AMTP Proceedings 2026

Document Type

Conference Proceeding

Publication Date

Spring 2026

Abstract

Artificial intelligence (AI) has revolutionized personalized advertising by enabling precise targeting based on consumer profiles, yet mistargeted ads—those falsely implying eligibility for benefits like premium credit cards—can trigger frustration, disappointment, and negative brand perceptions. This study examines how consumers respond to such errors when attributed to a human employee, an AI system, or an AI system with human oversight. Grounded in attribution theory (Folkes, 1988; Weiner, 2000), it tests perceived group homogeneity (Longoni et al., 2022) and negative self-perception (Grewal et al., 2019) as mediators, with belief in a just world (BJW; White et al., 2012) as a moderator. The framework predicts stronger backlash for human errors, viewed as controllable and morally careless, compared to AI errors seen as technical glitches. Algorithmic transference may trigger AI cases through overgeneralized distrust, while the jilting effect (Garvey et al., 2017) evokes emotional disappointment and self-doubt from revoked eligibility. BJW is expected to amplify reactions to perceived injustices, particularly for human-attributed errors.

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