A Two-Stage Model of Generating Product Advice: Proposing and Testing the Complementarity Principle
研究提出产品建议的两阶段模型，发现第一阶段推荐与第二阶段改进功能之间的互补性可提高决策质量，但会增加感知努力，对电商设计和消费者行为研究有参考价值。
Most extant research into product recommendations focuses on how advice from recommendation agents (RAs), consumers, or experts facilitates an initial (or single-stage) screening of available products and provides relevant product recommendations. The literature has largely overlooked the possibility and effects of the second stage of product advice using a recommendation improvement (RI) functionality, during which users can refine and improve the accuracy of the first-stage product recommendations. Thus, our understanding of how users make product choices is incomplete. To rectify this, we propose a two-stage model of generating product advice, and we use it to test what we propose as the complementarity principle. This principle posits that the first-stage recommendations (personalized or nonpersonalized) influence the impact of different types of second-stage RI functionality, which augment the first stage by facilitating either alternative-based or attribute-based processing. Results show that the complementary synergies between the two stages result in higher perceived decision quality, but at the expense of higher perceived decision effort. We contribute to the literature by helping researchers better understand users’ adoption of the second-stage RI functionality in conjunction with first-stage recommendations. In addition, e-commerce designers are advised to provide different and complementary types of recommendation sources and RI functionalities to facilitate online consumers’ decision making.