  With the growing popularity of intelligent assistants (IAs), evaluating IA
quality becomes an increasingly active field of research. This paper identifies
and quantifies the feedback effect, a novel component in IA-user interactions:
how the capabilities and limitations of the IA influence user behavior over
time. First, we demonstrate that unhelpful responses from the IA cause users to
delay or reduce subsequent interactions in the short term via an observational
study. Next, we expand the time horizon to examine behavior changes and show
that as users discover the limitations of the IA's understanding and functional
capabilities, they learn to adjust the scope and wording of their requests to
increase the likelihood of receiving a helpful response from the IA. Our
findings highlight the impact of the feedback effect at both the micro and meso
levels. We further discuss its macro-level consequences: unsatisfactory
interactions continuously reduce the likelihood and diversity of future user
engagements in a feedback loop.
