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How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations

College of Business and Economics, Australian National University, Canberra, ACT 0200, Australia
ESSEC Business School, 95000 Cergy-Pontoise, France
Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802
Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802
Rotterdam School of Management, Erasmus University, 3000 DR Rotterdam, The Netherlands
ujwal.kayande{at}anu.edu.au
debruyn{at}essec.fr
Glilien{at}psu.edu
arvindr{at}psu.edu
gbruggen{at}rsm.nl

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.

Key Words: decision support systems; DSS design; feedback; learning; mental models; evaluations
History: This paper was received on February 20, 2008.