Abstract: Online reviews have an important influence on consumer purchasing decisions. However, the number of online reviews provided by consumers is far from sufficient, and most of the reviews are short, lack of sufficient information, or of poor quality. Many e-commerce platforms have begun to increase review volume through automatically filling in reviews (i.e., default review) if the customer did not write a review, but the impact of default reviews on the generation of consumer reviews has not yet been explored. In this context, the following research questions are raised: What is the impact of default reviews on the volume and length of subsequent consumer reviews? What is its underlying mechanism? These questions are explored by combining second-hand data analysis and experimental method. In study 1, consumer reviews and sales data are crawled from Alibaba to form a panel dataset. The dataset containes 462 products and 120,864 reviews, ranging from September 6th, 2017 to December 6th, 2017. A two-way fixed effects model is constructed to analyze the impacts of default reviews on consumer review volume and review length. The results show that with the increasing of default reviews, subsequent consumer review volume and review length decrease. In Study 2, experimental method is adopted and 165 participants are recruited to examine the psychological process of how default reviews impacted consumer review intentions. The results show that the underlying mechanism is conformity. Specifically, the increase in the volume of default reviews can enhance consumers’ conformity, therefore consumers are less likely to write reviews. This research sheds light on the literature of the generation of consumer reviews by identifying the social influence of silent consumers. Moreover, this research contributes to research on online communities by expanding the scope of subjects that interact with each other in content generation behavior. In addition, the current research provides managerial implications for both online shopping platforms and merchants in designing review systems and stimulating online reviews.

Key words: default reviews; conformity; review volume; review length