Information Technology Reference
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Expressiveness also includes message construction. The frustration that can result from mes-
sages that are too cryptic is well known to all users of computer-based systems. On the other hand,
some evidence suggests that anthropomorphic phrasing is counterproductive because users feel
threatened and intimidated by messages that they feel are condescending and wordy (Buchheit
and Moher, 1990; Shneiderman, 1987, pp. 322-325; Shneiderman, 1992, pp. 312-314). Rather than
proving engaging, dialogs that exhibit anthropomorphic phrasing have been shown to distance the
user from the decision by decreasing the decision maker's feeling of responsibility for the deci-
sion (Quintanar et al., 1982). According to Shneiderman (1992, p. 313), “the anthropomorphic
interface . . . deceives, misleads, and confuses.”
Collectively, it is clear that “. . . the words and phrases used in designing a computer dialog can
make important differences in people's perceptions, emotional reactions, and motivations”
(Shneiderman, 1987, p. 323; Shneiderman, 1992, p. 312). In this way, the expressiveness of a
DSS's dialog can affect decision making. How user calibration is affected by rhetorical strategy,
presentation frame, connectiveness, phrasing, message construction, and other aspects of a dia-
log's expressiveness characteristics remains to be investigated.
Visibility
“Seeing is believing.” Recognition of the importance of visibility characteristics in problem solv-
ing is increasing. Visibility aspects are also receiving increasing attention in the human-computer
interaction literature (Keller and Keller, 1993; Shneiderman, 1992; Veryard, 1986). Through vis-
ibility characteristics, the decision maker “sees” both the specific problem and the DSS better.
Shneiderman recognizes the need for increased visibility capability in DSS when he writes,
“Expert systems . . . tax the user with complexity, lack of visibility of the underlying process, and
confusion about what functions the system can and cannot handle” (Shneiderman, 1992, p. 169).
Visibility requires that the user see the DSS work, and at work, that the user see the logical
operations performed by the DSS and their application to a specific problem (Veryard, 1986).
This requires increasing the observability of the DSS and their behavior. To improve decision
making and user calibration, DSS must not only work; they must be seen working. This means
that the DSS must facilitate the user's understanding of their logical behavior by effectively
depicting their behavior. Indicators that the DSS work can be found in descriptive, outcome indi-
cators such as information about the system's domain of competence, its history of performance,
its performance over a range of tasks within its domain of expertise (Muir, 1987), and personal
tests and benchmarks, among others. Depicting the DSS doing work is the challenge of informa-
tion visualization and other forms of visual computing.
The notion of visibility characteristics also includes the user's mental imagery of the prob-
lem—that is, the visual imagery of the problem as discussed in the theory of symbolic represen-
tation in problem solving. In this sense, there is increasing recognition of the importance of
visualization for creativity and problem solving. Information technologies are playing an increas-
ingly important role in the visual depiction of complex problems (Keller and Keller, 1993). “A
technical reality today and a cognitive imperative tomorrow, . . . the ability to visualize (informa-
tion) is absolutely essential to ensure the integrity of analysis, to provoke insights, and to com-
municate those insights to others” (McCormick et al., 1987, p. 7). The need to support the user's
cognitive abilities and visual imagery makes information visualization central to the development
of graphical user interfaces.
Information visualization, according to Keller and Keller (1993), consists of exploration, analysis,
and presentation. Exploration requires interactive forms of imaging to identify new relationships
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