Start Date
11-12-2016 12:00 AM
Description
Free product sampling has increasingly become a popular promotional strategy, and served as a new mechanism of product review generation in e-commerce. We empirically analyze how a product’s engagement in free product sampling affects the product’s review rating, and also examine important contingent factors of product pricing and product popularity. Using a rich data set from Taobao.com and multiple identification strategies and estimation methods, we find that engaging in free product sampling increases product rating by 1.1%. We argue that it is consumers’ reciprocal behavior of giving higher ratings as a return to retailers’ beneficial actions that causes rating bias. We further find that the bias would be larger with higher original price, but smaller with higher price discount and higher product popularity. Our empirical findings provide important contributions to the literature on product sampling and word-of-mouth, and offer critical managerial implications to online retailers, rating system designers, and consumers.
Recommended Citation
Lin, Zhijie; Zhang, Ying; and Tan, Yong, "An Investigation of Free Product Sampling and Rating Bias in E-Commerce" (2016). ICIS 2016 Proceedings. 5.
https://aisel.aisnet.org/icis2016/EBusiness/Presentations/5
An Investigation of Free Product Sampling and Rating Bias in E-Commerce
Free product sampling has increasingly become a popular promotional strategy, and served as a new mechanism of product review generation in e-commerce. We empirically analyze how a product’s engagement in free product sampling affects the product’s review rating, and also examine important contingent factors of product pricing and product popularity. Using a rich data set from Taobao.com and multiple identification strategies and estimation methods, we find that engaging in free product sampling increases product rating by 1.1%. We argue that it is consumers’ reciprocal behavior of giving higher ratings as a return to retailers’ beneficial actions that causes rating bias. We further find that the bias would be larger with higher original price, but smaller with higher price discount and higher product popularity. Our empirical findings provide important contributions to the literature on product sampling and word-of-mouth, and offer critical managerial implications to online retailers, rating system designers, and consumers.