DELIBERATIVE OR AUTOMATIC: DISENTANGLING THE DUAL PROCESSES BEHIND THE PERSUASIVE POWER OF ONLINE WORD-OF-MOUTH

• 成果类型: Article
• 署名作者: Lei, Zhanfei; Yin, Dezhi; Zhang, Han
• 署名单位: University of Massachusetts System; University of Massachusetts Amherst; State University System of Florida; University of South Florida; University System of Georgia; Georgia Institute of Technology; Hong Kong Baptist University
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2024/17614
• 发表日期: 2025-03
• 页码: 331-346
• 关键词: Review elaborateness; review exposure; review valence; deliberative process; automatic process; dual-process theories; persuasion; mere exposure; reviews; accessibility; INFORMATION; consumer; attitude; helpfulness; strategies; emotions; BIAS

摘要

As online reviews become increasingly indispensable for consumers, they have attracted significant attention from both practitioners and researchers. It is a common belief that the persuasive effect of online reviews involves a deliberative and conscious process. Drawing on dual-process theories and the persuasion literature, we challenge this conventional wisdom, distinguish Type 2 processing (which requires deliberation) and Type 1 processing (which occurs automatically), and disentangle their relative impacts. With a focus on review elaborateness and review exposure, we propose that the automatic process of review exposure may play a greater role than elaborateness in changing consumers' attitudes and purchase intentions. In addition, in line with the negativity bias, we posit that the persuasive impact of review exposure (vs. elaborateness) is moderated by the valence of highly exposed reviews. The results of the two experiments provide consistent support for these predictions. Our findings complement and extend the emerging literature starting to explore the role of automatic Type 1 processing in consumers' use of online reviews, reveal the primary driver of persuasion and its boundary condition in online wordof-mouth, and provide important implications for review platforms, product manufacturers, and retailers.