When Post Hoc Explanation Knocks: Consumer Responses to Explainable AI Recommendations

IF 6.8 1区 管理学 Q1 BUSINESS
Changdong Chen, Allen Ding Tian, Ruochen Jiang
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引用次数: 1

Abstract

Artificial intelligence (AI) recommendations are becoming increasingly prevalent, but consumers are often reluctant to trust them, in part due to the “black-box” nature of algorithm-facilitated recommendation agents. Despite the acknowledgment of the vital role of interpretability in consumer trust in AI recommendations, it remains unclear how to effectively increase interpretability perceptions and consequently enhance positive consumer responses. The current research addresses this issue by investigating the effects of the presence and type of post hoc explanations in boosting positive consumer responses to AI recommendations in different decision-making domains. Across four studies, the authors demonstrate that the presence of post hoc explanations increases interpretability perceptions, which in turn fosters positive consumer responses (e.g., trust, purchase intention, and click-through) to AI recommendations. Moreover, they show that the facilitating effect of post hoc explanations is stronger in the utilitarian (vs. hedonic) decision-making domain. Further, explanation type modulates the effectiveness of post hoc explanations such that attribute-based explanations are more effective in enhancing trust in the utilitarian decision-making domain, whereas user-based explanations are more effective in the hedonic decision-making domain.
当事后解释来敲门时:消费者对可解释人工智能推荐的反应
人工智能(AI)推荐正变得越来越普遍,但消费者往往不愿信任它们,部分原因是算法辅助推荐代理的“黑箱”性质。尽管人们认识到可解释性在消费者对人工智能推荐的信任中起着至关重要的作用,但如何有效地提高可解释性认知,从而增强消费者的积极反应,目前尚不清楚。目前的研究通过调查事后解释的存在和类型在促进消费者对不同决策领域的人工智能建议的积极反应方面的影响来解决这个问题。在四项研究中,作者证明,事后解释的存在增加了可解释性的感知,这反过来又促进了消费者对人工智能推荐的积极反应(例如信任、购买意愿和点击)。此外,他们还表明,事后解释的促进效应在功利性(相对于享乐性)决策领域更强。此外,解释类型调节了事后解释的有效性,例如基于属性的解释在功利决策领域更有效地增强信任,而基于用户的解释在享乐决策领域更有效。
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来源期刊
CiteScore
20.20
自引率
5.90%
发文量
39
期刊介绍: The Journal of Interactive Marketing aims to explore and discuss issues in the dynamic field of interactive marketing, encompassing both online and offline topics related to analyzing, targeting, and serving individual customers. The journal seeks to publish innovative, high-quality research that presents original results, methodologies, theories, and applications in interactive marketing. Manuscripts should address current or emerging managerial challenges and have the potential to influence both practice and theory in the field. The journal welcomes conceptually rigorous approaches of any type and does not favor or exclude specific methodologies.
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