This paper explores the key factors inhibiting algorithm aversion from the perspectives of information, emotion, control and social interaction based on the interactive experience framework, and discusses the moderating role of emotion types.
Based on offline retail scenarios, this study designed two sub-studies using a combination of between-group experiments and PLS-SEM; a total of 541 online sample data were used to verify the relevant factors affecting consumers' algorithm aversion from the perspective of the interaction experience framework.
The findings show that: (1) Interactive (compared to advisory) AI-assisted product selection can promote higher-quality interactive experiences for consumers and inhibit consumers' algorithm aversion. (2) Information interaction, emotional interaction, control interaction and social interaction all significantly affect consumers' algorithm aversion and have significant mediating effects. (3) Emotion types play a significant moderating role in the path between AI-assisted product selection modes and algorithm aversion. Among them, positive emotions help consumers project their self-emotions into the interactive process of AI algorithm decision-making, which is conducive to further alleviating algorithm aversion in the advisory AI-assisted product selection mode.
Compared with previous studies, this paper enriches the understanding of algorithm aversion, expands the value of the interaction framework and emotion theory in guiding consumer behavior and can provide practical guidance and insights for brands and AI developers.
