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1972). This user(s)-task-technology triad exists within the contexts of both an organization and its
external environment.
Data from a data subsystem (e.g., data warehouse/mart), models from a model subsystem (e.g.,
OLAP), and judgment from the decision-maker “subsystem” combine to form the DSS that addresses
a specific task at a specific time within a specific organization impacted by a specific set of external
environmental conditions. These data, model, and decision-maker subsystems combine to form the
DSS through a dialog subsystem. All capabilities of the data, model, and decision-maker subsystems
are joined through a dialog subsystem. “All capabilities of the system must be articulated and imple-
mented through the Dialog” (Sprague and Carlson, 1982, p. 28). If the technology and decision maker
are to combine to produce holistic outcomes, the dialog must be designed to orchestrate an effective
symphony of data, models, and decision maker.
Model and Data Subsystem
The model subsystem consists of a model base and model management. A model base is a col-
lection of computer-based decision models (Liang, 1988). The functions of a model base are sim-
ilar to that of a database except that the objects stored in a model base are models. A model can
be considered as a mathematical abstraction of a specific problem or a class of problems. Model
management insulates the decision maker from the physical details of model base activities so
that the decision maker can concentrate on decision-making logic (Blanning, 1993).
The development of online analytic processing (OLAP) represents the current instantiation of
the model subsystem (Thomsen, 2002, pp. 8-10). A review of the pioneering work on model man-
agement subsystems can be found in Blanning (1993). The notion of data systems includes both
data and knowledge, as first envisioned for DSS by Bonczek, Holsapple, and Whinston (1981,
pp. 70-71). This merging of data and knowledge is occurring in data warehouse technology. The
conceptualization of a data warehouse is evolving into a federated warehouse that includes both
data and knowledge (Kerschberg, 2001). Ma (1997) argues that data management has received
extensive treatment in the DSS literature, citing maturity of database technology and associated
database design and database management systems as the reason.
Decision Maker
User differences have been and continue to be the focus of much discussion and research on inter-
face design and system performance (Bickmore and Cassell, 2001; Curl et al., 1988; Huber, 1983;
Markus et al., 2002). Indeed, user modeling—the encoding of knowledge about the user to improve
human-computer interaction—has become a well-recognized topic in intelligent systems research
(Kass and T., 1988; Moulin et al., 2002). Particularly relevant to the DSS design theory for user
calibration, “[a] system may have several models of the same user who may be expert in one
domain and novice in another” (Moulin et al., 2002, p. 177). Moreover, user acceptance and cal-
ibration of system output requires an explanation customized and adapted to the user during prob-
lem solving (Moulin et al., 2002). We discuss the development of user modeling, especially in the
form of critiquing systems, below.
Dialog Subsystem
From the decision makers' perspective, the dialog is the system (Sprague and Carlson, 1982, p. 29).
Dialog is an observable two-way exchange of symbols and actions (Hartson and Hix, 1989, p. 8).
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