用机器学习分析家庭机器人接受度，发现体验、社交和乐趣是关键
Astro, I'm Home! Investigating Factors that Influence the Acceptance of Home Robots Using Supervised Machine Learning
- 基于UTAUT2框架，用正则化回归找影响因素
- 性能预期、社交影响、享乐动机最强，可用性等三变量也重要
- 适合研究人机交互与智能产品设计的学者参考
家庭环境中社交机器人的应用日益增长。本探索性研究采用正则化技术（如Lasso和Ridge回归）分析变量，识别社会机器人情境下的技术接受新模型。在原始UTAUT2框架中，性能预期、社会影响和享乐动机是使用意愿最强且最一致的预测因子。此外，可用性、信任和能力被确认为预测使用意愿的有前景变量。
原文摘要 · Abstract (English)
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.
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