Driven by ongoing improvements in machine learning, chatbots have increasingly grown from experimental interface prototypes to reliable and robust tools for process automation. Building on these advances, companies have identified various application scenarios, where the automated processing of human language can help foster task efficiency. To this end, the use of chatbots may not only decrease costs, but it is also said to boost user satisfaction. People's intention to use and/or reuse said technology, however, is often dependent on less utilitarian factors. Particularly trust and respective task satisfaction count as relevant usage predictors. In this paper, we thus present work that aims to shed some light on these two variable constructs. We report on an experimental study ($n=277$), investigating four different human-chatbot interaction tasks. After each task, participants were asked to complete survey items on perceived trust, perceived task complexity and perceived task satisfaction. Results show that task complexity impacts negatively on both trust and satisfaction. To this end, higher complexity was associated particularly with those conversations that relied on broad, descriptive chatbot answers, while conversations that span over several short steps were perceived less complex, even when the overall conversation was eventually longer.

翻译：在机器学习的持续进步推动下，聊天机器人已从实验性界面原型逐步演变为流程自动化领域可靠且稳健的工具。依托这些进展，企业识别出多种应用场景，其中人类语言的自动化处理有助于提升任务效率。为此，聊天机器人的使用不仅可能降低成本，据称还能提升用户满意度。然而，人们使用或重复使用该技术的意愿往往取决于非功利因素，尤其是信任与任务满意度，两者均被视为重要的使用预测指标。本文旨在阐明这两个变量构念，通过一项实验研究（n=277）探讨四种不同的人机交互任务。参与者在每项任务后需完成关于感知信任、感知任务复杂度及感知任务满意度的问卷调查。结果显示，任务复杂度对信任与满意度均产生负面影响。具体而言，复杂度较高的对话与依赖宽泛描述性回答的聊天内容密切相关，而由多个简短步骤构成的对话即便整体交互时间更长，也被视为复杂度较低。