Abstract
During the late 1970s the term “decision support systems” was first coined by P. G. W. Keen, a British Academic then working in the United States of America. In 1978, Keen and Scott Morton published a book entitled, Decision Support Systems: An Organizational Perspective (Keen & Scott Morton, 1978), wherein they defined the subject title as computer systems having an impact on decisions where computer and analytical aids can be of value but where the manager’s judgment is essential. Information systems (IS) researchers and technologists have developed and investigated decision support systems (DSS) for more than 35 years (Power, 2003b). The structure of this article is as follows: The background to DSS will be given. Some DSS definitions, a discussion of DSS evolution, development of the DSS field and frameworks are then presented. Some future trends for DSS are then suggested.
TopBackground
Van Schaik (1988) refers to the early 1970s as the era of the DSS concept because during this period the concept of DSS was introduced. DSS was a new philosophy of how computers could be used to support managerial decision-making. This philosophy embodied unique and exciting ideas for the design and implementation of such systems. There has been confusion and controversy in respect of the interpretation of the decision support system notion and the origin of this notion originated in the following terms:
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Decision emphasises the primary focus on decision-making in a problem situation rather than the subordinate activities of simple information retrieval, processing or reporting.
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Support clarifies the computer’s role in aiding rather than replacing the decision maker.
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System highlights the integrated nature of the overall approach, suggesting the wider context of machine, user and decision environment.
DSS deal with semi-structured and some unstructured problems.
TopDecision Support Systems
With the ever-increasing advances in computer technology, new ways and means of computer-assisted decision-making was born. As a result hereof, over the passage of time, different DSS definitions arose:
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Little (1970) defines DSS as a “model-based set of procedures for processing data and judgments to assist a manager in his decision making (sic).”
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The classical definition of DSS, by Keen and Scott Morton (1978), states that “Decision Support Systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semi-structured problems.”
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Mann and Watson (1984) state that “a decision support system is an interactive system that provides the user with easy access to decision models and data in order to support semi-structured and unstructured decision-making tasks.”
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Bidgoli (1989) defines DSS as “a computer-based information system consisting of hardware/software and the human element designed to assist any decision-maker at any level. However, the emphasis is on semi-structured and unstructured tasks.”
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Sprague and Watson (1996) define a DSS as computer-based systems that help decision makers confront ill-structured problems through direct interaction with data and analysis models.
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Sauter (1997) notes that DSS are computer-based systems that bring together information from a variety of sources, assist in the organisation and analysis of information and facilitate the evaluation of assumptions underlying the use of specific models.
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Turban, Rainer, and Potter (2005) broadly define a DSS as “a computer-based information system that combines models and data in an attempt to solve semi-structured and some unstructured problems with extensive user involvement.”
Key Terms in this Chapter
Communications-Driven DSS: Systems built using communication, collaboration and decision support technologies.
Information Systems (IS): A combination of technology, people and process to capture, transmit, store, retrieve, manipulate and display information.
Analytical Processing: Involves analysis of accumulated data, frequently by end-users in an organisation. Analytical processing activities include data mining, decision support and querying.
Data-Driven DSS: These systems analyse large “pools of data” found in major organisational systems and they support decision-making by allowing users to extract useful information that was previously buried in large quantities of data.
Model-Driven DSS: Model-driven DSS emphasise access to and manipulation of a model.
Data Warehouse: A repository of subject-oriented historical data that is organised to be accessible in a form readily acceptable for analytical processing activities.
Document-Driven DSS: These systems integrate a variety of storage and processing technologies to provide complete document retrieval and analysis.
Knowledge-Driven DSS: These systems contain specialised problem-solving expertise wherein the “expertise” consists of knowledge about a particular domain.