Mind the Style: Impact of Communication Style on Human-Chatbot Interaction
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Abstract (arXiv)
Conversational agents increasingly mediate everyday digital interactions, yet the effects of their communication style on user experience and task success remain insufficiently understood. Addressing this gap, we report a between-subject user study in which participants interacted with one of two versions of a chatbot called NAVI, which assisted them in an interactive map-based 2D navigation task. The two chatbot versions were designed to differ primarily in communication style: one used a friendly and supportive tone, while the other used a direct and task-focused tone. We also included a control condition where participants did not interact with a chatbot but received the step-by-step navigation instructions. The friendly chatbot significantly increased users' communication satisfaction and was associated with higher task success than the direct chatbot. However, participants in the control condition achieved the highest task success overall, suggesting that chatbot interaction may introduce overhead in tasks that can be completed effectively using straightforward instructions. We did not find significant evidence that gender moderated the effects of communication style, although exploratory gender-stratified analyses suggested patterns that warrant further investigation. Finally, we found limited evidence of global linguistic accommodation, with only selective feature-level alignment. These findings suggest that chatbot communication style influences users' perceptions of conversational agents and may improve performance relative to less supportive chatbot designs, but the overall value of chatbot interaction depends on the task context. The study highlights the need for task-sensitive, transparent and carefully evaluated communication-style choices in conversational-agent design.
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References · 5
- 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 182
- 2024A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systemscited by 170
- 2022Same Author or Just Same Topic? Towards Content-Independent Style Representationscited by 83
- 2023Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformerscited by 11
- 2025Conversational Alignment with Artificial Intelligence in Contextcited by 1
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