The use of algorithm assistance for decision making is vastly increasing in recent years, largely in online settings wherein users receive advice from recommendation systems. Despite the prevalence of such augmented decision making, users sometimes demonstrate aversion toward algorithms and even reject the recommendations of highly accurate systems. Research in behavioral decision making on the one hand, and in information systems on the other, has yet to fully comprehend these phenomena. We build on insight from these two research streams, as well as from self-determination theory, in identifying the psychological need for autonomy as a driver of decreasing algorithm aversion and increasing recommendation acceptance. We identify two concrete autonomy mechanisms—choice autonomy and control autonomy—which may increase the willingness of users to accept algorithmic recommendations. In three online experiments, simulating the process of selecting a vacation package, we consistently find that showing users multiple recommendations instead of a single recommendation, thereby increasing their choice autonomy, significantly increases recommendation acceptance, both when the system is perfectly accurate (Study 1) and when it is not (Studies 2 and 3). Furthermore, we find in Study 3 that allowing users control over whether they receive single or multiple recommendations, thereby increasing their control autonomy, has a positive moderating effect on the relationship between choice autonomy and recommendation acceptance, suggesting a complementarity between the two autonomy mechanisms. In so doing, we provide theoretical grounding and empirical evidence for the positive consequences of increasing user autonomy on the effectiveness of recommendation systems.

中文翻译：

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让我来决定：增加用户自主权可以提高推荐接受度

近年来，算法辅助决策的使用大幅增加，主要是在用户从推荐系统接收建议的在线环境中。尽管这种增强决策很普遍，但用户有时表现出对算法的厌恶，甚至拒绝高度准确的系统的建议。一方面对行为决策的研究，另一方面对信息系统的研究尚未完全理解这些现象。我们基于这两个研究流以及自我决定理论的见解，确定了对自主的心理需求是减少算法厌恶和提高推荐接受度的驱动因素。我们确定了两种具体的自治机制——选择自治和控制自治——这可能会增加用户接受算法推荐的意愿。在三个在线实验中，模拟选择度假套餐的过程，我们一致发现，向用户显示多个推荐而不是单个推荐，从而增加他们的选择自主权，显着提高推荐接受度，无论是在系统完全准确的情况下（研究 1）以及当不是时（研究 2 和 3）。此外，我们在研究3中发现，允许用户控制自己是否接受单个或多个推荐，从而增加他们的控制自主权，对选择自主权和推荐接受之间的关系具有正向调节作用，表明两种自主机制之间存在互补性。在此过程中，我们为增加用户自主权对推荐系统有效性的积极影响提供了理论基础和经验证据。