Abstract
DSS evaluation may be either ex-post or ex-ante. In the former, evaluation focuses on determining the actual result of DSS implementation. In the latter, emphasis is on predicting the likely impact of DSS alternatives on a given task set or on estimating relationships between DSS characteristics and task set(s) performance. If a firm can, ex-ante, effectively estimate or predict the performance of varying DSS on arrays of tasks or task sets, then it can avoid costly DSS selection errors and gain competitive and strategic advantages. We outline a methodoloy for developing such information using the induced-value methodology of experimental economics. An example experiment is detailed and initial results are presented relating to one general DSS hypothesis and one implication derived from a specific theory of DSS portfolio selection.
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