THE DARK SIDE OF REVIEWS: THE SWAYING EFFECTS OF ONLINE PRODUCT REVIEWS ON ATTRIBUTE PREFERENCE CONSTRUCTION

• 成果类型: Review
• 署名作者: Ben Liu, Qianqian; Karahanna, Elena
• 署名单位: City University of Hong Kong; University System of Georgia; University of Georgia
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• 发表日期: 2017
• 页码: 427-+
• 关键词: word-of-mouth; Consumer choice; INFORMATION; retrieval; PERSPECTIVE; regression; variance; DYNAMICS; weights; IMPACT

摘要

Based on the wisdom of the crowd effect, consumer-generated online reviews are supposed to help consumers make more accurate product evaluations. However, the large amount of information in the reviews, coupled with conflicting opinions, can make it difficult for consumers to identify and consider those attributes relevant to their decision. Thus, while online product reviews are generally believed to empower consumers, we suggest that they may have swaying effects in that attribute preferences (i.e., the relative importance consumers place on various product attributes in product evaluation) are more heavily influenced by characteristics of the online reviews rather than by the relevance of the attributes to the consumers' decision context. We propose that three characteristics of online reviews affect the assessment of attribute preferences: (1) the amount of information about attribute-level performance, which is often unevenly distributed across attributes, (2) the degree of information conflict about attribute-level performance, and (3) the relationship between the overall numeric rating and the attribute-level performance information in the reviews. We test our hypotheses in two randomized experiments and a free simulation study. Results from the three studies show that the three review characteristics influence attribute preferences and that their effects are strong enough such that attribute preferences are influenced more by these online review characteristics than by the relevance of the attributes to the consumers' decision context. Our work, which illustrates a dark side to online reviews, has implications for both online word-of-mouth and preference construction research.