ADVANCING THE THEORY OF DSS DESIGN FOR USER CALIBRATION - Human-Computer Interaction and Management Information Systems: Foundations - page 52

in the information. Analysis requires precise and rigorous techniques for comparing images to
study known relationships in information. Presentation requires images that effectively convey the
meaning of information. In this way, exploration involves “seeing” the problem, analysis involves
seeing the DSS at work, and presentation involves seeing the work of the DSS.
For many problems, user calibration depends upon the user's ability to visualize the problem
and to see the DSS at work and working through an effective presentation. The more visible the
problem and observable and understandable the aid's behavior, the better is the user able to assess
the quality of his or her decision, resulting in better calibration. The emerging technology of
information visualization promises to provide the DSS tools to enable users to explore, analyze,
and present information in order to improve user calibration.
Inquirability
In his well-known topic on the subject, Churchman (1971, p. 275) concluded that the design of an
inquiring system must consider both “knowing” and the “feeling of knowing.” Knowing is the
essence of decision quality and the feeling of knowing is decision confidence. By recognizing the
importance of both decision quality and decision confidence, Churchman implies that the design
of an inquiring system should consider user calibration. The term inquirability is used here to
indicate how the actions of a DSS dialog engender an accurate feeling of knowing.
Inquirability characteristics define how well the dialog actions support the act of discovery,
revealing and resolving. Inquirability characteristics comprise a continuum of dialog actions,
ranging from the servile and illusory that lull to the contrarian that engage and continuously chal-
lenge. Dialog actions that are servile and illusory are designed to please, to unquestioningly
respond by providing data that supports the decision maker's position(s) and assumption(s).
Little, if any, new knowledge is revealed or resolved by a DSS dialog that is servile and illusory.
At the other extreme, DSS dialog actions designed to be contrarian can engage and challenge the
decision maker's positions and assumptions to reveal and resolve new knowledge through the
dialectic process of debate and resolution.
Near the servile end of the inquirability continuum, the actions of a DSS dialog can be designed
to generate data that justifies or supports a position, a set of assumptions, or a decision that the user
has already made. According to survey data reported by Alter (1977), a major reason for using a
DSS is to justify a decision that has already been made. Unfortunately, this adds nothing to holis-
tic performance. The limited evidence suggests that decision makers who surrounded themselves
with servile systems, “yes men” that voice no criticism, fail to make good decisions in crisis situa-
tions (Dunbar and Goldberg, 1978). Servile inquirability fails to inform because it simply presents
data that accords with the decision maker's position. As might be expected, data that accords with
one's decision does not affect decision confidence (Koriat et al., 1980) or improve decision-maker
calibration. This suggests that inquirability designed or used to justify or support a position, set of
assumptions, or a decision that the decision maker has already made has no effect on user calibra-
tion, cannot improve decision quality, and fails to produce holistic, synergistic outcomes.
Between servile and contrarian, designs of inquirability include highlighting, prompting, and
other actions that direct, give advice, and suggest choices. Using these dialog actions, the DSS rec-
ommends a response to the user, providing varying degrees of explanation to support its recom-
mendation. In the extreme, but not uncommon case, the DSS is incapable of providing any
evidence to support its recommendation. Even in some of the most sophisticated systems, the DSS
seem incapable of providing the user with anything more than a very superficial explanation for
their recommendation. In general, dialog designs that simply recommend a response with little or