估价(财务)
业务
垄断竞争
危害
完美信息
经济盈余
消费者信息
微观经济学
不完美的
营销
产品(数学)
信息经济学
信息不对称
结果(博弈论)
消费者行为
产业组织
公共信息
期望效用假设
精算学
钥匙(锁)
电子商务
新产品开发
完整信息
附加价值
作者
Fatong Shi,Tingting Li
标识
DOI:10.1109/tem.2026.3666760
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI