Algorithm Aversion in Joint Decision‐Making: The Role of Preference for Neutrality
研究了消费者在联合决策（与他人共同决策）中比单独决策时更少厌恶算法推荐，原因是联合决策时人们更偏好中立，且当决策伙伴目标不对称时更倾向使用算法。
ABSTRACT Given the widespread integration of algorithms into consumer decision‐making, a growing body of research has examined how individuals respond to algorithmic versus human recommenders. However, most existing work has focused on individual decision‐making, leaving open the question of how recommender preferences unfold in joint decision‐making contexts. This study addresses that gap by investigating how consumers choose between algorithmic and human recommenders when making decisions individually versus jointly. Across five studies, we find that consumers exhibit lower algorithm aversion in joint (vs. individual) decision‐making contexts. This effect is mediated by an increased preference for neutrality in joint decision‐making contexts and is moderated by the presence of symmetries between decision partners, with algorithm use more likely when partners are asymmetrical in their goals. These findings contribute to the literature on algorithm aversion by extending it into social decision contexts and offer practical insights for the design and deployment of recommender systems in collaborative settings.