aiqbench analysis
This study refines the UTAUT2 framework for social robots by confirming performance expectancy, social influence, and hedonic motivation as primary drivers of adoption, while highlighting usability, trust, and competence as critical emerging variables. By applying regularization techniques like Lasso and Ridge regression, the research provides a robust statistical basis for predicting user acceptance in home environments. These findings offer actionable insights for developers aiming to optimize robot design and deployment strategies to overcome barriers to entry in the domestic robotics market.
Astro, I'm Home! Investigating Factors that Influence the Acceptance of Home Robots Using Supervised Machine Learning
arXiv:2606.07551v1 Announce Type: cross Abstract: The use of social robots in home environments is on the rise. This exploratory study applies regularization techniques (e.g., Lasso and Ridge regression) to investigate variables and identify new models of technology acceptance in the context of social robots. Within the original UTAUT2 framework, performance expectancy, social influence, and hedonic motivation emerged as the strongest and most consistent predictors of intention to use the technology. In addition, usability, trust, and competence were identified as promising variables in a model predicting intention to use.
Title:Astro, I'm Home! Investigating Factors that Influence the Acceptance of Home Robots Using Supervised Machine Learning
Abstract:The use of social robots in home environments is on the rise. This exploratory study applies regularization techniques (e.g., Lasso and Ridge regression) to investigate variables and identify new models of technology acceptance in the context of social robots. Within the original UTAUT2 framework, performance expectancy, social influence, and hedonic motivation emerged as the strongest and most consistent predictors of intention to use the technology. In addition, usability, trust, and competence were identified as promising variables in a model predicting intention to use.
Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Robotics (cs.RO)
arXiv:2606.07551 [cs.CY]
(or arXiv:2606.07551v1 [cs.CY] for this version)
https://doi.org/10.48550/arXiv.2606.07551 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 197 words
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