  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.
