Research data set, data processing (code system and variables)

This study aims to identify the factors that can lead to potential communication crises in AI-enabled chatbots, based on real user experiences. The qualitative research method used was interpretive phenomenological research, as user experiences were at stake. In this context, in-depth interviews were conducted with 12 users who experienced chatbots in solving problems with different organisations in Turkey. The data set obtained as a result of the interviews was analysed using content analysis technique. The results highlighted 4 factors while expressing both the quality of artificial intelligence and users' experiences. These are: chatbots cannot understand users, they do not create an interaction environment, they cannot produce solutions, and they are insufficient in terms of speed and time.

本研究旨在基于真实用户体验，识别可能引发人工智能（AI）赋能聊天机器人潜在沟通危机的影响因素。鉴于用户体验为核心研究关切，本研究选用解释现象学研究法作为质性研究路径。在此框架下，研究人员对土耳其境内12名曾在不同机构场景中借助聊天机器人解决问题的用户开展了深度访谈。访谈所获数据集采用内容分析技术进行分析。研究结果共提炼出四类影响因素，同时覆盖人工智能性能表现与用户体验两大维度，具体包括：聊天机器人无法理解用户意图、未能构建有效的交互环境、无法生成解决方案，以及在响应速度与时效性层面存在短板。