Too Many or Too Few? Information Cues in Recommender Systems and Consequences for Search and Purchase Behavior
通过在线零售商的随机对照实验，研究了推荐系统中信息线索数量（无、单一、双重）与销售额之间的倒U型关系，发现单一线索（价格或评价）效果最佳，为电商平台优化信息展示提供参考。
This article examines how the number of information cues in recommender systems influences consumer search and purchase. E-commerce platforms often display a list of recommended products on product pages, where consumers can browse and click on individual items for details. Given space constraints, determining the appropriate amount of information to display is crucial, as it affects consumers’ use of both recommender systems and nonrecommender search tools. Through a randomized controlled field experiment with an online retailer, the authors test four information designs: no cues (product name only), single cues (either price or review), and dual cues (price and review). They find an inverted U-shaped relationship between the number of information cues and sales, with single cues yielding the highest sales compared with both more information (dual cues) and less information (no cues). This nonlinear effect stems from the interplay between search intensity and efficiency. The no-cue condition increases search intensity but forces consumers to rely on a less efficient nonrecommender search process. In contrast, the highly efficient dual-cue condition provides sufficient information for evaluation but discourages further exploration beyond recommenders. Single cues strike a balance, offering just enough information to aid product evaluation while maintaining high search intensity across both recommender and nonrecommender tools.