The Use of Simulation as an Experimental Methodology for DMSS Research

The Use of Simulation as an Experimental Methodology for DMSS Research

Giusseppi Forgionne (University of Maryland, Baltimore County, USA) and Stephen Russell (University of Maryland, Baltimore County, USA)
Copyright: © 2008 |Pages: 11
DOI: 10.4018/978-1-59904-843-7.ch106
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Abstract

Decision-making support-system (DMSS) research has been based largely on case and field studies of real organizations or entities and laboratory experiments involving human participants. Each methodology has provided useful results. Case and field studies can identify and describe relevant variables and relationships, suggest theories, and offer conceptual and practical models for evaluation (Benbasat, Goldstein, & Mead, 1987; Fjermestad & Hiltz, 2000; Orlikowski & Baroudi, 1991). Participant-based laboratory experiments can provide initial theory and model hypothesis tests, identify key evaluation assumptions, and offer suggestions for future research (Amaratunga, Baldry, Sarshar, & Newton, 2002; Das, 1983; Patton, 2002). Case and field studies and participant-based laboratory experiments, however, have limitations for decision- making support evaluation. In each setting, it is difficult to acquire and motivate the participants. Real organizations and entities are reluctant to disrupt operations or provide proprietary information. Laboratory participants often consist of students in universitybased studies, and as such frequently have little actual knowledge of the decision situation or incentive to mimic real behavior. Even decision-knowledgeable participants may, for political, psychological, or other reasons, mask real behavior. In addition, case studies, field trials, and laboratory settings may not be representative of the population. By their nature, case and field studies usually involve few organizations or entities. Given the diversity experienced in practice, the few organizations or entities, even if selected judiciously, are unlikely to capture the disparity. While great care may be exercised in selecting sample participants for laboratory experiments, there will be Type I and Type II errors that result from even well-designed experiments (Alavi & Joachimsthaler, 1992).

Key Terms in this Chapter

Hypothesis: A hypothesis is a testable explanation for an observable phenomenon or scientific theory.

Forecasting: Forecasting means predicting the likelihood of an event or value occurring in the future based on available historical data.

Intelligent Decision Support System: This is a decision support system that incorporates artificial intelligence and/or knowledge system capabilities.

Decision Variable: A decision variable is an entity denoting a quantity or symbolic representation that may assume any of a set of values, relevant to a decision problem or opportunity. A decision variable may be considered independent, dependent, controlled, uncontrolled, or a combination of these characteristics.

Decision Support System: It is an information system that utilizes database or model-base resources to provide assistance to decision makers through analysis and output.

Strategy Making: It is a management concept for the creation of a structured, systematic plan or direction intended to achieve an objective. Strategy making usually consists of three tasks: vision or mission setting, objective determination or definition, and strategic choice.

Model: A model is a construct that represents another usually complex entity’s or process’s characteristics or behavior.

Simulation: Simulation is the imitation of the operation of a real-world process or system over time (Banks, 1998).

Decision-Making Support System: It is an information system whose purpose is to provide partial or full support for the decision-making phases: intelligence, design, choice, and monitoring.

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