A psychological approach to decision support systems

Rapid advances in information technology have brought decision makers the mixed blessing of an increasingly vast amount of easily available data. Designers of decision support systems (DSS) have focused on incorporating the latest technology with little attention to whether these new systems are compatible with the psychology of decision makers. Our premise is that DSS should be designed to take advantage of the distinctive competencies of decision makers while using technology to compensate for their inherent weaknesses. In this study we apply this approach to a forecasting task. We find that to arrive at a forecast decision makers often search their experience for a situation similar to the one at hand and then make small adjustments to this previous situation. Our theoretical model of the performance of this intuitively appealing strategy shows that it performs reasonably well in highly predictable environments, but performs quite poorly in less predictable environments. Results from an experiment confirm these predictions and show that providing decision makers with a simple linear model in combination with a computerized database of historical cases improves performance significantly. We conclude by discussing how these results can be used to help improve forecasting in applied contexts, such as promotion forecasting in the retail grocery industry.