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Co-oP pp 149-161 | Cite as

Evaluation issues for GDSS

Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 290)

Abstract

This chapter addressed the critical aspect of evaluation in the GDSS development process. At the risk of dating ourselves, we sought to identify organizational opportunities for GDSS use. Based on a classification scheme of organizational problem types and the current technology, a four-cell contingency model was proposed. As the model implies, GDSS should be used with discretion by organizations because the system is presumably useful to help solve only high tasks/low relationship and, to a lesser extent, high task/high relationship problems. As detailed in Chapter 12, GDSS's ability to generate multi-perspective decision alternatives, to digest and model vast amounts of data, and to help group members reach consensus would make it an ideal tool for facilitating group processes to solve these two problem types. However, as the model suggests, GDSS could be counterproductive to help solve both high relationship/low task and low task/low relationship problems. For the former problem type, a less structured, more creative and intuitive form of decision making is called for, and for the latter type an individual decision maker with perhaps the help of an IDSS should be used.

Obviously, our model can be expanded to account for a variety of other organizational factors that influence effective problem solving and thus the wise use of GDSS as a problem-solving tool. In fact, the four cells in our model structure could be broken down to finer structures. More than just problem types should be examined to realistically assess the complex organizational environment that determines whether GDSS can contribute to solving complex management problems (see also Chapter 12).

Keywords

Problem Type Group Task Relationship Problem Contingency Model Oriented Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1987

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