对比友好与直接两种对话风格,发现友好型更提升用户满意度和任务成功率。
Mind the Style: Impact of Communication Style on Human-Chatbot Interaction
- 设计友好与直接两种风格的聊天机器人进行对照实验
- 友好型聊天机器人使用户满意度更高,任务成功率达78%
- 适合需要情感支持的任务场景,需结合具体任务评估价值
对话代理日益参与日常数字交互,但其沟通风格对用户体验和任务成效的影响仍不明确。本研究通过被试间设计,让参与者与名为NAVI的两个版本聊天机器人互动,完成基于地图的2D导航任务。一个版本采用友好支持性语气,另一个采用直接任务导向语气。另设无聊天机器人控制组,仅提供逐步导航指令。结果表明,友好型聊天机器人显著提升用户沟通满意度,并带来更高的任务成功率(78%),而控制组整体任务成功率最高。性别未显著调节风格效应,但分性别分析显示潜在趋势需进一步研究。语言适应性有限,仅在部分特征层面出现选择性对齐。研究提示,聊天机器人沟通风格影响用户感知,可优于缺乏支持的设计,但整体价值取决于任务情境,强调对话系统设计需考虑任务敏感性、透明度与充分评估。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。