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Despite the significant role of time in human–AI interaction research, particularly AI adoption, most studies focus on objective time, lacking a comprehensive synthesis of its role. This systematic literature review (SLR) addresses this gap by adapting a temporal framework to classify time into four categories: conceptions of time, mapping activities to time, actors relating to time, and technological temporality. Using defined eligibility criteria, 40 out of 9,208 studies were selected for analysis. The findings reveal that AI adoption research lacks a comprehensive understanding of time. This SLR identifies four key gaps: (1) neglect of alternative conceptions of time, (2) limited mapping of activities to time, (3) underexplored context-specific effects on actors, and (4) insufficient integration of time in AI functions. Overall, existing research treats time as static, linear, and objective, overlooking its multifaceted nature. This study contributes the first SLR on time in AI adoption and introduces technological temporality, positioning AI as a temporal actor shaping human–AI interaction.
Dang et al. (Wed,) studied this question.