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Investigating the Impact of AI Labeling on Technology Acceptance and Deployer Attitudes: Trustworthy vs Reliable Automotive AI
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Description: This study explores how labeling automotive AI as either TRUSTWORTHY or RELIABLE influences user perceptions and acceptance of automotive AI technologies. Utilizing a one-way between-subjects design, the research examines various dependent variables related to the Technology Acceptance Model (TAM), including perceived ease of use, perceived usefulness, attitude, behavioral intention, and human-like and functionality-based trust. By analyzing responses from participants assigned to different AI labels, the study aims to provide insights into how specific labels affect attitudes toward using, learning to use, technology acceptance, and adopting certain attitudes toward automotive AI technology. The findings will contribute to understanding the role of labeling in shaping user attitudes and trust towards AI systems.
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