Abstract:
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.
journal_name
Hum Factorsjournal_title
Human factorsauthors
Yuviler-Gavish N,Gopher Ddoi
10.1177/0018720811406725subject
Has Abstractpub_date
2011-06-01 00:00:00pages
230-44issue
3eissn
0018-7208issn
1547-8181journal_volume
53pub_type
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