Although users’ expectations of a chatbot’s performance could greatly shape their interaction experience, they have been underexplored in the context of social support where chatbots are gaining popularity. A 2 × 2 experiment created expectancy violation and confirmation conditions by matching or mismatching a chatbot’s expertise label (expert vs. non-expert) and its interactional contingency (contingent vs. generic feedback to users). Contingent feedback from chatbots was found to have positive effects on participants’ evaluation of the bot and their perceived emotional validation, regardless of the bot’s expertise label. When providing generic feedback to participants, a bot received worse evaluation and induced less emotional validation on participants when it was labeled as an expert, rather than a non-expert, highlighting the detrimental effect of negative expectancy violation than negative expectancy confirmation in interactions with a social support chatbot. Theoretical and practical implications are discussed.

中文翻译：

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当聊天机器人让您失望时：社交支持环境中人与聊天机器人交互的预期违反

尽管用户对聊天机器人性能的期望可能会极大地影响他们的交互体验，但在聊天机器人越来越受欢迎的社会支持背景下，这些期望尚未得到充分探索。2 × 2 实验通过匹配或不匹配聊天机器人的专业知识标签（专家与非专家）及其交互偶然性（对用户的偶然与一般反馈）来创建预期违规和确认条件。研究发现，无论机器人的专业标签如何，来自聊天机器人的偶然反馈都会对参与者对机器人的评估及其感知的情感验证产生积极影响。当向参与者提供通用反馈时，当机器人被标记为专家而不是非专家时，它会收到更差的评估，并且对参与者产生更少的情感验证，这突显了在与机器人交互时，负预期违反比负预期确认的有害影响。社交支持聊天机器人。讨论了理论和实践意义。