Joint monitoring of post-sales online review processes based on a distribution-free EWMA scheme

Abstract

With the booming of online shopping, the post-sales online review process becomes a crucial activity in product life cycle management, as it provides abundant user-generated content (UGC) for e-commerce businesses to monitor customer satisfaction, identify potential recalls, and improve electronic Word of Mouth (eWOM). As the core of UGCs, online reviews are generally divided into negative reviews and non-negative reviews in terms of sentiment polarity. Quality issues, such as failures of products or services, are more likely to be hidden in negative reviews. Hence this paper focuses on detecting the abnormal changes of the time-between-review T and sentiment scores S of negative reviews. Due to the complexity and variability of online review processes, the distribution assumptions for S and T may be invalid in real cases. To overcome this problem, we design a distribution-free two-sided monitoring scheme by using the max-type combining function to combine the exponentially weighted moving average (EWMA)-based Wilcoxon rank-sum (WRS) statistics for S and T. The IC and OC performances of the proposed scheme are investigated via simulation study. The results indicate that the proposed scheme outperforms other schemes including a distribution-free EWMA scheme and three parametric Shewhart schemes. Finally, a real example from Ctrip is provided for illustrating the application of the proposed scheme in post-sales service monitoring.