Lasso and Ridge regression on UTAUT2 survey data identifies performance expectancy, social influence, and hedonic motivation as strongest predictors of intention to use home robots, plus usability, trust, and competence as promising additions.
Human Autonomy and Sense of Agency in Human-Robot Interaction: A Systematic Literature Review
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abstract
Human autonomy and sense of agency are increasingly recognised as critical for user well-being, motivation, and the ethical deployment of robots in human-robot interaction (HRI). Given the rapid development of artificial intelligence, robot capabilities and their potential to function as colleagues and companions are growing. This systematic literature review synthesises 22 empirical studies selected from an initial pool of 728 articles published between 2011 and 2024. Articles were retrieved from major scientific databases and identified based on empirical focus and conceptual relevance, namely, how to preserve and promote human autonomy and sense of agency in HRI. Derived through thematic synthesis, five clusters of potentially influential factors are revealed: robot adaptiveness, communication style, anthropomorphism, presence of a robot and individual differences. Measured through psychometric scales or the intentional binding paradigm, perceptions of autonomy and agency varied across industrial, educational, healthcare, care, and hospitality settings. The review underscores the theoretical differences between both concepts, but their yet entangled use in HRI. Despite increasing interest, the current body of empirical evidence remains limited and fragmented, underscoring the necessity for standardised definitions, more robust operationalisations, and further exploratory and qualitative research. By identifying existing gaps and highlighting emerging trends, this review contributes to the development of human-centered, autonomy-supportive robot design strategies that uphold ethical and psychological principles, ultimately supporting well-being in human-robot interaction.
fields
cs.CY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Astro, I'm Home! Investigating Factors that Influence the Acceptance of Home Robots Using Supervised Machine Learning
Lasso and Ridge regression on UTAUT2 survey data identifies performance expectancy, social influence, and hedonic motivation as strongest predictors of intention to use home robots, plus usability, trust, and competence as promising additions.