Towards task-sensitive assistance in public spaces

Purpose: Performing tasks in public spaces can be demanding due to task complexity. Systems that can keep track of the current task state may help their users to successfully fulfill a task. These systems, however, require major implementation effort. The purpose of this paper is to investigate if and how a mobile information assistant which has only basic task-tracking capabilities can support users by employing a least effort approach. This means, we are interested in whether such a system is able to have an impact on the way a workflow in public space is perceived. Design/methodology/approach: The authors implement and test AIRBOT, a mobile chatbot application that can assist air passengers in successfully boarding a plane. The authors apply a three-tier approach and, first, conduct expert and passenger interviews to understand the workflow and the information needs occurring therein; second, the authors implement a mobile chatbot application providing minimum task-tracking capabilities to support travelers by providing boarding-relevant information in a proactive manner. Finally, the authors evaluate this application by means of an in situ study (n = 101 passengers) at a major European airport. Findings: The authors provide evidence that basic task-tracking capabilities are sufficient to affect the users’ task perception. AIRBOT is able to decrease the perceived workload airport services impose on users. It has a negative impact on satisfaction with non-personalized information offered by the airport, though. Originality/value: The study shows that the number of features is not the most important means to successfully provide assistance in public space workflows. The study can, moreover, serve as a blueprint to design task-based assistants for other contexts.