Using Artificial Neural Networks to Prove Hypothetic Cause-And-Effect Relations: A Metamodel-Based Approach to Support Strategic Decisions

Decision models which are based on recent management approaches often integrate cause-and-effect relations in order to identify critical operational measures for a strategic goal. Designers of Decision or Executive Support Systems implementing such a model face the problem that many of the supporting indicators are of non-financial nature (e.g.: customer satisfaction, efficiency of certain business processes, etc.) and cannot be easily quantified as a consequence. Since fuzzy-logic-applications provide numerous specific approaches in this area, our interest focuses on another issue which arises in this context: Due to this lack of numeric assessability of many lag indicators, the interdependencies between those figures cannot be formally described like between financial ratios. In this work, we propose an approach to overcome some shortcomings of many DSS/ESS which force their users to make unproven assumptions about existing interrelations: Because the accuracy of these hypotheses is one of the key quality issues of a decision model we provide a framework to evaluate and prove hypothetic cause-and-effect relations by the use of Artificial Neural Networks.