  Understanding user's perception of service variability is essential to
discern their overall perception of any type of (transport) service. We study
the perception of waiting time variability for ride-hailing services. We
carried out a stated preference survey in August 2021, yielding 936 valid
responses. The respondents were faced with static pre-trip information on the
expected waiting time, followed by the actually experienced waiting time for
their selected alternative. We analyse this data by means of an instance-based
learning (IBL) approach to evaluate how individuals respond to service
performance variation and how this impacts their future decisions. Different
novel specifications of memory fading, captured by the IBL approach, are tested
to uncover which describes the user behaviour best. Additionally, existing and
new specification of inertia (habit) are tested. Our model outcomes reveal that
the perception of unexpected waiting time is within the expected range of 2-3
times the value-of-time. Travellers seem to place a higher reward on an early
departure compared to a penalty for a late departure of equal magnitude. A
cancelled service, after having made a booking, results in significant
disutility for the passenger and a strong motivation to shift to a different
provider. Considering memory decay, our results show that the most recent
experience is by far the most relevant for the next decision, with memories
fading quickly in importance. The role of inertia seems to gain importance with
each additional consecutive choice for the same option, but then resetting back
to zero following a shift in behaviour.
