It's kind of long, but it might be worth reading the whole thing. Specially the Experiment section, it's fun. :)
In a TED video by Shyam Sankar he suggests that:
You can start by designing the human into the process. Instead of
thinking what a computer would do to solve the problem, design the
solution around what the human would do as well. When you do this you
will realize that you'll spend all of your time on the interface
between man and machine. Specifically on designing around friction and
interaction. In fact the friction is more important than the power of
the man or the power of the machine in determining over all
a = Analytic Capability
h = human
c = computer
M(h*c) = Gestalt of (Human & Computer)
1+fi = Friction
He concludes saying that:
Computers don't detect novel patterns and new behavior, but humans do.
Humans using technology, testing hypothesis, searching for insight by
asking machines to do things for them.
And to test an hypothesis you've got to have an expectation first and search for insight, this way, the mind is already ahead of the request made to the machine (or decision support system).
Why is Gestalt of Human and Computer (
M(h*c)) embedded in the Analytic Capability?
Well, According to Gestalt Theorists:
the ability to perceive objects [or find results] on a screen is a
result of prior knowledge and expectations + image on retina.
In your case an answer or report or solution from the decision support system.
Let's make an experiment. See the following image and try to identify things in it. Something. Anything:
When presented with ambiguous stimuli, our knowledge of the world helps us to make sense of it -- same with ambiguous info on computer screen. At this point you have no prescribed expectation of what to find. Hence, the image is likely to be almost senseless.
From my experience in the office, me and some coworkers were clueless to what was represented in the image, then we were given a little bit more time, and nothing.
This would be similar to a decision support system, giving us numbers without units, headers, titles, boundaries, etc.
Now, look at the image again, and search for a Dalmatian. Aha! There it is! Same image, different you (a you with the expectation of finding a Dalmatian).
Are users more inclined to take the suggestion if it aligns with their
Yes, as expectations do bias result perceived quality.
If the suggestion does not align with their expectations, how best to
'persuade' them to comply with it?
Educating your users to align their expectations to the results your decision support system delivers.