The Evaluation of Decision-Making Support Systems' Functionality

The Evaluation of Decision-Making Support Systems' Functionality

Giusseppi Forgionne (University of Maryland, Baltimore County, USA) and Stephen Russell (University of Maryland, Baltimore County, USA)
Copyright: © 2008 |Pages: 10
DOI: 10.4018/978-1-59904-843-7.ch038
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Contemporary decision-making support systems (DMSSs) are large systems that vary in nature, combining functionality from two or more classically defined support systems, often blurring the lines of their definitions. For example, in practical implementations, it is rare to find a decision support system (DSS) without executive information system (EIS) capabilities or an expert system (ES) without a recommender system capability. Decision-making support system has become an umbrella term spanning a broad range of systems and functional support capabilities (Alter, 2004). Various information systems have been proposed to support the decision-making process. Among others, there are DSSs, ESs, and management support systems (MSSs). Studies have been conducted to evaluate the decision effectiveness of each proposed system (Brown, 2005; Jean-Charles & Frédéric, 2003; Kanungo, Sharma, & Jain, 2001; Rajiv & Sarv, 2004). Case studies, field studies, and laboratory experiments have been the evaluation vehicles of choice (Fjermestad & Hiltz, 2001; James, Ramakrishnan, & Kustim, 2002; Kaplan, 2000). While for the most part each study has examined the decision effectiveness of an individual system, it has done so by examining the system as a whole using outcome- or user-related measures to quantify success and effectiveness (Etezadi-Amoli & Farhoomand, 1996; Holsapple & Sena, 2005; Jain, Ramamurthy, & Sundaram, 2006). When a study has included two or more systems, individual system effects typically have not been isolated. For example, Nemati, Steiger, Lyer, and Herschel (2002) presented an integrated system with both DSS and AI (artificial intelligence) functionality, but they did not explicitly test for the independent effects of the DSS and AI capabilities on the decision-making outcome and process. This article extends the previous work by examining the separate impacts of different DMSSs on decision effectiveness.

Key Terms in this Chapter

Executive Information System (EIS): An EIS is an information system that accesses, reports, and helps users interpret problem-pertinent information.

Simulation: A simulation is an approach to data creation and analysis that utilizes a model to represent reality.

Management Support System (MSS): An MSS is an information system that integrates the functional capabilities of a decision support system, executive information system, and knowledge-based or expert system.

Decision Support System (DSS): A DSS is an information system that interactively supports the user’s ability to evaluate decision alternatives and develop a recommended decision.

Knowledge-Based or Expert System (KBS or ES): This is an information system that captures and delivers problem-pertinent knowledge and advice for users.

Management Game: It is a model used by a participant to experiment with decision strategy plans in a simulated organization.

Decision Making Process: This is the process of developing a general problem understanding, formulating the problem explicitly, evaluating alternatives systematically, and implementing the choice.

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