Effect of descriptive information and experience on automation reliance.

OBJECTIVE

The present research addresses the issue of reliance on decision support systems for the long-term (DSSLT), which help users develop decision-making strategies and long-term planning. It is argued that providing information about a system's future performance in an experiential manner, as compared with a descriptive manner, encourages users to increase their reliance level.

BACKGROUND

Establishing appropriate reliance on DSSLT is contingent on the system developer's ability to provide users with information about the system's future performance.

METHOD

A sequence of three studies contrasts the effect on automation reliance of providing descriptive information versus experience for DSSLT with two different positive expected values of recommendations.

RESULTS

Study I demonstrated that when automation reliance was determined solely on the basis of description, it was relatively low, but it increased significantly when a decision was made after experience with 50 training simulations. Participants were able to learn to increase their automation reliance levels when they encountered the same type of recommendation again. Study 2 showed that the absence of preliminary descriptive information did not affect the automation reliance levels obtained after experience. Study 3 demonstrated that participants were able to generalize their learning about increasing reliance levels to new recommendations.

CONCLUSION

Using experience rather than description to give users information about future performance in DSSLT can help increase automation reliance levels.

APPLICATIONS

Implications for designing DSSLT and decision support systems in general are discussed.