In large IS development projects a huge number of natural language documents becomes available and needs to be analyzed and transformed into structured requirements. This elicitation process is known to be time-consuming and error-prone when performed manually by a requirements engineer. Thus, there is a clear demand for advanced support of the entire elicitation process. Our work focuses on providing automated and knowledge-based support of the task elicitation sub-process. Following a design science approach, design principles for task elicitation systems are conceptualized and instantiated in an artifact. We evaluate our design principles in a laboratory experiment and examine its external validity in a field setting. We contribute to the body of knowledge by explaining effects of the conceptualized and instantiated design principles. Specifically, our results show that the level of automation as well as the extent and origin of the knowledge used for the automation process affect task elicitation productivity.
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