From User Experience To Trust: A Data-Driven Model Of Satisfaction and Trust Formation In Agentic Artificial Intelligence

Engineering notes

Key topics: autonomous driving, planning. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Agentic artificial intelligence (AI) systems represent a new generation of autonomous technologies capable of independent decision-making, multi-step planning, and self-directed action. Manus AI exemplifies this paradigm by autonomously executing complex tasks through goal decomposition and workflow automation. Understanding user satisfaction and trust in such systems is critical for sustainable human–AI interaction. This study develops a data-driven model linking system quality, information quality, interaction quality, enjoyment, transparency, and perceived intelligence to user satisfaction and trust. After data cleaning, 55,667 English-language reviews were analyzed using LDA to identify experiential themes, RoBERTa for polarity and subjectivity features, expert construct mapping, and machine learning regression techniques. Enjoyment has the strongest effect on satisfaction, followed by transparency and system quality. Satisfaction strongly predicts trust, with affective satisfaction exerting a greater influence than evaluative satisfaction. These findings support a dual-path model where cognitive and affective factors jointly shape trust. The study extends the information systems success framework to agentic AI.

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