Abstract: With the rapid iteration and widespread application of algorithm technology, algorithm recommendation has become an important reference for consumer purchasing decisions. However, existing research has mostly focused on the technical development and ethical governance of algorithm recommendation, and there is a serious lack of research on its emotional impact, especially consumer pleasure. This study examined the impact of algorithmic recommendations on consumer enjoyment and its underlying mechanisms through a fieldsurvey and three experiments. Their results suggest that (1) algorithm recommendations indeed influence consumer pleasure, and personalized-based targeting algorithm recommendations are more likely to influence consumer pleasure, compared with similarity-based inference algorithmic recommendation; (2) perceived fluency and vivid imagery jointly mediate the effect of algorithm recommendations on consumer pleasure; (3) consumers' cognitive resources not only moderate the effect of algorithm recommendations on perceived fluency and vivid imagery, but also influence the overall dominance of the mediation structure. These research conclusions not only expand the research boundaries of algorithmic recommendation theory and enrich the application scenarios of metacognitive theory and mental imagery theory, but also Porovide important managerial implications for practitioners.

Key words: Algorithm Recommendation, Perceptual Fluency, Imagery Vividness, Cognitive Resources, Consumer Pleasure

摘要： 随着算法技术的快速迭代与广泛应用,算法推荐已成为消费者购买决策的关键影响因素。然而,现有研究多聚焦于算法推荐的技术开发与伦理治理等,而对情绪影响尤其是消费者愉悦感的研究相对缺乏。本研究通过一项田野调查和三项实验验证了算法推荐对消费者愉悦感的影响及其内在机制,结果表明:算法推荐会影响消费者愉悦感,且较之基于相似性推断算法推荐,基于个人化定向算法推荐更容易增强消费者愉悦感;感知流畅性与意象生动性共同中介算法推荐对消费者愉悦感的影响;消费者的状态性与特质性认知资源不仅分别调节算法推荐对感知流畅性和意象生动性的影响,而且还调节整个中介效应主导结构。本研究不仅拓展了算法推荐理论的研究边界、丰富了元认知理论和心理意象理论的应用场景,而且对厂商具有重要的管理启示。

关键词: 算法推荐, 感知流畅性, 意象生动性, 认知资源, 消费者愉悦感