Exploring the impact of anthropomorphism to individual context in voice assistants: a dual-contextual analysis

Purpose This study investigates how anthropomorphic traits in voice assistants (VAs) shape user perceptions and adoption behaviors. The anthropomorphic voice assistant acceptance (AVAA) Model examines how humanlike and behavioral traits influence perceived humanness, enjoyment, value, trust and satisfaction, which in turn drive VA adoption. A dual-contextual analysis integrates both anthropomorphic and individual user factors to provide a comprehensive understanding of AI-driven VA interactions. Design/methodology/approach A survey of 627 respondents was conducted to test the proposed framework. Partial least squares structural equation modeling (PLS-SEM) validated the relationships among humanlike traits, behavioral traits, perceived enjoyment, perceived value, trust, satisfaction and behavioral intention to use VAs. Findings The results indicate that VA anthropomorphism–comprising humanlike and behavioral traits–significantly influences individual factors, including enjoyment, value, trust and satisfaction, ultimately leading to higher adoption intention. These findings highlight the importance of anthropomorphic design in enhancing user experience and engagement with AI-driven VAs. Practical implications This study provides insights for developers and managers, demonstrating how humanlike and behavioral traits enhance user satisfaction and adoption. Personalized and responsive interactions strengthen engagement, reduce perceived risks and drive continued use. Originality/value This research advances the technology acceptance and AI anthropomorphism literature by distinguishing between humanlike traits and behavioral traits, offering a dual-contextual framework for understanding user interaction with VAs. The AVAA model extends traditional acceptance models by emphasizing the role of perceived humanness in shaping user perceptions and behavioral outcomes.