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 inco rporating the latest technology with little attention to whether these new systems are compatible with the psychology of decision makers. Ou r premise is that DSS should be designed to take advantage of the dist inctive competencies of decision makers while using technology to comp ensate for their inherent weaknesses. In this study we apply this appr oach to a forecasting task. We find that to arrive at a forecast decis ion makers often search their experience for a situation similar to th e one at hand and then make small adjustments to this previous situati on. Our theoretical model of the performance of this intuitively appea ling strategy shows that it performs reasonably well in highly predict able environments, but performs quite poorly in less predictable envir onments. Results from an experiment confirm these predictions and show that providing decision makers with a simple linear model in combinat ion with a computerized database of historical cases improves performa nce significantly. We conclude by discussing how these results can be used to help improve forecasting in applied contexts, such as promotio n forecasting in the retail grocery industry.