Decision Support Systems

In general, a DSS is used to make better decisions or to make a decision with less effort. DSS use increases when the DSS decreases the effort required for implementing an effortful strategy, and when use of the DSS leads to increased decision quality or accuracy. Individual-level decision performance measures include objective outcomes, better understanding of the decision problem, or user perception of the system's usefulness. Previous research on decision support has also used decision performance as a means of comparing systems and comparing other facets of decision support, such as data representations. When a DSS extends the capabilities of users, it enables them to overcome limited resources and assists them in making better decisions. Empirical research indicates that improved decision performance results if a DSS is a good fit for a task and supports the user through reduced effort.

Additionally, a meta-analysis conducted by Fried and Ferris supports the relationship between task motivation and decision performance. Task motivation has been reported to be a strong predictor of performance. The impact of task motivation on performance has been supported in the context of sports and education. Research on the job characteristics model also reports that variables with job motivating features have a positive impact on performance.

Chan's motivational framework provides a stream of research for investigating the impact of various variables on DSS use and decision performance. It is important to recognize the existence of alternative relationships among the constructs in the framework. For example, Chan proposes and tests a model that examines how task motivation interacts with DSS effectiveness and efficiency to affect DSS use. Chan et al. also present a model that examines how feedback and rewards influence decision performance.

The next section discusses a study by Chan that tests some of the constructs in the motivational framework.