BuyMate: Making AI Interventions Effective in Promoting Rational Consumption in Live Commerce
Shiyi Wang, Yishan Liu, Zhihang Zhu, Jintao Liu, Xuerui Ma, Xin Guan, Tianyang Feng, Qingfei Zhao, XinZhi Zhang, Yuan Yao, Haipeng Mi
Abstract
Live commerce platforms frequently employ algorithmic recommendations and time-limited promotions to trigger impulsive purchases, challenging rational consumer decision-making. While existing research has identified manipulative design patterns in live commerce, significant gaps remain in understanding consumer psychological motivations and developing counter-persuasion interventions. We conducted a multi-stage formative study involving surveys (N = 116), interviews (N = 21), and co-design workshops (N = 16) to explore user preferences for rational consumption support systems. Informed by these insights, we designed BuyMate, which provides gentle, real-time rational interventions through product comparison and persuasive speech reframing. A user evaluation (N = 35) demonstrates that the system effectively reduces impulsive purchases, enhances decision autonomy, and promotes sustainable consumption. This work contributes an AI-driven counter-persuasion approach, identifies user-centered principles for adaptive interventions, and offers practical guidance for responsible AI in digital commerce.
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