Peeling back acceptance: An onion model of AI humanoid robot adoption in homes

As AI humanoid robots increasingly enter domestic spaces, understanding the multifaceted drivers of user acceptance becomes crucial. However, existing technology acceptance models often overlook the unique contextual and emotional demands of home settings, the spaces characterized by intimacy, informality, and privacy sensitivity. This study proposes a novel “onion model” of AI humanoid robot acceptance in home settings, structured across three layers: technical, psychological, and social. Drawing on data from 1373 individuals in China, results reveal that technical performance, particularly reliability (β = 0.355) and usability (β = 0.341), serves as the foundational layer of acceptance, while customization (β = 0.230) and cost–benefit evaluation (β = 0.325) significantly enhance user attachment and rational justification. Perceived intrusiveness strongly deters acceptance (β = −0.101), reflecting heightened privacy sensitivities in home environments. For social factor, peer influence was also found to play a significant role (β = 0.207), highlighting the importance of collective norms in shaping individual decisions. The findings contribute both a contextually grounded and theoretically novel framework for understanding AI adoption, offering actionable insights for developers, policymakers, and researchers seeking to advance human–AI integration in everyday life.