Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application
提出基于理论的数据驱动方法，利用亚马逊评分数据衡量产品类型（搜索品vs体验品、水平vs垂直差异化）及购买不确定性来源，发现验证购买者评分能反映客观价值，但现有系统解决匹配不确定性的能力有限。
Search and experience goods, as well as vertical and horizontal differentiation, are fundamental concepts of great importance to business operations and strategy. In our paper, we propose a set of theory-grounded data-driven measures that allow us to measure not only product type (search vs. experience and horizontal vs. vertical differentiation) but also sources of uncertainty and to what extent consumer reviews help resolve uncertainty. We used product rating data from Amazon.com to illustrate the relative importance of fit in driving product utility and the importance of search for determining fit for each product category at Amazon. Our results also show that, whereas ratings based on verified purchasers are informative of objective product values, the current Amazon review system appears to have limited ability to resolve fit uncertainty. Industry practitioners could utilize our approaches to quantitatively measure product positioning to support marketing strategy for retailers and manufacturers, covering an expanded group of products.