产品(数学)
计算机科学
过程(计算)
认知
产品类型
认知心理学
心理学
数学
几何学
操作系统
神经科学
程序设计语言
作者
Anders Hauge Wien,Alessandro M. Peluso
标识
DOI:10.1016/j.jbusres.2021.08.016
摘要
Previous research suggests that consumers would listen more to product recommendations from other consumers (human recommenders) than from systems based on artificial intelligence (AI recommenders). We hypothesize that this might depend on the type of product being recommended, and propose an underlying process driving this effect. Three experiments show that, for hedonic products (but not for utilitarian products), human recommenders are more effective than AI recommenders in influencing consumer reactions toward the recommended product. This effect occurs because, when compared to AI recommenders, human recommenders elicit stronger mentalizing responses in consumers. This, in turn, helps consumers self-reference the product to their own needs. However, humanizing AI recommenders increases mentalizing and self-referencing responses, thus increasing the effectiveness of this type of recommenders for hedonic products. Together, these findings provide insight into when and why consumers might rely more on product recommendations from humans as compared to AI recommenders.
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