Zusammenfassung
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 ...
Zusammenfassung
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.
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