular products and newly released products, vendors are more motivated to acquire private information that is more precise than the average precision to adjust their price. Third, we document a full demand-mediation model between rating and price. In other words, there is no direct linkage between price and rating, and the impact of rating on price (the vendor learning) as well as the impact of price on rating (the consumer learning) are all through demand. Our results show that there is no fundamental difference between the pricing decisions with and without the consumer generated contents. The price is still driven by the supply and demand relationship and vendors only adjust their price in response to review change when those reviews impact sales. We proposed either the impact of reviews has been incorporated into sales or reviews are less truth worthy due to potential review manipulation. Given the complicate situation, we call for further study to unveil this double learning process with double blinding results.