Information Provision Strategy of Retailers Under Consumer Reference-Dependent Behavior
研究了在线零售商在消费者存在参照依赖行为时，如何通过提供降低不确定性的产品信息来减少退货，发现信息精度过高反而可能损害零售商利益，但适当的信息策略可实现零售商与消费者的双赢。
Due to the considerable valuation uncertainty in online shopping, consumers often return products after purchase. To reduce consumer returns, retailers provide detailed product information, referred to as uncertainty-reducing (UR) information, to mitigate consumers' valuation uncertainty. Even so, consumers cannot fully ascertain their true valuations before purchase. Furthermore, they often discover that the actual utility after purchase deviates from their pre-purchase expectations, leading to perceived losses or gains, which is referred to as reference-dependence. This study considers a monopolistic online retailer selling products to consumers with valuation uncertainty. The retailer can provide UR information to consumers who receive an imperfect signal (good or bad) indicating their true valuation types. We examine the retailer's optimal information provision strategy for rational and reference-dependent consumers, respectively. Furthermore, we analyze the interplay between the retailer's provision of UR information and the consumers' reference-dependent behavior. The results show that, for rational consumers, the retailer can always provide information, and higher information accuracy is more advantageous. However, with reference-dependent behavior, even if providing information is optimal, overly accurate information may harm the retailer's interests. Additionally, providing overly accurate information can exacerbate the return behavior of reference dependent consumers. Moreover, we analyze consumer surplus under the retailer's optimal information provision strategy and find that the strategy of providing UR information and offering full market coverage can lead to a win-win outcome for the retailer and the consumers. To ensure analytical rigor, we extend the analysis by considering a positive return hassle cost of consumers and demonstrate that all key insights remain qualitatively robust.