How time fuels AI device adoption: A contextual model enriched by machine learning
研究了时间视角（未来导向与现在导向）如何通过推理过程间接影响AI智能音箱的采纳意愿，发现教育水平调节了未来时间视角与采纳意愿的关系。
Most AI device adoption research prioritize immediate factors such as user needs and device functionality, while the complex and dynamic nature of time and individual differences in temporal perspectives are less frequently examined. This study addresses the impact of time in terms of individual differences on AI adoption behaviors, specifically highlighting how different time perspectives influence individuals' decision-making regarding AI device adoption. Machine learning techniques and structural equation modeling were employed to analyze how decision-making varies across temporal dimensions among adopters of AI smart speakers. The results show that individuals, regardless of being future- or present-oriented, show a preference for reasons supporting adoption over reasons against it, indicating a predominant cost-benefit consideration. No direct effects of time perspectives on adoption intentions were noted; rather, the influence of time perspectives is mediated through reasoning processes. Among examined sociodemographic factors, prior experience influences attitude and intentions positively, whereas education level significantly moderates the relationship between a future time perspective and the intention to adopt AI. This paper enriches the AI adoption literature by uniquely combining Behavioral Reasoning Theory with Time Perspective Theory, offering novel insights into the mediation role of reasoning processes in the relationship between time perspectives and adoption intentions. • AI smart speaker adoption is influenced indirectly via reasoning processes tied to different time perspectives. • Future- and present-oriented users prioritize facilitators over inhibitors, reflecting contradictory reasoning. • Education moderates the link between future time perspective and AI smart speaker adoption intentions. • Reasons for and against influence attitudes and intentions toward adopting AI smart speakers. • Machine learning complements SEM in exploring the interplay between reasons for and against AI adoption.