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Paper Type
Complete
Description
Artificial intelligence (AI) has been widely used in many products and services and has become an important means to assist users in decision-making. In the context of human-AI collaboration, both the quality and speed of AI’s decision-making are the two system features that users can readily identify. However, the existing research focuses on decision-making quality and pays little attention to the effect of AI’s decision-making speed. Drawing from the theory of cue utilization, this research explored the effect of AI decision speed on user’s adoption intention. The results of two experiments show that users have higher AI decision adoption intention at high AI decision speed than at low; the perceived intelligence and perceived risk in decision-making play a mediating role in the above effects. Additionally, historical decision quality moderates the impact of AI decision speed on users’ adoption by weakening the above impact in the high-quality condition. The findings enrich the research on AI adoption and have some practical implications for AI service providers and developers.
Paper Number
1864
Recommended Citation
Wang, Guoxin; Lu, Shouwang; and Wang, Kanliang, "Effect of AI Decision Speed on User Adoption in Human-AI Collaboration: The Moderating Role of Historical Decision Quality" (2023). AMCIS 2023 Proceedings. 22.
https://aisel.aisnet.org/amcis2023/sig_odis/sig_odis/22
Effect of AI Decision Speed on User Adoption in Human-AI Collaboration: The Moderating Role of Historical Decision Quality
Artificial intelligence (AI) has been widely used in many products and services and has become an important means to assist users in decision-making. In the context of human-AI collaboration, both the quality and speed of AI’s decision-making are the two system features that users can readily identify. However, the existing research focuses on decision-making quality and pays little attention to the effect of AI’s decision-making speed. Drawing from the theory of cue utilization, this research explored the effect of AI decision speed on user’s adoption intention. The results of two experiments show that users have higher AI decision adoption intention at high AI decision speed than at low; the perceived intelligence and perceived risk in decision-making play a mediating role in the above effects. Additionally, historical decision quality moderates the impact of AI decision speed on users’ adoption by weakening the above impact in the high-quality condition. The findings enrich the research on AI adoption and have some practical implications for AI service providers and developers.
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