Navigating uncertainty: Exploring consumer acceptance of artificial intelligence under self-threats and high-stakes decisions

In an era of transformation fueled by Artificial Intelligence (AI), human resistance to adopt this powerful technology has emerged as one of its most critical barriers. In a series of four studies involving almost 4,000 consumers, this research explores factors that contribute to consumer reluctance toward AI through theories related to algorithm aversion, decision-making under risk, and compensatory decision-making. The results underscore the impact of decision stakes and their adverse outcomes on AI service agent adoption across decision domains. These effects can be attributed to the self-threat experienced by consumers in high-stakes decision scenarios. Together, the current findings advance our understanding of consumer responses in the context of AI adoption, illustrating how perceived stakes and self-threats foster reluctance to rely on AI agents for advice. From a practical standpoint, the results emphasize the need of a hybrid approach—combining AI and human agents—for a successful transition toward AI-powered service industries. • Consumers show persistent bias against advice from AI agents. • High decision stakes lead consumers to prefer human advice over AI advice. • This effect is more (less) pronounced in medical (legal, retail) decisions. • Perceived self-threat influences AI advice acceptance, particularly in high-stakes decisions. • AI combined with human agents shows mixed but more positive results.