Behavioral Patterns beyond Posting Negative Reviews Online: An Empirical View

Abstract

Negative reviews on e-commerce platforms are posted to express complaints about unsatisfactory experiences. However, the exact knowledge of how online consumers post negative reviews still remains unknown. To obtain an in-depth understanding of how users post negative reviews on e-commerce platforms, a big-data-driven approach with text mining and sentiment analysis is employed to detect various behavioral patterns. Specifically, using 1,450,000 negative reviews from JD.com, the largest B2C platform in China, the posting patterns from temporal, perceptional and emotional perspectives are comprehensively explored. A massive amount of consumers across four sectors in recent 10 years is split into five levels to reveal group discrepancies at a fine resolution. The circadian rhythms of negative reviewing after making purchases are found, suggesting stable habits in online consumption. Consumers from lower levels express more intensive negative feelings, especially on product pricing and customer service attitudes, while those from upper levels demonstrate a stronger momentum of negative emotion. The value of negative reviews from higher-level consumers is thus unexpectedly highlighted because of less emotionalization and less biased narration, while the longer-lasting characteristic of these consumers’ negative responses also stresses the need for more attention from sellers. Our results shed light on implementing distinguished proactive strategies in different buyer groups to help mitigate the negative impact due to negative reviews.