AI Appreciation for Rejection: How Advice Source Shapes Consumers' Responses to Rejection Versus Endorsement Advice

ABSTRACT Offering product recommendations based on AI or human analysis is common in contemporary marketing practice. Beyond endorsing an option to choose (recommend‐for; RF advice), platforms increasingly provide rejection‐oriented advice that discourages specific alternatives (recommend‐against; RA advice). We propose that consumers respond differently to these two advice types depending on the advice source. Across five studies (including a supplementary study), we show that consumers are more likely to adopt RA advice when it is generated by AI rather than human advisors, whereas acceptance of RF advice does not differ by source. This AI appreciation for rejection is driven by consumers' perceptions of the advice source's filtering ability and is attenuated when the filtering procedures underlying advice generation are made transparent. These findings contribute to the literature on rejection and consumer responses to AI, while also offering managerial implications for how firms can effectively leverage AI and advice framing to facilitate consumer decision making.