Advice most likely to result in achieving a goal
may be challenging to follow or appear counterintuitive.
For example, when the quickest journey time
may involve driving a circuitous route,
or when the optimal advice involves large-scale change
that is difficult to maintain.
Such advice is less likely to be adopted
by users, which poses a dilemma for designers of systems that
aim to offer useful advice.
In this paper, we explore this dilemma
between what we have called goal-directed advice
and adoption-directed advice.
Goal-directed advice is that advice
which is most likely to result in an individual user achieving
their goal fully, but which users are
less likely to follow strictly.
Adoption-directed advice is useful advice
that is more likely to be followed by a greater
number of users, but which has a lower
probability that individual users will fully
achieve their goal.
We conducted an online experiment
exploring this dilemma in four scenarios,
asking two research questions.
First, are participants' preferences
sensitive to domain context?
And second, do preferences differ
depending on whether participants
are giving or receiving advice?
In our analysis, we found a clear preference
for showing goal-directed advice, which
was consistent across scenarios and also
in different participant role.
In the paper, we discuss these findings in detail,
using our analysis of participant survey responses
and building on prior theory.
Here we show a few examples of the themes covered.
One of the key themes is the relationship
that individual participants have
to the crowd of other users.
Here we see a contrast between a majority, who
see individual responsibility as the key motivator,
and others who focus on the wider benefits that might
be achieved across populations.
Related to this, we see many participants
adopting an egocentric standpoint towards advice.
These participants often discounted the efforts
of others while potentially overstating
their own capabilities, a finding
the reflects prior research.
We also see that task complexity and task
familiarity played an important role
in participants' preferences.
For example, some participants selecting
adoption-directed advice stated the less challenging, but still
beneficial, advice reflected common sense.
While for familiar tasks, such as route finding,
preferences were often justified with reference
to personal experience.
The final thing we introduce here
is the notion that preferences should be good for business,
reflecting our experimental premise the participant is
making a design decision.
Here the divide between goal-directed advice
and adoption-directed advice was reflected
in attention between attracting an initial user base
and long-term user retention.
In conclusion, we show how the dilemma
we identify echoes wider dilemmas discussed
in previous literature and start to unpack
its implications for the design of algorithmic systems.