5 research outputs found
Mechanical behaviour of both sides of an amorphous Fe78B14Si8 alloy ribbon as determined from miniaturized disk-bend tests
How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations
Model-based decision support systems (DSSs) improve performance in many contexts that are datarich,
uncertain, and require repetitive decisions. But such DSSs are often not designed to help users
understand and internalize the underlying factors driving DSS recommendations. Users then feel
uncertain about DSS recommendations, leading them to possibly avoid using the system. We argue
that a DSS must be designed to induce an alignment of a decision maker’s mental model with the
decision model embedded in the DSS. Such an alignment requires effort from the decision maker
and guidance from the DSS. We experimentally evaluate two DSS design characteristics that facilitate
such alignment: (i) feedback on the upside potential for performance improvement and (ii) feedback on
corrective actions to improve decisions. We show that, in tandem, these two types of DSS feedback
induce decision makers to align their mental models with the decision model, a process we call deep
learning, whereas individually these two types of feedback have little effect on deep learning. We
also show that deep learning, in turn, improves user evaluations of the DSS. We discuss how our
findings can potentially lead to DSS design improvements and better returns on DSS investments