<p>With the integration of large language models (LLMs) in humanoid robots, expectations are rising for face-to-face dialogue and personalised services. However, as these models are primarily trained on text, their responses may not be well suited for voice interactions, where response brevity and contextual relevance are particularly important. This study investigates how three response styles (i.e. generic, concise contingent, and lengthy contingent) affect user satisfaction. A between-subjects experiment (<i>N</i> = 96) was conducted, with contingent responses generated by an LLM and generic responses selected from a predefined phrase library. Results indicated that compared to generic responses, concise contingent responses produced higher satisfaction through an increased sense of feeling heard. Furthermore, feeling heard enhanced satisfaction through perceived intelligence and perceived connection, indicating a cognitive–affective dual pathway. However, when the contingent responses shifted from concise to lengthy, participants experienced greater annoyance, which in turn reduced satisfaction. This indirect effect of lengthy (vs. concise) contingent responses on satisfaction via annoyance was moderated by user sociability: it was significant among high-sociability participants but not among low-sociability participants. Practically, these findings suggest that service robots should tailor response length to individual traits and achieve a balance between informativeness and brevity to maintain user satisfaction.</p>