On the Role of Beliefs and Trust for the Intention to Use Service Robots: An Integrated Trustworthiness Beliefs Model for Robot Acceptance
This academic study investigates the psychological factors influencing the adoption of service robots by proposing and validating an integrated trustworthiness beliefs model for robot acceptance (TB-RAM). Combining elements from the Theory of Planned Behaviour, the Unified Theory of Acceptance and Use of Technology (UTAUT), and trust literature, the researchers conducted an online study with 400 participants to examine how beliefs mediate trust and the intention to use service robots in general as well as specific assistant robots. The findings show that general trust serves as a baseline for specific trust formation, which in turn mediates the relationship between distinctive beliefs (such as competence and effort expectancy) and the intention to use.
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Who issued it
Who it governs
Researchers and human-robot interaction designers should read this to understand the psychological mechanisms and belief structures driving user acceptance of service robots.
What the document contains
The document covers the psychological foundations of robot acceptance, integrating the Technology Acceptance Model (TAM/UTAUT), Theory of Planned Behavior (TPB), and trust literature into a comprehensive framework called the TB-RAM. It evaluates how beliefs like effort expectancy, competence, and social influence cascade through trust to affect the intention to use service robots.
The study does not cover actual long-term physical interactions with robots, as it was conducted via an online survey with vignettes rather than a real-world longitudinal field experiment.
- General trust in service robots serves as a foundational baseline that predicts trust in specific newly introduced robots.
- Trust acts as a significant mediator between user beliefs and the intention to use service robots.
- Replacing broad and overlapping beliefs with distinctive beliefs (like competence and effort expectancy) improves the explanatory value of robot acceptance models.
- Social influence plays a greater role in fostering trust for robots deployed in public spaces compared to private settings.
- Perceived competence is one of the strongest direct predictors of trust in both general categories and specific assistant robots.
1 Introduction 2 Theoretical Background 3 Method 4 Statistical Analysis and Results 5 Discussion
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Accountability
0/5Clear responsibility for AI outcomes
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screening score · 2026-10-24
Fairness
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Human Dignity
0/5Respect for the inherent worth of persons
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screening score · 2026-10-24
Human Oversight
0/5Humans remain in control of consequential decisions
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screening score · 2026-10-24
Privacy
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Enforcement
0/5Consequences and compliance mechanisms with real force
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Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
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screening score · 2026-10-24