Shopping with Voice Assistants: How Empathy Affects Individual and Family Decision-Making Outcomes

Artificial intelligence (AI)-enabled voice assistants (VAs) such as Amazon Alexa increasingly assist shopping decisions and exhibit empathic behavior. The advancement of empathic AI raises concerns about machines nudging consumers into purchasing undesired or unnecessary products. Yet, it is unclear how the machine’s empathic behavior affects consumer responses and decision-making outcomes during voice-enabled shopping. This article draws from the service robot acceptance model (sRAM) and social response theory (SRT) and presents an individual-session experiment where families (vs. individuals) complete actual shopping tasks using an ad-hoc Alexa app featuring high (vs. standard) empathic capabilities. We apply the experimental conditions as moderators to the structural model, bridging selected functional, social-emotional, and relational variables. Our framework collocates affective empathy, explicates the bases of consumers’ beliefs, and predicts behavioral outcomes. Findings demonstrate (i) an increase in consumers’ perceptions, beliefs, and adoption intentions with empathic Alexa, (ii) a positive response to empathic Alexa holding constant in family settings, and (iii) an interaction effect only on the functional model dimensions whereby families show greater responses to empathic Alexa while individuals to standard Alexa