A study about return policies in the presence of consumer social learning

Bingsheng Liu, Wenwen Zhu, Yinghua Shen, Yuan Chen, Tao Wang, Fengwen Chen, Maggie Wenjing Liu and Shi‐Hao Zhou

Production and Operations Management, 2022, vol. 31, issue 6, 2571-2587

Abstract: Sellers are conventionally generous with their return policies for valuation‐uncertain products, such as experience products and new products. However, with the development of online review platforms, an increasing number of consumers are engaging in social learning by referring to others' reviews to reduce valuation uncertainty. In this study, we investigate how social learning interacts with sellers' return policies. There are three main conclusions. First, when sellers have a relatively higher expectation of product quality (or simply the product quality is high), social learning makes the sellers offering either no‐refund policies or partial‐refund policies better off in terms of the increased profit. It will cause the no‐refund sellers to choose higher prices and inventory, and the partial‐refund sellers to set lower prices and refund amounts. Second, under social learning, the partial‐refund policy tends to be more beneficial to sellers than both full‐refund and no‐refund policies; although, when the product quality is high, the no‐refund policy tends to bring more benefits to sellers than the full‐refund policy. Hence, sellers may finally switch to the partial‐refund policy. Third, for partial‐refund policies, more often than not, social learning increases social welfare when the product quality is high; specifically, in many cases, it increases not only the profit of the seller but also the welfare of consumers.

Date: 2022
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