考虑默认好评规则的在线评论偏差研究OA

Default positive rating rules automatically record unsubmitted consumer reviews as positive.While this practice significantly increases the overall positive rating rate,the resulting information bias diminishes the reference value of online review systems for building trust between buyers and sellers.To address this issue,this paper develops a latent variable model based on online review systems of Chinese e-commerce platforms.The model effectively utilizes actual transaction data to identify and reveal the relationship between consumers'true satisfaction and biased online reviews.Combining survey data on consumers'review habits,it employs maximum likelihood estimation to infer consumers'true satisfaction and proposes corresponding bias correction methods.The proposed latent variable model quantifies the degree of bias caused by default positive rating rules in current online review systems and reveals the true evaluation tendencies of silent consumers.These findings provide theoretical guidance for optimizing e-commerce platform review systems and supporting consumer purchase decisions.