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How time fuels AI device adoption: A contextual model enriched by machine learning

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  • Dang, Simon
  • Quach, Sara
  • Roberts, Robin E.

Abstract

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.

Suggested Citation

  • Dang, Simon & Quach, Sara & Roberts, Robin E., 2025. "How time fuels AI device adoption: A contextual model enriched by machine learning," Technological Forecasting and Social Change, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:tefoso:v:212:y:2025:i:c:s004016252500006x
    DOI: 10.1016/j.techfore.2025.123975
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