AI 中文总结
本研究通过实验发现，针对新手用户的笔记本电脑产品推荐添加解释可提升感知帮助度与学习度，而专家不受补充信息影响，据此提出了四条文本产品顾问的设计准则。
AI 中文摘要
对话式电商利用数字助手为电商中的搜索过程和决策提供支持。这类交互中的有效沟通可通过助手调整沟通风格以适配用户并支持共同理解来实现。在该场景中，一项开放性挑战是针对不同领域知识水平的用户调整复杂产品信息的呈现方式。为探究此类知识水平适配的策略，我们搭建了一个由聊天机器人辅助的笔记本电脑搜索场景。在一项被试间实验（n=251）中，我们考察了新手和专家对产品属性推荐的感知，这些推荐仅以技术信息（T）呈现，或辅以性能类别（TC）、属性解释（TE），或两者兼具（TCE）。对于新手，带有解释的方法（TE、TCE）被感知为更有帮助，且带来更高的感知学习度。新手还在信息数量方面将组合方法（TCE）评为比基线（T）和TC更合适，表明解释对于理解并从性能类别中获益至关重要。关键的是，专家在各条件间未表现出显著差异，这说明对新手有益的补充信息并未损害他们的体验。我们将这些发现提炼为面向技术领域包容性文本产品顾问的四条具体设计准则：默认使用TCE；保持单一的包容性界面；避免独立的类别；支持用户自主权并根据所述用例进行个性化。
英文摘要
Conversational commerce uses digital assistants to support the search process and decision-making in e-commerce. Effective communication in these interactions can be facilitated by assistants adapting their communication style to users and supporting shared understanding. An open challenge in this context is adapting the presentation of complex product information to users with varying levels of domain knowledge. To investigate strategies for such knowledge-level adaptation, we set up a chatbot-assisted laptop search scenario. In a between-subjects experiment (n = 251), we examined novice and expert perceptions of product attribute recommendations presented as technical information only (T), or augmented with performance categories (TC), attribute explanations (TE), or both (TCE). For novices, approaches with explanations (TE, TCE) were perceived as more helpful and led to higher perceived learning than those without. Novices also rated the combined approach (TCE) more appropriate than the baseline (T) and TC in terms of information quantity, indicating that explanations are crucial to understand and benefit from performance categories. Critically, experts showed no significant differences across conditions, suggesting that providing supplementary information beneficial to novices did not detract from their experience. We distill these findings into four concrete design guidelines for inclusive text-based product advisors in technical domains: use TCE by default; keep a single inclusive interface; avoid standalone categories; and support user agency and personalize to the stated use case.
