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With the growing popularity of intelligent assistants (IAs), evaluating IAquality becomes an increasingly active field of research. This paper identifiesand quantifies the feedback effect, a novel component in IA-user interactions:how the capabilities and limitations of the IA influence user behavior overtime. First, we demonstrate that unhelpful responses from the IA cause users todelay or reduce subsequent interactions in the short term via an observationalstudy. Next, we expand the time horizon to examine behavior changes and showthat as users discover the limitations of the IA's understanding and functionalcapabilities, they learn to adjust the scope and wording of their requests toincrease the likelihood of receiving a helpful response from the IA. Ourfindings highlight the impact of the feedback effect at both the micro and mesolevels. We further discuss its macro-level consequences: unsatisfactoryinteractions continuously reduce the likelihood and diversity of future userengagements in a feedback loop.

Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out