Humanoid social robots are increasingly present in daily life, making sustained user engagement a critical factor for their effectiveness and acceptance. While prior work has often examined affective evaluations or anthropomorphic design, less is known about the relative influence of dynamic conversational qualities and perceived robot characteristics in determining a user's intention to re-engage with Large Language Model (LLM)-driven social robots. In this study, 68 participants interacted in open-ended conversations with the Nadine humanoid social robot, completing pre- and post-interaction surveys to assess changes in robot perception, conversational quality, and intention to re-engage. The results showed that verbal interaction significantly improved the robot's perceived characteristics, with statistically significant increases in pleasantness ($p<.0001$) and approachability ($p<.0001$), and a reduction in creepiness ($p<.001$). However, these affective changes were not strong and unique predictors of users' intention to re-engage in a multiple regression model. Instead, participants' perceptions of the interestingness ($β=0.60$, $p<.001$) and naturalness ($β=0.31$, $p=0.015$) of the robot's conversation emerged as the most significant and robust independent predictors of intention to re-engage. Overall, the results highlight that conversational quality, specifically perceived interestingness and naturalness, is the dominant driver of re-engagement, indicating that LLM-driven robot design should prioritize engaging, natural dialogue over affective impression management or anthropomorphic cues.

翻译：人形社交机器人日益融入日常生活，使得持续的用户参与成为其效能与接受度的关键因素。尽管先前研究多关注情感评估或拟人化设计，但对于动态会话质量与感知机器人特征在决定用户与大型语言模型（LLM）驱动的社交机器人再互动意愿中的相对影响，目前仍知之甚少。本研究邀请68名参与者与Nadine人形社交机器人进行开放式对话，并通过交互前后问卷调查评估其对机器人感知、会话质量及再参与意愿的变化。结果显示，言语交互显著改善了机器人的感知特征，其愉悦度（$p<.0001$）与亲和力（$p<.0001$）呈现统计学显著提升，而诡异感（$p<.001$）则有所降低。然而在多元回归模型中，这些情感变化并未成为用户再参与意愿的强有力独立预测因子。相反，参与者对机器人会话趣味性（$β=0.60$，$p<.001$）与自然度（$β=0.31$，$p=0.015$）的感知，成为预测再参与意愿最显著且稳健的独立因素。总体而言，研究结果凸显了会话质量——特别是感知趣味性与自然度——是驱动再参与意愿的主导因素，这表明LLM驱动的机器人设计应优先构建引人入胜的自然对话，而非侧重于情感印象管理或拟人化线索。