리뷰 양, 리뷰 방향 및 AI 리뷰요약이 리뷰유용성에 미치는 영향

Effects of Review Volume, Review Valence, and AI Summary on Review Helpfulness

With the rapid growth of e-commerce and social media, online reviews have become a critical source of information for consumers in evaluating product and service quality as well as making purchasing decisions. By relying on reviews, consumers aim to reduce decision-making risks and enhance their shopping experiences. Accordingly, examining the influence of online reviews and the role of AI-generated review summarization is of increasing importance. This study investigates the effects of review volume, review valence, and AI review summarization on consumers’ perceived review usefulness. While greater review volume is generally expected to increase usefulness, excessive volume can result in information overload, thereby diminishing perceived usefulness. In terms of review valence, negative reviews are typically perceived as more diagnostic and thus more useful than positive ones. Moreover, with the advancement of artificial intelligence, review summarization has emerged as a practical tool to help consumers extract key information efficiently, especially when review volume is high. To test these assumptions, a 3×2 factorial experiment was conducted, manipulating review volume (short, medium, long) and review valence (positive, negative). For long reviews, an additional condition of AI-generated summarization (present vs. absent) was introduced. Data were collected from 201 participants, and the results revealed that all three factors—review volume, review valence, and AI summarization—significantly influenced review usefulness. Specifically, medium review volume, negative reviews, and the presence of AI summarization were associated with higher perceived usefulness. Based on these findings, theoretical and managerial implications for optimizing review systems on e-commerce platforms are discussed.