How Incorporating Feedback Mechanisms in DSS Affects DSS Evaluations
Date
2009
Authors
Kayande, Ujwal
De Bruyn, Arnaud
Lilien, Gary
Rangaswamy, Arvind
van Bruggen, Gerrit
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Institute for Operations Research and the Management Sciences (INFORMS)
Abstract
Model-based decision support systems (DSS) improve performance in many contexts that are data-rich, uncertain, and require repetitive decisions. But such DSS 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 could lead to DSS design improvements and better returns on DSS investments.
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Keywords: Decision support system (dss); Design characteristics; Design improvements; Evaluations; Feedback mechanisms; Improve performance; Learning; Mental model; Alignment; Artificial intelligence; Decision support systems; Feedback; Models; Decision making Decision support systems; DSS design; Evaluations; Feedback; Learning; Mental models
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Source
Information Systems Research
Type
Journal article
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2037-12-31
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